Solid-state NMR at natural isotopic abundance for bioenergy applications

Addison, Bennett; Dickwella Widange, Malitha C.; Pu, Yunqiao; Ragauskas, Arthur J.; Harman-Ware, Anne E.

doi:10.1186/s13068-025-02648-z

Review
Open access
Published: 28 April 2025

Solid-state NMR at natural isotopic abundance for bioenergy applications

Bennett Addison¹^na1,
Malitha C. Dickwella Widange¹^na1,
Yunqiao Pu²,
Arthur J. Ragauskas^2,3,4 &
…
Anne E. Harman-Ware¹

Biotechnology for Biofuels and Bioproducts volume 18, Article number: 46 (2025) Cite this article

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Abstract

Lignocellulosic biomass offers a vast and renewable resource for biofuel production and carbon management solutions. The effective conversion of lignocellulosic biomass into economically competitive biofuels and bioproducts demands a comprehensive understanding of its complex structure and composition, often requiring a range of analytical tools to achieve meaningful insights. However, for the analysis of rigid solids, many traditional methods necessitate dissolution or chemical/physical modification of the sample, which limit our ability to capture an intact view of its structural components. This highlights the need for non-destructive approaches, such as solid-state nuclear magnetic resonance (ssNMR), which preserves the sample’s natural state while providing deep, molecular-level insights. While advanced multi-dimensional ssNMR on ¹³C-enriched materials has recently proven exceptionally valuable for elucidating the complex macrostructure of biomass, isotopic enrichment is expensive, laborious and is clearly infeasible at large scales. In this review, we explore the role of solid-state NMR methods at natural isotopic abundance as essential tools for the non-destructive, in-depth characterization of lignocellulosic biomass and bioenergy materials in their native and unaltered state. After a brief introduction to the basic principles of solid-state NMR, we first describe the acquisition and interpretation of routine 1D ¹³C ssNMR spectra of lignocellulose and other related biopolymers and products. We then delve into more advanced ssNMR approaches, including key spectral editing techniques, probing polymer dynamics, and various 2D methods applicable at natural abundance. Understanding of domain miscibility as observed from proton-based spin diffusion effects is a theme throughout. Our aim is to highlight key examples where ssNMR provides valuable insights into the composition, structure, dynamics, and morphology of rigid biomaterials relevant to the bioenergy economy, revealing both the native structures and fundamental transformations that occur across conversion and decomposition pathways. We hope that this review encourages a broader adoption of ssNMR methods in bioenergy research, where it can serve as a pivotal analytical tool for achieving sustainable biomass utilization and advancing a carbon-efficient bioeconomy.

Background

Lignocellulosic biomass, derived primarily from dedicated crops, agricultural waste, and forestry residue, exceeds 150 billion tons per year globally and presents enormous potential for the abundant production of biofuels and bioproducts from non-petroleum sources [1,2,3]. A wide array of strategies are being explored to overcome biomass recalcitrance and convert lignocellulosic biomass into fuels and products. For example, the conversion of biomass to traditional paper and packaging materials is a major global industry [4]. However, another major opportunity lies in second-generation biofuels, produced from non-food-based feedstocks like agricultural residues (e.g., corn stover, straw), forestry waste, and dedicated energy crops (e.g., switchgrass, poplar wood) [5, 6]. A current leading approach for second-generation biofuel production involves a three-stage process: (1) pretreatment to reduce recalcitrance and enhance enzymatic accessibility; (2) enzymatic hydrolysis to convert cellulose into fermentable sugars; and (3) fermentation to produce desired products like ethanol [7]. Common pretreatment methods include steam explosion and dilute acid or alkali treatments, with many alternative emerging strategies gaining attention including organosolv, ionic liquids, deep eutectic solvent pretreatment, mechanical refining, sonification, and microwave irradiation [8, 9].

A promising alternative strategy to the three-step biorefinery approach is consolidated bioprocessing, or CBP. By leveraging the natural biomass-degrading capabilities of thermophilic anaerobic microorganisms, like Clostridium thermocellum, CBP combines sugar production and fermentation into a single process potentially reducing costs and improving efficiency [10, 11]. Mechanical refining between sequential fermentations, referred to as cotreatment, has been proven effective at reducing certain biomass recalcitrance and enhancing total carbohydrate solubilization [12,13,14]. Also leveraging mechanical refining, the National Renewable Energy Laboratory (NREL) has pioneered a process called deacetylation and mechanical refining (DMR), which integrates low-severity chemical pretreatment and mechanical refining to boost sugar yields from lignocellulosic biomass while minimizing the production of fermentation inhibitors [15, 16]. This technology has gained attention because it can leverage equipment and processes similar to those used in the pulp and paper industry, reducing capital costs for biofuel production facilities.

Another leading pathway to biofuels production is thermochemical conversion strategies that utilize heat and chemical reactions to break down biomass into valuable products [17]. These methods include pyrolysis, which decomposes biomass at high temperatures in the absence of oxygen to produce bio-oil, biochar, and gases; gasification, where biomass is converted into syngas (a mixture of hydrogen, carbon monoxide, and carbon dioxide) through partial oxidation; and hydrothermal liquefaction, which uses high-pressure water to convert biomass into liquid biofuels as well as solid and gas products. These processes provide flexibility in producing a range of biofuels and co-products, including biochar, which has applications in materials, soil amendment and carbon sequestration [18].

In many of these approaches, as well as the pulp and paper industries, lignin is treated as a low-value waste byproduct. Lignin is the second most abundant polymer on earth after cellulose from terrestrial biomass and composes roughly 15–30% of the mass (feedstock dependent) and approximately 40% of the total energy value, but its complex and heterogeneous structure makes it challenging to valorize, and as a result it often ends up being underutilized. There are a number of promising strategies to handle lignin waste streams [19,20,21]. For one, lignin has high heating value and is often burned for process heating in biorefinery settings. While cost-effective, burning lignin waste streams fails to capitalize on lignin’s chemical potential to produce higher-value products. For example, lignin can be valorized directly in applications like biodegradable additives, lignin-based materials such as bioplastics and carbon fibers, and for long-term carbon storage solutions like incorporation into cement [22]. Alternatively, lignin-first biorefineries would first extract and depolymerize lignin into monomeric units to produce valuable aromatic chemicals before the cellulose is converted [23, 24]. Reductive catalytic fractionation (RCF) is an exciting example of such a lignin-first approach, whereby lignin is extracted and selectively depolymerized into low molecular weight aromatic units that can be used as fuel blends or building blocks for bioplastics, resins and other specialty chemicals [25, 26].

In all such explorations, including polysaccharide-based materials, lignocellulose conversion to biofuels, understanding biomass decomposition, thermochemical processing and waste stream valorization, precise and comprehensive analytical characterization of solid residues is essential. Various analytical tools offer critical insights into the composition, variability, and macromolecular structure of biomass, while also monitoring the molecular transformations and changes in residual solids throughout the deconstruction and conversion processes. This detailed characterization of residuals enhances understanding of process efficiency and product quality, illuminating the retention and transformation of key structural components within the biomass. Inevitably this could include a plethora of techniques including wet-chemistry methods (e.g., compositional analyses, sugar release assays), chromatography (HPLC, GC), spectroscopic techniques (FTIR, UV–Vis, pyrolysis-MBMS, solution-state NMR), microscopy (optical, fluorescence, SEM, TEM), thermal characterization techniques (TGA, DSC) and scattering methods (XRD, neutron scattering) [27,28,29]. The National Renewable Energy Laboratory (NREL) has developed a series of Laboratory Analytical Procedures (LAPs) to standardize and validate such methods for analyzing biomass composition, which can be found freely available at nrel.gov. However, most common analytical characterization techniques (e.g., benchtop, wet chemistry, and many instrumental methods) require some kind of chemical or physical treatment or sample dissolution, only analyze a portion of the material, and are broadly incapable of revealing molecular-level interactions and physical structures responsible for recalcitrance.

Overcoming some of these limitations, solid-state nuclear magnetic resonance (ssNMR) methods provide compositional, structural, morphological, and dynamical information of solid and semi-solid samples in their native, unaltered state without the need for chemical or physical alteration, thus preserving the integrity and complex structure of the neat material. Advanced multi-dimensional ssNMR methods have proven exceptionally valuable in the last decade towards elucidating the molecular structure and architecture of ¹³C-enriched plant cell walls, including grasses, hardwoods, and softwoods and solid products derived from these feedstocks [30,31,32,33,34,35,36,37,38,39,40,41]. While details differ among feedstock types, ssNMR results on ¹³C-enriched biomass have led to a general consensus macromolecular architecture as follows: a nanoheterogeneous arrangement of cellulose bundles are encased in a sheath of decorated hemicelluloses (e.g., acetylated xylan) such that the evenly spaced hemicellulose decorations protrude away from the cellulose surface and provide a favorable surface for interactions with polyaromatic lignin.

However, isotopic enrichment of biomass is practically limited due to the high cost of ¹³C labeled CO₂ and obvious challenges in producing mature lignocellulosic feedstocks with ¹³C enrichment at scales relevant to the bioenergy economy. Therefore, the aim of this review is to bring into focus the utility and application of ssNMR methods at natural isotopic abundance for revealing a fundamental understanding of lignocellulose, its deconstruction and conversion into marketable goods, and for various carbon management solutions (Fig. 1). We will first briefly review the fundamentals of solid-state NMR to provide necessary context for experimental methods and interpretation of cited examples. We then introduce the polymers and generalized ¹³C spectral features for lignocellulosics and related bioenergy materials detectible by ssNMR, then review first routine then advanced ssNMR techniques applicable for characterizing biomass and biomass-derived products and waste streams at natural ¹³C isotopic abundance. Generalized topics will include routine ¹³C spectra by cross-polarization or direct-polarization methods, spectral editing techniques, revealing dynamics-based information, select two-dimensional methods, and future directions. The interpretation of proton spin diffusion effects in relation to domain sizes and heterogeneities will be a theme throughout. As we drive towards an energy-secure bioeconomy and seek a fundamental understanding of the restrictions to biological and chemical conversion of biomass to fuels, chemicals, and various downstream products, we hope that this fresh perspective, in addition to other related summarizing ssNMR works [42,43,44,45,46,47], will guide researchers in the bioenergy space to apply solid-state NMR in addition to more traditional characterization techniques expanding their analytical toolkit.

Main text

Fundamentals of solid-state NMR

NMR is a sophisticated analytical technique that exploits the resonance of atomic nuclei when placed in an external magnetic field and exposed to electromagnetic radiation at specific frequencies. Most elements of the periodic table have at least one isotope that is NMR-active (e.g., spin quantum number is not 0), and are thus fundamentally detectable by NMR. In biomaterials research the most commonly used nuclei are ¹H, ¹³C, and sometimes ¹⁵N, ³¹P and ²H. ¹H is the most abundant and has the highest gyromagnetic ratio, making it the most common NMR nucleus for liquid-state NMR, but it is not commonly observed directly in the solid state due to strong and detrimental dipolar interactions (Fig. 2a). Atomic nuclei with a net charge generate a magnetic field due to their intrinsic spin angular momentum, which produces a magnetic moment (µ). This magnetic moment arises from the nucleus’ intrinsic spin, which can be visualized as the nucleus rotating around its own axis. When a nuclear magnetic moment is placed in an external static magnetic field, it results in a net magnetic moment (µ₀) within the sample, referred to as net magnetization (M₀). The magnitude of the detectible net magnetization is influenced by factors such as the number of detectable spins (N), the strength of the external magnetic field (B₀), the gyromagnetic ratio (γ) and spin quantum number (I) of the nucleus, and the sample temperature (T) (Fig. 2b).

To manipulate the bulk magnetization vector and probe atomic environments, radiofrequency (rf) pulses are employed to apply on the samples. These pulses vary in field strength (B₁) and frequency, typically near the Larmor frequency of the nuclei.

NMR spectroscopy provides profound insights into molecular and material structures by exploiting several key principles. In addition to the main factors of nuclei and strength of external magnetic field (Larmor frequency) and quantity (number of spins), NMR signals are also governed by the following key nuclear interactions: scalar couplings (J) between adjacent nuclei that give rise to multiplicities, chemical shielding (CS) interactions that depend on the local electronic shielding environment of the nucleus, space-based homonuclear and heteronuclear dipole–dipole interactions (D) between nuclei, and quadrupolar interactions (Q). The combined features result in deeply rich spectroscopic data reflecting the quantity, electronic environment (i.e., functional groups) and chemical bonding (Fig. 2b). Of particular importance is the sensitivity of NMR frequencies to the electron distribution around the nuclei. This chemical shielding interaction alters the nuclei's magnetic environment, resulting in slight frequency variations due to differences in the shielding constant (σ) among nuclei of the same isotope within a molecule. Frequencies are reported as chemical shifts (δ) in parts per million (ppm), with reference compounds like tetramethylsilane (TMS) and sodium trimethylsilyl propane sulfonate (DSS). Chemical shifts for ¹H typically range from 0 to 20 ppm and for ¹³C from 0 to 220 ppm (Fig. 2c).

Challenges of NMR of rigid solids

NMR interactions are generally anisotropic, meaning the strength of these interactions depend on orientation of the magnetic moments with respect to the external magnetic field or with respect to other coupled nuclei. In the solution state where molecular tumbling is sufficiently fast, the dipole–dipole interactions average to zero as does the anisotropic component of the CS term, leaving only the isotropic component of the chemical shielding term. Intensities and multiplicities (J couplings) survive; thus we are left with a data-rich NMR signal that provides information on population (number of spins), chemical shielding environment, and any scalar couplings to adjacent nuclei. In the solid or semi-solid state however, where molecular tumbling is insufficient to average anisotropic nuclear interactions, the resulting NMR signal becomes severely broadened rendering it virtually impossible to interpret for most sample types. The characteristic values of one-bond dipolar coupling constants for ¹H–¹H and ¹H–¹³C interactions are approximately 100 kHz and 22 kHz, respectively (Fig. 2b). It reflects the strong through-space dipolar interactions for nuclei at close proximity; the ¹H–¹H dipolar interaction is much stronger due to larger gyromagnetic ratio of protons. Further, in heterogeneous solids like biomaterials, the problem is compounded by the complex and varied molecular components, such as cellulose, hemicellulose, and lignin, which differ in crystallinity and chemical environment, further complicating the analysis and making it difficult to assign specific signals to individual molecular species without specialized techniques.

Key ssNMR techniques (MAS, CP, decoupling)

To overcome the limitations of NMR of rigid solids, three key techniques have been developed that are now standard practice: magic angle spinning (MAS), cross-polarization (CP), and high-power proton decoupling.

Magic-angle spinning (MAS) is a crucial technique developed in the 1950s to help overcome challenges associated with anisotropic interactions. By mechanically spinning the sample in a cylindrical rotor at the magic angle of 54.74° relative to the external magnetic field (Fig. 2d), MAS effectively averages out these anisotropic interactions leading to sharper, more interpretable spectral lines. This approach enhances the resolution and precision of ssNMR, providing clearer insights into the molecular and structural properties of solid samples.

Common pulse sequences for initial ¹³C polarization are shown in Fig. 2e. The simplest form of ¹³C ssNMR is direct polarization (DP), wherein the ¹³C nuclei are directly excited and observed. DP with MAS (DP-MAS) is often employed due to its straightforward approach, especially for non-rigid materials like solvent-swollen systems. However, it is crucial to acknowledge that ¹³C nuclei of highly rigid solids generally exhibit long longitudinal relaxation times (T₁), which along with lower sensitivity (γ_C = ¼ that of γ_H) and low natural isotopic abundance (1.1%) severely limits the effectiveness of direct polarization. Circumventing these issues, the second major ssNMR breakthrough was the advent of cross-polarization (CP), introduced by Schaefer and Stejskal in the 1970s, where signal (magnetization) from an abundant spin like ¹H is transferred to a dilute spin like ¹³C through the heteronuclear dipolar interaction [48, 49].

CP is widely utilized for two major reasons. First, CP is used to enhance the signal of low-γ nuclei, such as ¹³C and ¹⁵N, by exploiting the high gyromagnetic ratio and abundance of ¹H spins in biological samples. Theoretical polarization enhancements are predicted to be γ_H/γ_X, which corresponds to approximately fourfold for ¹³C and tenfold for ¹⁵N. CP achieves polarization transfer by synchronizing radiofrequency irradiation on ¹H and the observed ¹³C nucleus according to the Hartmann–Hahn (HH) condition (ɷ_1,H = ɷ_1,C ± nɷ_r), effectively locking the spins of the ¹H and ¹³C nuclei in the transverse plane [50]. Second, with CP-based experiments one is limited by the relaxation delays of ¹H instead of ¹³C, which are usually substantially faster and thus more signal averaging per unit time is possible. However, practical enhancements often fall short of theoretical values due to factors such as molecular dynamics and inhomogeneous polarization.

In CP, the HH matching profile can suffer significant mismatch issues when the MAS frequency approaches the sample’s dipolar couplings. This situation causes the profile to break into narrow bands separated by the spinning speed, complicating the achievement and maintenance of optimal matching conditions. Variable-amplitude cross-polarization (VACP) has been shown to effectively restore flat profiles at high spinning speeds [51]. A more advanced approach, the ramped-amplitude cross-polarization sequence (RAMP-CP) (Fig. 2e), further enhances this technique [52]. RAMP-CP enhances CP in ssNMR by varying rf field strength, broadening the HH matching condition, and improving ¹³C signal intensity. This mitigates rf inhomogeneities, ensures efficient polarization transfer, and enables more quantitative spectra. It also enhances resolution by reducing spinning sidebands, making it essential for complex material analysis.

The third key ssNMR building block is high-power proton decoupling, first introduced in the late 1950s and improved in subsequent decades [53, 54]. Here, strong radiofrequency fields are applied on the ¹H channel to suppress dipolar interactions between abundant ¹H nuclei and other nuclei, such as ¹³C. This decoupling technique eliminates or suppresses the influence of inter-nuclear couplings, which can broaden the spectra in solid samples, allowing for clearer detection of the less abundant nuclei. High-power decoupling is particularly critical in solid-state NMR, where strong dipolar couplings are prevalent, making it possible to observe sharp signals from nuclei like ¹³C even in complex materials like lignocellulosic biomass. The CP experiment can be modified with a series of 180° pulses to suppress sidebands, enhancing spectral clarity. Additionally, incorporating multiple CP blocks can improve quantitative accuracy by averaging polarization transfer across different molecular environments (Fig. 2f). More details are referred in the quantitative multi-CP and side-band suppression CP-TOSS section.

The combination of these techniques into cross-polarization magic angle spinning (CP-MAS) forms the foundation of modern solid-state NMR. To summarize and exemplify ssNMR on rigid solids like glycine, MAS reduces the broadening of signals by averaging out anisotropic interactions (Fig. 2g), while CP enhances the sensitivity of low-concentration nuclei like ¹³C and reduces experimental time thanks to the shorter ¹H relaxation times, and proton decoupling sharpens the spectra by minimizing heteronuclear ¹H–¹³C interactions (Fig. 2h). Together, these techniques allow for high-resolution analysis of solid materials like lignocellulosic biomass, revealing detailed molecular structures that are crucial for optimizing biofuel conversion processes. CP-MAS is commonly used as an initial step and integral component in various 1D, 2D, and 3D ssNMR pulse sequences, and further, the same concepts play a crucial role in transferring polarization from a more abundant sources, such as electrons, in indirect dynamic nuclear polarization (DNP) experiments (Fig. 2e).

CP-MAS, while widely used for signal enhancement, is not inherently quantitative due to its dependence on cross-polarization efficiency and dynamics. Quantitative solid-state ¹³C NMR spectroscopy is reliably and simply obtained using direct polarization DP-MAS, or with more advanced methods like multiple cross-polarization (MultiCP) [55]. DP-MAS is inherently quantitative, provided the recycle delay (d1) is set to 3–5 times the longest T₁ relaxation time, ensuring complete spin–lattice relaxation, though it may require extended and sometimes untenably long acquisition times for carbons with long T₁ values. In contrast, the MultiCP method offers an alternative for obtaining quantitative and high signal-to-noise ratio spectra with shorter acquisition times compared to direct polarization methods. The MultiCP pulse sequence works by repeated cross-polarization transfer from ¹H to ¹³C, with repeat CP blocks separated by a relaxation delay, resulting in incremental magnetization buildup for all carbon types (Fig. 2f). These repeated CP steps cumulatively enhance the ¹³C signal, even for carbons with weak dipolar couplings to protons, such as non-protonated carbons. All of this put together allows MultiCP to reliably supply quantitative signal enhancement, thereby improving sensitivity and spectral resolution of all varieties of spin systems.

In ¹³C MAS NMR spectra, distinguishing between isotropic peaks and spinning sidebands can be challenging, especially when spinning sidebands overlap with isotropic peaks from other resonances. This overlap complicates spectral analysis. To mitigate this issue, an effective method is to acquire spectra at different spinning speeds. This allows the sidebands to shift in frequency, while the isotropic peaks remain stationary, making it easier to separate them. Alternatively, using a pulse sequence to suppress spinning sidebands can simplify the analysis by leaving only the isotropic peaks. A widely used sequence for this purpose is the TOSS (TOtal Suppression of Spinning Sidebands) sequence (Fig. 2f) [56]. It involves applying four precisely timed 180° pulses prior to FID acquisition, which randomizes the phases of the spinning sidebands while preserving the phase of the isotropic resonances.

These set of approaches, including CP-MAS, DP-MAS, MultiCP and TOSS comprise the set of routine 1D ssNMR applications. The core observable of a routine ¹³C MAS NMR experiment is to non-destructively reveal the carbon types of a solid sample regardless of structural configuration (amorphous, crystalline, semi-crystalline). While ssNMR suffers from plenty of disadvantages including low throughput, limited spectral resolution, inherently poor sensitivity of ¹³C at natural abundance and long acquisition times, one of the most significant advantages of solid-state NMR is its non-destructive nature, allowing lignocellulosic biomass and other bioenergy-relevant materials to be studied in its native, unaltered state. ssNMR makes it possible to explore the complex, multi-phase structure of these materials without the need for dissolving or chemically/physically modifying the sample. This is particularly important for lignocellulose and carbon management research where maintaining the integrity of the materials during analysis can provide more accurate insights into its natural structural properties and how they influence the efficiency of conversion processes. The following sections will first set the stage by conveniently introducing the major biopolymers detectable by ssNMR, then delve into first routine and then advanced ssNMR applications in the study of biomass and related bioenergy materials at natural isotopic abundance.

Biopolymers of lignocellulose and related bioenergy materials

Lignocellulosic biomass is a complex organic material that forms the structural framework of plant cell walls, primarily consisting mostly of cellulose, hemicellulose, and lignin. Cellulose, a polysaccharide composed of glucose units, provides strength and rigidity to the cell wall, while hemicellulose, a diverse group of polysaccharides such as xylans and mannans, binds closely to cellulose fibers, enhancing structural support and providing an interaction surface for lignin. Lignin, an aromatic hydrophobic polymer within the plant cell wall, adds rigidity and resistance to microbial degradation, making lignocellulose highly durable. Another important polysaccharide is pectin, which is found abundantly in the primary cell walls and middle lamella of plants. Acting as a glue, pectin plays a key role in cell adhesion and maintaining the structural integrity of plant tissues [57, 58].

Cellulose, one of the most renewable and abundant biopolymers, is a key component (30–50 wt%) of plant cell walls, enhancing their mechanical strength [59]. Discovered by Anselme Payen in 1838, it has the formula (C₆H₁₀O₅)ₙ, where “n” denotes the degree of polymerization. Composed of linear chains of β(1 → 4) linked d-glucose units (Fig. 3a), cellulose forms rigid, semi-crystalline microfibrils (CMF) of β-(1 → 4)-glucan chains, with diameters of 3–5 nm. Each cellulose chain features a d-glucose unit at one end, with a C4-OH as the reducing end and a C1-OH as the terminal group. In some forms, like bleached wood pulp, additional carbonyl and carboxyl groups may be present, modifying its properties [60].

Hemicellulose is a heterogeneous group of polysaccharides in plant cell walls (approximately 15–35 wt%), comprising shorter, branched chains of sugars, such as xylose, mannose, and glucose (Fig. 3a). Hemicellulose often includes multiple sugar units and is categorized by its primary sugars, with key types including xyloglucans, xylans, mannans, glucomannans, and various other β-(1 → 3,1 → 4)-linked glucans [67]. Xylan, the most prevalent hemicellulose in terrestrial plants, is generally structured as a β-(1 → 4)-linked xylose backbone with varying substituents, exhibiting structural distinctions across plant types [67, 68]. In grasses and other non-grass monocots, xylan is primarily substituted with arabinose residues at the O-3 position of the xylosyl backbone, forming glucuronoarabinoxylan (GAX). These arabinose side chains can be further esterified by ferulic acid, which allows for cross-linking with lignin, contributing to the rigidity of grass cell walls. Additionally, grass xylan may include glucuronic acid (GlcA) side chains, though less frequently. In contrast, the xylan in woody dicots (e.g., hardwoods) is mainly glucuronoxylan (GX) is heavily acetylated, and is further decorated with α-(1 → 2)-linked glucuronic acid or 4-O-methylglucuronic acid residues attached at regular intervals to the xylan backbone [32]. This pattern is less complex than the branching seen in grass xylans. Both types can be acetylated, although the extent and pattern of acetylation vary, influencing how xylan interacts with other cell wall components. For example in hardwoods like poplar wood, xylan is acetylated every other xylose unit enabling the polymer to interact with the cellulose microfibril surface with acetate groups pointing generally away from cellulose, providing a favorable surface for lignin binding [31, 33, 41].

Pectin's structural complexity arises from its galacturonic acid backbone linked by α-(1 → 4) glycosidic bonds, with varying degrees of methyl esterification affecting solubility and gelling properties (Fig. 3a). It includes homogalacturonan (HG), rhamnogalacturonan-I (RG-I), and RG-II, which integrate into the cellulose–hemicellulose network to regulate wall porosity and cell expansion. Pectin also features neutral sugar side chains, such as rhamnose and arabinose, enhancing its three-dimensional structure [69].

Lignin exhibits remarkable structural complexity as a polymer composed of phenolic compounds, primarily formed from p-coumaryl, coniferyl, and sinapyl alcohols linked through various chemical bonds [70, 71]. The diversity in lignin's structure reflects its various polymers, functional groups, and linking methods. Three major types of phenylpropanoid building units can be identified: guaiacyl (G), syringyl (S), and p-hydroxyphenyl (H), corresponding to coniferyl, sinapyl, and coumaryl alcohols, respectively (Fig. 3b). Additionally, grasses contain notable amounts of ester-linked ferulate (FA) and p-coumarate (pCA) units, which contribute to cross-linking lignin with hemicelluloses [72]. Lignin also contains numerous functional groups, such as phenolic and alcoholic hydroxyls, methoxyls, carbonyls, and carboxyls, whose relative abundance varies by source, affecting its physical and chemical properties [73]. Unlike polysaccharides, lignin lacks a defined repeating unit, resulting in significant variability across plant species. This variability influences lignin's chemical reactivity and interactions with other biomolecules, while its degree of cross-linking enhances rigidity and resistance to degradation, providing structural support in cell walls and complicating biomass conversion in biofuel production. Additionally, lignin integrates into the cellulose–hemicellulose matrix, impacting the mechanical properties and accessibility of these components during degradation.

Both composition and structure vary significantly among differing feedstock types. By composition, hardwoods (e.g., poplar) generally contain about 40–50% cellulose, 25–35% hemicellulose-enriched xylose, and 20–30% lignin, which contains primarily guaiacyl and syringyl units, making them complex, dense, and durable in material structure. This composition and polymeric arrangements makes hardwoods structurally strong and stiff; thus, their main uses include furniture and cabinetry (Fig. 3d). The general composition of softwoods (e.g., pine) is 40–45% cellulose, 20–30% hemicellulose, mainly galactoglucomannan, and 25–35% lignin, with mostly guaiacyl units and no syringyl units. Importantly, these different compositions have a strong impact on the efficiency of enzymatic hydrolysis and fermentation processes, which all together may determine the sustainability of hardwood and softwood as feedstocks to produce biofuel and other bio-based materials. Lignocellulose in grasses, including wheat, rice, and switchgrass, is specifically constituted in ways that can enhance its use as a bioenergy feedstock. The composition generally consists of 30–50% cellulose, 20–30% hemicellulose rich in xylose and arabinose, and 10–25% lignin, mainly consisting of guaiacyl and syringyl units. The structural arrangement of biopolymers and their monomeric constituents, as well as other components, contributes toward the efficiency of hydrolysis with enzymes and fermentation. Thus, grasses represent one of the promising feedstocks in the realm of biofuels, even as they offer fast growth and adaptability.

Other biomass tissues in bark, roots, leaves and other plant organs consist of other biopolymers such as suberin and cutin. For example, cork, derived from the bark of the cork oak tree (Quercus suber), is a versatile biomaterial known for its lightweight, buoyant, and insulating properties. Composed mainly of 40% suberin, 22% lignin (predominantly guaiacyl and syringyl units), and 20% carbohydrates (including cellulose and hemicellulose), cork’s unique lignocellulosic composition provides water resistance and fire retardancy (Fig. 3d) [74]. Suberin is found in below-ground tissues like the root epidermis, endodermis, and periderm, and above-ground periderm from bark. This protective cell wall biopolymer is composed of phenolic and long-chain fatty acid monomers, with glycerol-based cross-linking. This is in contrast to other non-carbohydrate biopolymers, which include lignin and cutin, due to the presence of two unique domains: one poly(phenolic) and one poly(aliphatic), each with a distinct chemical composition (Fig. 3c). Both occur in the same cells, where they provide a barrier to prevent water loss, guard against pathogens, and participate in wound healing [75,76,77]. Cutin is a major constituent in plant leaves, flowers and fruit cuticles where it plays roles in water, solute and gas exchange as well as resistance to biotic and abiotic stressors [78]. Suberin and cutin are both lipopolymers that impart resilience to plant tissues and may play important roles in a bioeconomy that depends on sustainable plant traits and efficient use of all carbon-containing plant components [79].

Routine 1D ¹³C solid-state NMR of lignocellulose

Holistic overview of chemical shift regions and spectral features

Lignocellulosic compounds can be characterized and quantified using ssNMR while preserving their native state. The diverse chemical environments and functional groups present in various lignocellulosic materials result in several distinguishable chemical shift regions, allowing for effective differentiation and analysis of these complex structures, though with overlapping of signals existing at some levels. Here, we first provide a holistic overview of the ¹³C ssNMR fingerprint including the chemical shift ranges and structural features of typical biomass, then extend deeper into characterization applications aimed at tracking biomass conversion and deconstruction (Fig. 4).

The ¹³C ssNMR spectra of biomass can be generally divided into four major spectral regions (aliphatic, carbohydrate, aromatic, carbonyl) with further sub-division if desired. The aliphatic region (~ 0–60 ppm) includes methyl and methylene carbons from hemicelluloses, protein backbone and sidechain, fatty acids and lipids, and methoxy carbons (Fig. 3d). The most characteristic ssNMR features for biomass include the acetate methyl at 22 ppm from acetylated hemicelluloses, fatty acid and lipid signals near 30 ppm, and lignin methoxy groups at 56 ppm. Suberin for example is rich in esterified fatty acids and thus reveal strong methylene features at 30 and 32 ppm. If pectin is abundant, for example in primary cell walls, a methyl ester group near 54 ppm is detectable, typically assigned to methyl-esterified galacturonic acid. Although minor in secondary cell walls, the rhamnose C6 methyl can often be seen near 17 ppm. The carbohydrate region exhibits characteristic spectral features within the range of 60 to 110 ppm. The anomeric carbon (C1) is typically deshielded relative to other non-anomeric carbohydrate signals and is observed as a cluster from approximately 98 to 105 ppm. This region is dominated by the cellulose C1 at 105 ppm, with an upfield shoulder from a heterogeneous mixture of hemicellulose environments. In poplar wood for example, the predominant hemicellulose (acetylated xylan) is multi-conformational with extended xylan (twofold, cellulose bound) exhibiting an anomeric carbon shift near 104 ppm while more disordered (threefold) C1 shifts coming in near 101–102 ppm. Other non-anomeric carbohydrate signals, for example cellulose C2,3,5 and xylan C2,3 overlap near 72–73 ppm.

Cellulose chemical shifts in NMR spectroscopy provide critical insights into its structural and dynamic properties, which are essential for understanding its role in biological and material systems. In solid-state ¹³C NMR, cellulose exhibits characteristic chemical shifts for its carbon atoms: C1 (~ 105 ppm), C4 (~ 89 ppm and ~ 84 ppm), C6 (~ 65 and ~ 62 ppm) and C2/3/5 (~ 75 and ~ 72 ppm). The split shifts of the C4 and C6 sites reflect the influence of hydrogen bonding, crystallinity, and molecular packing, particularly that of the hydroxymethyl C6 unit. Historically, the distinction between crystalline and amorphous regions was based on the 89/84 ppm chemical shift split, which was instrumental in developing the crystallinity index CrI (89/84 + 89) as a measure of cellulose order [42]. However, this simplistic interpretation is complicated for intact lignocellulose because the 84-ppm signal overlaps with contributions from matrix polymers, such as hemicelluloses and lignin inter-unit linkages, restricting the direct assessment of cellulose within intact biomass.

Recent advancements have introduced a more nuanced interpretation of cellulose chemical shifts. According to this refined interpretation, the 89 ppm signal reflects the more rigid packing structure of the hydroxymethyl groups in the tg conformation, which are predominantly found in the interior of the cellulose fibrils. In contrast, the 84-ppm signal corresponds to disordered hydroxymethyl groups in the gt/gg conformations, which are prevalent at the fibril surface. This understanding highlights that crystalline regions, which are more ordered, are predominantly located in the fibril core, while amorphous regions, which are more disordered, are more common at the surface. However to complicate matters further, the surface/interior assignment of 84 vs 89 ppm signals is contested by recent findings that a subfraction of surface cellulose can present at 89 ppm, leading to some authors assigning signals to domain 1 and domain 2 [30, 41]. A consensus is building that some surface cellulose may have its hydroxymethyl oriented towards the fibril interior, and it’s possible that in the densely packed secondary cell wall, surface cellulose units may hydrogen bond with matrix polysaccharides like bound xylan, stabilizing the tg conformation. To summarize, cellulose C4 at 89 ppm apparently arises when cellulose hydroxymethyl adopts a tg arrangement, which is more ordered or crystalline-like, and is over-represented but not exclusively within the cellulose microfibril core, while C4 at 84 ppm arises from a more disordered hydroxymethyl packing of gt/gg and appears more heavily represented on the fibril surface [80]. Therefore, assignments seen in the literature are mixed or interchanged: 89 ppm (crystalline, tg, interior, domain1) and 84 ppm (amorphous, gt/gg, surface, domain2).

Spectral overlap in the carbohydrate region of lignocellulose NMR spectra complicates cellulose crystallinity analysis, as signals from lignin inter-unit linkages overlap with cellulose carbons. For instance, the cellulose C1 resonance at ~ 105 ppm overlaps with hemicellulose C1 as well as syringyl lignin (S-lignin) C2,6. The carbohydrate signals in the 90–60 ppm range also overlap with lignin inter-unit linkages, for example the β-O−4 Cβ lignin linkage around 83 ppm, Cα near 73 ppm, and Cγ near 62 ppm. These complexities emphasize the need for advanced NMR techniques, such as spectral editing and 2D correlation or selective isotope labeling to differentiate cellulose from hemicellulose and lignin contributions.

The aromatic region spanning roughly 110–160 ppm is often dominated by lignin but can also include contributions from several other components depending on the biomass type or processing state. In addition to the characteristic lignin methoxyl group signals around 56 ppm, the aromatic region is information-rich regarding lignin’s composition and abundance. For example, syringyl units (S3/5) resonate distinctly at ~ 152 ppm while guaiacyl units (G3/4) appear around ~ 147–148 ppm [81]. Protonated ring carbons in guaiacyl units (G2/5/6) and typically resonate at ~ 110–120 ppm, while S2/6 overlap with carbohydrate anomeric carbons near 105 ppm. Para-hydroxyphenyl (H) lignin sometimes can be identified H3/5 at 116 ppm and H4 at 159 ppm. These distinct chemical shifts provide a structural fingerprint for assessing the relative contributions of S, G, and H lignin. In addition to lignin, hydroxycinnamates including ferulates and coumarates (common in grasses) and other polyphenolic compounds including tannins and flavonoids can also contribute to the aromatic region. Hydrolysable tannins and condensed tannins, such as proanthocyanidins, are abundant in certain biomass sources like bark, leaves, and some hardwoods, with signals from aromatic rings spanning ~ 110–160 ppm. Similarly, the aromatic domains of suberin contribute signals from phenolic components (~ 140–155 ppm), which are often linked to lignin-like structures. Minor contributions can also arise from the aromatic amino acids tyrosine and phenylalanine in proteins, which generate signals around ~ 115–135 ppm. These are typically present in small amounts but may become noticeable in protein-rich biomass and would be correlated with protein features in the aliphatic region. In biochars and other carbonized materials, the aromatic region reflects the presence of partially condensed or graphitic aromatic carbons. These signals, often broad and overlapping, are indicative of the degree of aromatic condensation and structural complexity. Overall, while lignin dominates in lignocellulose, the aromatic region in ¹³C NMR spectra serves as a complex landscape reflecting the diversity of aromatic and phenolic structures present in lignocellulosic and related biomass materials.

Lignin quantification and characterization

Lignin quantification and characterization are of utmost importance to understand the role of lignin in plant biomass, its interaction with the processing and utilization of lignocellulosic materials for bioenergy and bioproducts, and the valorization of biomass in a sustainable manner. Accurate lignin analysis would likely be very helpful on the development of strategy in biomass valorization such as enhancing enzyme hydrolysis efficiency, or optimizing fermentation processes.

There are several methodologies proposed for the quantification of lignin using 1D ¹³C CP spectra. Gao et al. developed a non-destructive quantification method of lignin using ¹³C CP-MAS spectra with an internal standard and spectral integration (Fig. 5a) [82]. A calibration curve of the quantification of lignin was obtained by using sodium-3-trimethylsilylpropionate (TMSP) as an internal standard in ¹³C CP-MAS. TMSP was chosen because it has a sharp peak at 0 ppm that is not overlapped by other peaks, and also because TMSP is soluble in distilled water, which facilitates its even dispersion. A 0.100 g/mL TMSP solution was prepared and added to each 250 mg biomass sample. In order to maintain homogeneous distribution, the samples were adjusted with distilled water or, when necessary, centrifuged for a short time. After freeze-drying, the resulting NMR spectra had given a linear calibration curve obtained by relating the lignin signal area to the TMSP peak. The method proved highly accurate and stable for the quantification of lignin, and its internal validation was approved.

Besides integration of the aromatic region, Cipriano et al. have used the well-resolved methoxyl group signal at 56 ppm for the quantification of lignin [83]. The ¹³C CP-MAS NMR spectra for pure lignin, cellulose, a cellulose–lignin mixture, and sugarcane bagasse (SB) provided resolutions representative of the distinct signals for lignin and cellulose and hemicellulose in SB (Fig. 5b). Overlapping carbohydrate signals complicate separation, but the methoxyl carbon signals in lignin give clear, quantifiable data for the lignin content. A calibration curve was established by correlating the methoxyl signal intensity with lignin mass in the cellulose–lignin mixtures. This allowed for accurate quantification of lignin in an array of biomass samples which were cross-validated with acid hydrolysis (AH) measurements. The majority of NMR-AH comparisons were consistent except in samples where carbohydrate hydrolysis was incomplete. Although the content of methoxyl groups does vary depending on the plant source, the methoxyl signal represents a very robust and reliable signal to quantify lignin in a wide range of biomass materials [85]. This method proved highly reproducible across several NMR instruments and thus finds wide applicability. Carbon signals from aromatics used simultaneously for the quantitative estimation of lignin were less effective due to poorer resolution and broader chemical shift ranges.

In addition to quantification, lignin composition can be partially interpreted by routine ssNMR. Previously, Manders et al. introduced non-destructive ssNMR to determine S/G ratios in hardwood by comparing spectra from hardwood, containing both S and G units, and softwood, which only contains G units [86]. This method, however, tends to underestimate the contribution of S-lignin due to normalization issues during spectral subtraction. Happs et al. addressed this by applying spectral deconvolution in the oxygenated non-protonated aromatic region (140–160 ppm) in ¹³C CP spectrum to improve the accuracy of S/G ratio calculations (Fig. 5c) [84]. They validated their method using poplar and found good agreement with results from thioacidolysis, py-MBMS, and HSQC. Although the ssNMR deconvolution approach is non-destructive, representative of the whole cell wall, and allows for the analysis of multiple phenotypes, it has drawbacks such as low throughput, sensitivity to incorrect peak fitting parameters (e.g., initial chemical shifts, line widths and Gaussian/Lorentzian ratios), and limited applicability to complex species like grasses. Nevertheless, it shows promise as an alternative to traditional methods for accurately quantifying S/G ratios, particularly in hardwood species.

Cellulose crystallinity

Cellulose crystallinity index (CrI) is an important factor that relate significantly to the physical and mechanical properties of cellulose-based materials, such as strength, stiffness, and water absorption. It is widely used to describe the relative portion of crystalline fractions in cellulose and quantify their modifications in plants and cellulosic materials following genetic engineering, and physicochemical or biological treatment. Solid-state NMR has been not only instrumental in discovery of two allomorphs of native cellulose, but also on measurement of cellulose crystallinity.

While the classical interpretations of cellulose crystallinity in ssNMR spectra (e.g., 89 vs. 84 ppm) is now better understood as more nuanced [80], studying the crystallinity of cellulose by ssNMR is nevertheless beneficial and informative. This is especially true for isolated cellulose preparations in which overlapping matrix polymers have been largely removed through processing efforts. Typically, crystallinity index is measured using isolated pure cellulose or extracted bacterial cellulose samples. Cellulose isolated from pulp, genetic engineered plants, and pretreated biomass are well-suited for CrI measurement using ssNMR. For example, Park et al. determined crystallinity index (CI) of Avicel PH-101 (a pure cellulose sample) used solid state using ¹³C CPMAS NMR by dividing the area of the crystalline peak (87–93 ppm) by the total area of the C4 peaks (80–93 ppm), a method well-suited for pure cellulose samples (Fig. 6a) [87]. The Ragauskas lab has documented a dataset of cellulose crystallinity index of 72 topline switchgrass natural variants using ssNMR spectroscopy [88]. We have also measured cellulose CrI of a total of an additional 54 various switchgrass lines, shown for the first time in Fig. 6b. For these studies pure cellulose samples were prepared for crystallinity measurement after removing the extractives, hemicellulose and lignin from the switchgrass, according to the published isolation procedure [89]. The cellulose crystallinity index across both sets was found in the range of 36–43% with the average of 39%, indicating only slight variation of cellulose crystallinity among the switchgrass natural population. It therefore appears that cellulose crystallinity estimates by ssNMR for isolated preparations only have a limited relationship to the recalcitrance difference in the biological conversion to sugars [90].

While ssNMR for cellulose CrI measurement have been largely applied on isolated cellulose samples, the cellulose isolation process is usually laborious and time-consuming. Therefore, ssNMR on characterization of plant biomass without isolation of cellulose that still contain other components has also been explored. For example Yan et al. studied the changes of whole biomass crystalline structure in poplar after flow-through pretreatment at various temperature based on signals in the C4 region of the ¹³C CP-MAS spectrum (Fig. 6c) [91]. This analysis revealed the gradual changes in C4 peaks corresponding to a degradation of amorphous components, enhancing our understanding of temperature effects on biomass crystalline structure disruption during flow-through. However, it should be noted that the crystallinity index measured using ssNMR for whole biomass is not cellulose CrI per se due to the overlapping signals contributed from other amorphous components like hemicellulose, lignin, or pectin. Spectral editing methods, described in later sections, can be useful for obtaining a cleaner look into the cellulose substructure for intact lignocellulose.

Tracking biomass deconstruction and conversion

Routine ¹³C ssNMR is not only invaluable for characterizing the native structure and composition of lignocellulose and its constituent components, but also serves as a powerful tool for tracking the transformations that occur during biomass deconstruction and conversion. The effective conversion of lignocellulose requires overcoming its natural recalcitrance, primarily due to the complexity of plant cell walls [93]. Various pretreatment and conversion methods including chemical, physical, biological, and thermal have been developed to enhance cellulose accessibility, lignin removal, and improving overall component fractionation. For example, fungal pretreatment offers strong degradability but may consume valuable carbohydrates and require longer processing times. Chemical pretreatment, such as using ionic liquids (ILs) and alkaline methods, provides high efficiency in lignin removal and cellulose accessibility but can generate inhibitors and require careful handling [94]. Meanwhile, bacteria and microbial consortia present adaptability and efficiency, though they face challenges in laboratory culturing and competition [95]. The following sections, loosely divided by processing method, describe key examples of utilizing routine 1D ssNMR (CP, MultiCP and DP-MAS) for monitoring spectral changes associated these processing and conversion steps, providing critical insights into how these treatments overcome biomass recalcitrance and enhance accessibility for bioenergy applications.

Chemical pretreatments

Chemical pretreatment strategies, including dilute acid, alkaline and ionic liquids, are among the more common ways to reduce recalcitrance and expose cellulose access for microbial conversion. As a clear example, Lima et al. studied the composition and effectiveness of five different pretreatments of various grassy feedstocks and bark varieties for the production of bio-renewables [96]. The biomasses contained 39–46% cellulose, 16–27% hemicellulose, and 21–25% lignin and were pretreated in hot water, acid, NaOH, and NaHSO₃ at different temperatures. Routine ssNMR analysis was applied to all feedstocks under various pretreatment conditions, demonstrating its power to non-destructively reveal structural and compositional changes. As a highlight, Fig. 7a shows how CP-MAS spectra tracked structural changes in sugarcane bagasse subjected to alkaline treatments at 50 °C and 130 °C. The spectra revealed that both temperatures effectively removed acetyl groups (22 ppm) and hemicelluloses (~ 70–80 ppm), but their impact on lignin removal differed significantly; at 50 °C, lignin removal was limited, as indicated by the persistence of aromatic signals (~ 110–160 ppm), while treatment at 130 °C showed enhanced delignification alongside increased cellulose crystallinity (~ 88 ppm). The ssNMR analysis also confirmed minimal compositional changes in samples pretreated with hot water or bisulfite, underscoring the limited efficiency of these methods. Among the pretreatments, it was shown that NaOH at 130 °C demonstrated the best performance in terms of enzymatic sugars release. These results suggest that NaOH will enhance saccharification more effectively and significantly reduce lignin under wide biomass type moderations of temperature. In a similar example applied to poplar wood, Sun et al. investigated the dilute acid pretreatment for the production of bioethanol and found that cellulose crystallinity and accessibility were increased, while enzymatic yield was higher in the delignified samples [97]. ¹³C CP-MAS showed that amorphous cellulose was preferably removed, promoting its recrystallization.

In their research, Hossain et al. explored the effectiveness of imidazolium-based ILs containing acetate, formate, and chloride anions for pretreating lignocellulosic biomass [98]. The study examined both aprotic and protic varieties, utilizing quantitative ¹³C Multi-CP. Notably, the protic IL 1-ethylimidazolium chloride (EimCl) emerged as particularly effective, capable of dissolving whole wood (Pinus radiata) and significantly improving enzymatic hydrolysis of the biomass. In contrast, acetate- and formate-based protic ILs primarily served to extract lignin. Analytical techniques, including ¹³C Multi-CP and thermogravimetric analysis, revealed that EimCl selectively removes amorphous components from the biomass, thereby increasing cellulose crystallinity (Fig. 7b). This enhancement improves cellulase accessibility, leading to higher glucose yields. Meanwhile, the protic [Eim][OAc] primarily facilitates delignification without substantially altering cellulose characteristics [101]. These findings highlight the critical role of cellulose accessibility over mere lignin removal in effective biomass pretreatment. Additionally, NMR analysis indicates that acetate-based ILs significantly reduce the methoxy groups in lignin, suggesting structural changes, while chloride-based ILs have a milder impact on lignin structure. Overall, the type of anion and whether the IL is protic or aprotic are essential determinants of biomass pretreatment efficacy. Also applied to ionic liquid pretreatment with ssNMR, Husson et al. studied IL pretreatments, [Emim] + [CH3 COO] and [Emim] + [MeO(H)PO2] and found a significant decrease in the CrI of cellulose, while an increased glucose yield was obtained after [Emim] + [MeO(H)PO2] pretreatment due to increased accessibility of fibers [102].

Wi et al. evaluated the effectiveness of hydrogen peroxide-acetic acid (HPAC) pretreatment on pine wood biomass and were able to attain a lignin reduction of up to 98% with enhanced enzymatic digestibility (Fig. 7c) [99]. CP-MAS analysis indicated selective removal of amorphous components leading to higher glucose yields (93.4%) and better bioethanol production efficiency. Sathitsuksanoh et al. examined the cellulose solvent- and organic solvent-based lignocellulose fractionation (COSLIF) pretreatment, which combined phosphoric acid and organic solvents, significantly reducing CrI and enhancing cellulose accessibility through the degradation of amorphous regions (Fig. 7d) [100]. These studies demonstrate the effectiveness of NMR in monitoring lignocellulose structural and compositional changes induced by various pretreatments, which eventually improves cellulose deconstruction for biofuel production.

Physical impacts

Physical treatments, such as milling, extrusion, irradiation, sonication and other mechanical processes, are employed to reduce biomass particle size, and disrupt its structural integrity, and enhance polysaccharide accessibility and overall process efficiency. These physical treatment methods can be combined with other processing techniques for full and compounded effects, and their structural changes can be tracked using ssNMR. Ekwe et al. investigated the effect of ball milling on bamboo structure by using Multi-CP (Fig. 8a). Consequently, a treatment of dry biomass with 60 min of ball milling drastically reduced the crystalline cellulose content to about 10% in a crystalline state and increased the amorphous cellulose fraction. Lignin and hemicellulose showed less structural change (< 5%), indicating their stability during milling. Subsequent enzymatic hydrolysis partially removed hemicellulose and reduced amorphous cellulose while lignin remained intact. The shifting of the crystalline cellulose peak reflects a possible transformation of cellulose Iβ to Iα, while no formation of cellulose II was observed. NMR signal integration confirmed that the lignin content did not change after pretreatment and hydrolysis, indicating complete recovery of the sugars. These results demonstrate the effectiveness of mechanochemical pretreatment in disrupting cellulose crystallinity for the enhancement of enzymatic hydrolysis.

Similarly, Qu et al. demonstrated that ultrafine ball milling of wheat straw prior to mild alkali treatment significantly enhances lignocellulose enzymatic deconstruction (Fig. 8b), with up to 98% glucose yield observed by enzymatic hydrolysis [103]. ¹³C CP-MAS revealed several key findings. First, ssNMR showed that NaOH treatment after extensive ball milling (ultrafine powder) was much more effective at removing lignin and hemicelluloses compared to NaOH treatment of un-milled material (< 1 mm and < 0.5 mm particle sizes). This can be seen by tracking acetate and lignin aromatic/methoxy signals before and after NaOH treatment at various particle sizes (Fig. 8b). CP-MAS spectra also reveal structural changes to cellulose upon aggressive ball milling. Substantial lineshape broadening of cellulose C1-C6 peaks was shown in ultrafine powder prior to alkaline treatment (ultrafine powder AT0), indicating disruption of inter- and intramolecular bonds during grinding, with signals at 62 and 84 ppm attributed to amorphous cellulose. Ultrafine powder also exhibited a unique saccharide oligomer signal near 97 ppm, which in hindsight could be assigned to cellulose end groups and therefore reduced degree of polymerization. Upon exposure to water (dilute NaOH) the cellulose recrystallizes, with ssNMR spectral features resembling cellulose II structure in addition to loss of lignin and hemicellulose.

Complimenting Qu’s work, a few years later Yuan et al. employed Multi-CP NMR to analyze Avicel PH-101 cellulose before and after ball milling followed by acid hydrolysis or ethanolysis, providing critical insights into cellulose structure (Fig. 8c) [105]. Signals at 97 and 92.7 ppm, in a 2:1 intensity ratio, were assigned to reducing chain ends, confirmed by their disappearance after ethanolysis, which forms ethyl ethers. Spectral shifts revealed cellulose-II crystallites with resonances at 107, 88, and 63 ppm, highlighting crystal modifications and reduced crystallinity. Despite these treatments, the chain-end signals remained unchanged, indicating their structural stability. In another example, Balch et al. demonstrated that one-pot microbial fermentation of switchgrass by Clostridium thermocellum, a process termed consolidated bioprocessing (CBP), can be enhanced by ball milling during fermentation. CBP augmented with milling is referred to as cotreatment, or C-CBP [12]. The authors report substantially higher total carbohydrate solubilization (TCS) when CBP is augmented with ball-milling (~ 88% TCS) compared to fermentations without cotreatment (~ 45% TCS). CP-MAS results (Fig. 8d) revealed minimal lignin modification caused by cotreatment, in contrast to hydrothermal pretreatment, which significantly alters lignin structure. A reduction in hydroxycinnamate moieties was also observed around ~ 116 ppm, suggesting their removal due to cotreatment. Interestingly, the authors reported a broadening of lignin signals, which presumably is attributed to residual metal contamination from milling with stainless steel hardware. Ball-milling with non-metal materials like zirconium oxide would eliminate this potential contamination. In addition to ball milling, ¹³C ssNMR methods have also been leveraged to characterize other physical pretreatment tactics including steam explosion [106,107,108], mechanical extrusion [109], and both microwave-assisted and ultrasound-assisted chemical pretreatments [110, 111].

Thermal conversion processes—biochar

CP-MAS and DP-MAS methods have also been utilized to analyze biochar chemistry based on differences in feedstock and conversion conditions, although there are challenges associated with concentration of metal content in biochars impacting NMR observability of these materials. Generally, residual biopolymer content (carbohydrates, lignin), functional groups and aromaticity are important features analyzed in biochars using CP-MAS techniques [43]. Methods such as ¹³C CP-MAS are being increasingly applied in the characterization of organic matter because of enhanced sensitivity and shorter measurement times. For example, Ben et al. performed a comprehensive ¹³C CP-MAS analysis on the temperature-dependent structural changes of biochar structures originating from different biomass sources (Fig. 9a) [112]. During low-temperature pyrolysis (400 °C), functional groups such as aromatic C–O, methoxyl-aromatic (~ 145 ppm), and aliphatic C–O bonds (~ 85 ppm) were preserved in the bio-chars. With increasing temperature, these functional groups undergo step-by-step decomposition. By 600 °C, bio-chars from cellulose, lignin, and tannin are transformed into condensed aromatic structures, reflecting the loss of side chains and oxygen-containing groups, accompanied by enhanced aromaticity and reduced structural complexity. Further, Brar et al. employed ssNMR among other techniques to investigate changes in biomass structure that determine the efficiency of saccharification after autohydrolytic and hydrothermal pretreatments [113]. Thus, native sugarcane bagasse, whose morphology is described by tightly packed fiber bundles with a coating of lignin, showed dramatic changes in morphology after pretreatment, that is, partial removal of the lignin layer due to delignification with lignin redeposition, thereby exposing fiber bundles (Fig. 9b). This morphological change increases the efficiency of enzymatic hydrolysis. Hydrothermal pretreatment was more effective compared with autohydrolytic treatment; this was probably due to the fact that hemicellulose is more effectively removed, and that the lignin structure is changed, offering the enzyme good accessibility to insoluble substrates. The most characteristic peaks in ¹³C CP-TOSS within the range between 50 and 120 ppm were assigned to cellulose although some contribution from hemicellulose and lignin was detected. It should be pointed out that peaks assignable to hemicellulose and lignin became weaker or even disappeared in the pretreated samples, which has been further supported through chemical compositional analysis. Furthermore, Chandel et al. used CP-TOSS to eliminate the overlap of spinning sidebands in the NMR, the former reported an increase of CI from 65.75 to 67.25 following enzymatic hydrolysis of SB [114].

Freitas et al. also offered comprehensive insights from CP-MAS into the thermal transformation of lignocellulosic biomass, effectively distinguishing between crystalline (89.3 ppm) and amorphous (84.4 ppm) cellulose regions, hemicellulose acetates (21.8 and 174.3 ppm), and lignin-associated methoxyl (56.8 ppm) and aromatic carbons (115–150 ppm) [115].

However, CP-based methods have serious limitations in the analysis of biochars. The efficiency of ¹³C CP-MAS is poor for non-protonated carbons, mobile components, and fused-ring aromatic carbons in biochars, which results in considerable signal loss [43, 44]. Unlike CP-MAS, which depends on proton proximity, DP-MAS enables quantitative detection of all carbon types, making it particularly suited for analyzing carbonized materials with low hydrogen content. Shown in the same figure, Freitas pointed out the relevance of low-field ¹³C DP-MAS in the characterization of pyrolyzed lignocellulosic materials at high heat treatment temperatures (HTT) (Fig. 9c). High-field NMR, which gives excellent resolution to distinguish crystalline and amorphous cellulose, hemicellulose acetates, and lignin components, becomes inefficient at high HTTs due to the loss of hydrogen, where cross-polarization efficiency is weakened, hence leading to weaker signals, while aromatic carbons increase in chemical shift anisotropy (CSA), therefore leading to significant spinning sidebands, which obscure spectral resolution. Low-field NMR overcomes these issues by minimizing the CSA effects and providing much cleaner spectra dominated by an aromatic resonance at ~ 125 ppm, which indicates a highly ordered carbon matrix developed during pyrolysis. Direct polarization further helps to overcome the limitations of cross-polarization and thus allows for the robust characterization of highly carbonized materials. This makes lower-field DP-MAS NMR an important tool in understanding the structural transformation of lignocellulosic biomass under pyrolytic conditions. For a more comprehensive dive into ssNMR of biochars, please see the review by Baccile [43].

Biological treatments

Biological treatments, including fungal, microbial, enzymatic, and natural decay processes, play a pivotal role in biomass deconstruction by selectively targeting lignin, carbohydrates, and other structural components. Routine solid-state NMR techniques, particularly CP-MAS, have proven invaluable for characterizing the complex chemical and structural changes induced by these treatments. For example, CP-MAS was used by Sluiter and co-workers to help validate a new analytical protocol designed to quantify the abundance of cellulosic glucan in the presence of starch [116]. The protocol solves a challenging problem in biomass sugars compositional analysis for feedstocks in which both starch and cellulose are present, since both these glucan-derived polymers decompose into glucose monomers by two-stage acid hydrolysis and therefore the presence of starch can over-estimate the cellulosic glucan content. Accurately quantifying cellulosic versus starch-derived contributions to biofuel production is critical for ensuring compliance with regulatory frameworks and maximizing financial incentives, such as those provided under the Renewable Fuel Standard (RFS) and cellulosic-specific tax credits. Therefore, to accurately quantify cellulosic glucan within biomass when starch is also abundant, the authors proposed first chemically treating the sample with dilute NaOH to soften resistant starch granules, followed by complete enzymatic removal the starch with amylase while leaving cellulose unaffected. The CP-MAS results from Sluiter et al.’s study provided critical evidence validating the selective removal of starch from biomass while retaining cellulose (Fig. 10a). Specifically, the characteristic cellulose signals at 89 ppm and 105 ppm remained intact after dual chemical and enzymatic treatment, while the broad starch-associated resonance at 100–102 ppm was absent in the treated samples. The residual signal in this region was attributed to hemicellulose rather than starch, as confirmed by supplemental NMR methods.

CP-MAS has also provided valuable insights into the natural microbial decay of biomass, for example from soil microbiota or fungi, revealing selective lignin degradation, carbohydrate removal, and structural transformations that govern decomposition rates and biomass digestibility. For example, Bonanomi et al. utilized ¹³C CP-MAS for the detailed chemical characterization of plant litter, demonstrating its superior predictive power for decomposition rates compared to conventional indices [117]. The CP-MAS spectra enabled the quantification of carbon functional groups, including alkyl carbons (lipids, waxes), O-alkyl carbons (carbohydrates), aromatic carbons (lignin-derived), and carbonyl carbons (carboxylic acids, esters). Spectral integrations are shown in the bar chart in Fig. 10b. A key finding was the strong correlation between the relative abundance of alkyl and O-alkyl carbons and litter decay rates, with alkyl-rich components being highly recalcitrant and O-alkyl-rich fractions more labile and easily degraded by microbial activity. This chemical profiling surpasses conventional metrics such as carbon-to-nitrogen content and lignin/N ratios by capturing the heterogeneity of carbon structures influencing decomposition [120, 121].

Lignin-degrading fungi that have been used in biomass deconstruction include Ganoderma lucidum and Fomitopsis pinicola. Fu et al. developed a method of quantitatively measuring the amount of lignin using ¹³C CP-MAS by producing a standard curve using integral values from 157 to 142 ppm (Fig. 10c) [85]. With this curve, these authors were able to measure the amount of lignin in untreated kenaf biomass at 32% [122]. This research further demonstrated that lignin degradation via enzyme can also be measured quantitatively. In particular, treatment using white-rot fungi G. lucidum was effective in reducing the lignin content in kenaf by more than 16% to 26%, while brown-rot F. pinicola did not greatly influence the change in kenaf lignin content.

Martinez et al. employed ¹³C CP-MAS to investigate the progressive lignin degradation in Eucryphia cordifolia wood by G. australe [118]. The spectra, shown in Fig. 10d, reveal pronounced changes in aromatic C signals with partial delignification at the medium decay stage and near-complete lignin removal at the advanced stage (Klason lignin ~ 6%), accompanied by increased cellulose content and in vitro digestibility. Notably, the carbohydrate signals remain consistent throughout the decay, indicating selective lignin degradation without significant alteration to the polysaccharide matrix, which likely enhances biomass digestibility. Lastly, Fig. 10e shows work by Davis et al., who employed ¹³C CP-MAS to investigate fungal degradation (brown rots) of spruce wood, revealing selective carbohydrate removal and complex lignin transformations [119]. Enhanced aromatic signals (110–160 ppm) and diminished carbohydrate peaks (105.7, 63, and 84 ppm) highlight the preferential loss of hemicellulose, while new carbonyl peaks (220–165 ppm) indicate oxidative changes. The study highlights the presence of lignin modifications, including reduced methoxy groups, increased phenolic hydroxyls, and demethylation, without significant aromatic ring cleavage.

Advanced 1D: spectral editing techniques

Considering that lignocellulosic-derived materials are complex and heterogeneous multi-polymeric systems, characterization by ssNMR methods is somewhat limited due to spectral overlap and poor resolution. Spectral editing techniques can provide useful simplifications of complex spectra. In the following section, we introduce and review the more common methods by which selection and editing of ¹³C ssNMR spectra can be obtained.

Dynamics-based filtering

Relaxation and dynamics filtering methods are particularly useful for distinguishing mobile-like or rigid-like carbon environments. Generally, these techniques take advantage of differences in laboratory frame longitudinal (T₁) or transverse (T₂), or rotating frame (T_1ρ) relaxation of either the directly observed ¹³C nucleus or of the proton spins prior to CP. Molecular motion on either the nanosecond or microsecond timescales are relevant; spin–lattice relaxation in the laboratory frame (T₁) are maximally sensitive to molecular motion on the scale of the Larmor frequency (MHz timescale), while relaxation in the rotating frame (T_1ρ) is sensitive to motion near the spin-lock frequency, typically in the kHz regime [123].

Figure 11a shows the generalized T₁/T₂ curve as a function of the rotational correlation time. Generally, spin–lattice (T₁) relaxation undergoes a T₁ minimum in the intermediate dynamics regime when the correlation time is on the same order as the Larmor frequency (MHz, or nanosecond timescale). According to Ghosh and Dey, general “global” correlation times of cellulose are on the order of 1E-5 for amorphous or 1E-6 for crystalline environments [125]. Other studies on intact lignocellulosic materials suggest that the global dynamics of matrix polymers are of similar order as amorphous cellulose moieties [31, 34, 41]. At modern spectrometer frequencies of ~ 5–15 Tesla (200–600 MHz), these correlation times would place most if not all ¹³C environments within the lignified plant secondary wall on the ‘solid-side’ of the T₁ curve. Thus, as the molecular motion increases, we typically observe a decrease in the T₁ spin–lattice relaxation times (faster relaxation) owing to an increase in the molecular motion at or near the Lamour frequency. DP-MAS methods are capable of observing all carbon types regardless of molecular motion, but polymeric carbon environments with relatively increased molecular motion are therefore emphasized using a fast recycle delays, since slowly relaxing sites (e.g., rigid cellulose) become saturated and therefore under-represented in the resulting ¹³C spectra. With this concept in mind, contrasting CP-MAS and DP-MAS results with varying CP contact times and/or recycle delay times is one of the more simple but powerful ways to directly emphasize rigid vs mobile carbon environments. Fast-recycle delay DP-MAS spectra can be compared to CP-based methods to, respectively, compare more mobile-like and rigid-like environments within the material, as demonstrated on water-hydrated switchgrass in Fig. 11a.

Also leveraging differences in ¹³C T₁ relaxation times, Sparrman et al. developed a revised ¹³C solid-state NMR method for determining cellulose crystallinity [126]. Since crystalline and amorphous cellulose differ substantially in molecular mobility—and consequently in their ¹³C T₁ relaxation rates—the authors applied a T₁ filter following initial CP polarization. Using either inversion-recovery or saturation-recovery techniques, they selectively enhanced the crystalline cellulose subspectra. By integrating these filtered spectra and applying a simple equation, this method provided reliable and quantitative estimates of cellulose crystallinity.

Proton spin relaxation editing (PSRE)

Another form of dynamics-based filtering is proton spin relaxation editing (PSRE) ssNMR, a class of spectral editing techniques designed to filter the ¹³C CP-MAS profile based on differing relaxation rates (T₁, T_1ρ, T₂) of the associated protons [127]. The general idea is to select particular carbon types based on the relaxation properties of the proton bath prior to ¹H–¹³C cross-polarization. The first implementation of PSRE was by Zumbulyadis et al. to remove background signal from the rotor using a T₁(H) filter [127]. Here, a ¹H 180 inversion pulse is first applied followed by a carefully timed recovery delay such that ¹H signal associated the rotor is nulled while desired signal remains inverted. A ¹H hard pulse is then applied to rotate desired magnetization into the x–y plane for cross-polarization and ¹³C detection (Fig. 11b, ¹³C-detected ¹H T₁ by inversion recovery). Newman then showed that this methodology could be used to isolate the ¹³C spectra of heterogeneous systems based on differing T₁(H) relaxation times [128]. It is important to note here that strong dipolar coupling between protons leads to efficient spin-diffusion, manifesting as ¹H relaxation time averaging for spatially proximal domains (e.g., 30–50 nm for ¹H T₁). Thus, the separation of the ¹³C spectra by ¹H T₁ relaxation requires large phase-separated domains. The requirement of large domain sizes has restricted application of T₁(H) filtering on lignocellulosic materials, since these systems are more homogeneous on the nanoscale based on advanced ¹³C ssNMR results [40, 41]. However, Preston et al. showed that PSRE with T₁(H) filtering prior to ¹³C CP-MAS was capable of distinguishing decomposed (faster ¹H relaxation) and undecomposed (slower ¹H relaxation) wood chips, suggesting PSRE T₁(H) filtering may be useful for monitoring spatially heterogeneous decomposition [129].

Rotating frame (T_1ρ) relaxation filtering has proven more useful. At common (5–15 kHz MAS) spinning speeds ¹H T_1ρ is also sensitive to spin-diffusion and therefore averaging of relaxation times, but the length scale of spin-diffusion over the millisecond timescale typical of T_1ρ measurements is roughly 2–3 nm or less [130], much smaller than for T₁(H). Thus, selection of ¹³C signals is achievable through T_1ρ(H) relaxation filtering if domain sizes are on the order of 2 nm or larger. Averaging or partial averaging of ¹H T_1ρ between unique carbon environments is indicative of generally homogeneous polymer mixing [131]. If spin-diffusion effects are not desired, for example if site-specific T_1ρ filtering is intended, higher spinning speeds can be utilized to quench spin-diffusion and thereby T_1ρ averaging [132]. Spin diffusion can also be suppressed by collecting the experiment under the Lee–Goldburg condition.

After initial ¹H polarization, T_1ρ(H) filtering is accomplished by adding a sufficiently long (~ 2–20 ms) ¹H spin-locking period prior to cross-polarization. ¹³C signal associated with longer ¹H T_1ρ is retained while environments with shorter T_1ρ may be severely attenuated or lost. Rotating frame relaxation is maximally sensitive to molecular motion at the spin-lock frequency, which is typically on the order of 50–100 kHz. For example, water-plasticized hemicellulose and associated domains may experience increased cooperative polymeric motion relative to cellulose on the kHz timescales and therefore faster T_1ρ(H) relaxation, while lower-amplitude kHz cooperative motion for crystalline cellulose is reflected in longer T_1ρ(H).

For this reason, a common implementation of T_1ρ(H) filtering is to obtain cleaner cellulose subspectra of intact lignocellulose. This relates to our prior comments on interpretation of 1D ¹³C CP-MAS spectra of intact cellulose; correct interpretation of cellulose morphology (e.g., crystalline vs amorphous, or interior vs surface, or tg vs gt/gg) by ssNMR for intact cellulose is hindered by spectral overlap of cellulose signals with matrix polymers, especially in the ~ 84 ppm region. In an early implementation applied to various lignocellulose sources, Newman noted T_1ρ(H) filtering of ¹³C spectra was key in estimating the weight-averaged lateral dimensions of cellulose crystallites by quantifying the crystalline/interior to amorphous/surface ratio [133]. It is important to note that by using T_1ρ(H) filtering it is possible to overcome spectral overlap issues and estimate cellulose crystallinity for intact lignocellulose. Most notably, Walker used T_1ρ(H) filtering of CP-MAS spectra to reveal cellulose crystallinity details of raw and pretreated lignocellulose from three bioenergy feedstock candidates sorghum, switchgrass and poplar wood [124]. These ssNMR-derived metrics were capable of predicting enzymatic sugar yields achieved by hydrolysis of residual cellulose, regardless of biomass type or organic pretreatment solvent used; an increase in measured cellulose crystallinity was correlated with an increase in scarification yield (Fig. 11c). The key, the authors argue, is that an increase in cellulose crystallinity measured by T_1ρ(H) filtered ¹³C CP-MAS spectra is due to a decrease in the interactions with hemicellulose/lignin matrix at the microfibril surface. This finding is intriguing and possibly counterintuitive since amorphous cellulose is known to hydrolyze more easily than the crystalline allomorph [134]. Other attempts to correlate NMR-derived cellulose crystallinity estimates from isolated cellulose with enzymatic degradation efficiency have been unconvincing [135,136,137]. The discrepancy may be attributed to structural changes to cellulose during the isolation process; water has been shown to promote recrystallization of ball-milled cellulose faster than hydrolysis [138], and also pore structure collapse may restrict enzymatic access. Walker’s results suggest that cellulose crystallinity estimates by T_1ρ filtered ¹³C ssNMR on intact, never-dried material could be a suitable predictor of enzymatic hydrolysis efficiency.

Dipolar dephasing

Another common form of spectral editing is dipolar dephasing, which can differentiate between rigid protonated carbons and non-pronated carbons or protonated but mobile carbons. The dipolar dephasing (DD) experiment, first implemented by Opella and Frey [143], is designed to filter complex spectra based on differences in ¹H–¹³C dipolar interaction. The strength of the ¹H–¹³C dipolar coupling is foremost directly related to the inter-nuclear distance and therefore highly sensitive to if the carbon nucleus is protonated or non-protonated. Implementation of the DD method consists of initial ¹³C polarization by direct or CP methods followed by a short (40–100 μs) period during which no ¹H decoupling is performed (Fig. 12a). This leads to rapid dephasing of ¹³C signal with strong dipolar coupling to protons (i.e., rigid protonated sites) while signal from non-protonated carbons survives. In addition, since the dipolar term can be averaged by high molecular motion the technique is sensitive to local dynamics of the ¹H–¹³C bond vector; protonated carbon sites with sufficiently fast molecular motion may survive a dephasing period. Protonated methyl groups, which undergo fast three-site hopping at normal temperatures, are therefore also typically observed. As a result, dipolar-dephased ¹³C spectra filters out rigid non-methyl protonated carbons while retaining non-protonated and dynamic carbons. To view primarily the rigid protonated carbon types one can take a difference spectrum between a standard CP spectrum and the DD spectrum.

There are several examples where dipolar dephasing is utilized to understand lignocellulose and its deconstruction. One of the first implementations was by Gamble et al., in which CP-MAS and dipolar dephasing were applied on Bermuda grass treated with differing lignin-degrading white-rot fungi (Fig. 12b) [139]. It was shown that P. chrysosporium targets polysaccharides and to a lesser degree phenolic components, while C. subvermispora more substantially removed phenolic components over polysaccharides, and further suggested C. subvermispora shows a preference for guaiacyl over syringyl units. In another early example, dipolar dephasing was used to highlight non-protonated carbons in carbonaceous materials including lignin as well as other natural organic matter like charcoal and humic acids [140]. In this work it was also shown that long dipolar dephasing delays can help identify large fused-ring systems, since large fused aromatic rings are devoid of protons so long dephasing times are required to recouple the ¹H–¹³C dipolar interaction (Fig. 12c). Dipolar dephasing combined with TOSS was used to better characterize native and milled pine, with dipolar-dephased spectra showing improved clarity over routine CP. In particular, the authors emphasized that dipolar-dephased spectra helped distinguish between ketone (C=O) and aldehyde (H–C=O) units downfield of 170 ppm since as protonated C=O aldehydes are suppressed while ketones are less affected (Fig. 12d) [141]. Results showed that milling increased the concentration of carbonyl groups, particularly ketones, by breaking lignin’s ether bonds and generating new oxidated structures. Rezende used dipolar dephasing to aid in spectral assignments of the lyophilized hydrolysate after alkaline pretreatment of sugarcane bagasse [142]. Along with routine CP-TOSS spectra, DD results suggested that it is primarily lignin and minor cellulose that is removed on 4% NaOH pretreatment (Fig. 12e).

Rezende et al. also employed the CSA filter on the CPMAS-TOSS spectrum of hydrolysates from NaOH-pretreated sugarcane bagasse to selectively eliminate signals from cellulose and non-aromatic lignin carbons, thereby confirming the removal of cellulose during pretreatment (Fig. 12e) [142]. The CSA filter in solid-state NMR exploits the angular dependence of the CSA tensor to selectively enhance signals from nuclei with pronounced anisotropic shielding, such as sp² carbons or aromatic systems. By combining specific pulse sequences, such as dipolar decoupling and rotor-synchronized recoupling, the CSA filter isolates the anisotropic contribution while suppressing signals from nuclei in isotropic environments, such as sp³ carbons (Fig. 12a). This approach is invaluable for probing the molecular orientation, electronic environments, and the structural anisotropy of rigid and crystalline systems, providing a deeper understanding of local electronic distributions and bonding geometries. This approach highlighted that the remaining lignin peaks were associated with high chemical shift anisotropy, particularly from aromatic and methoxyl carbons, whereas cellulose-related signals were effectively filtered out, validating the spectral assignments. The application of the CSA filter provided useful insights into the structural changes and removal of both cellulose and lignin during the alkaline pretreatment of the bagasse samples.

Furthermore, corn stover residuals were characterized throughout dilute acid pretreatment (DAP) and enzymatic conversion process with advanced ssNMR methods, including dipolar dephasing and other spectral editing tools [144]. The authors come to some possible explanations for limitations of biological conversion of corn stover; chemical modification of polysaccharides, accumulation of cinnamic acids in residuals, close association of carbohydrates and lignin, and adsorption of proteinaceous or other nitrogen-containing material. This work serves as an excellent case study for applying advanced ssNMR techniques at natural abundance to better understand the lignocellulosic conversion process.

Exploring 2D and pseudo-2D ssNMR at natural abundance

In the previous sections we overviewed one-dimensional ssNMR methods, and in particular routine 1D DP-MAS, CP-MAS, and spectral editing and dynamics filtering methods designed to simply complex ssNMR spectra. Sans isotopic enrichment or access to DNP hardware, multi-dimensional solid-state NMR methods are more challenging, especially for heterogeneous polymer mixtures. ¹³C–¹³C correlation experiments such as Proton Driven Spin Diffusion (PDSD) and J-Refocused INADEQUATE techniques are powerful tools for ¹³C-enriched lignocellulose, but they are all but out of the question at natural abundance. Nevertheless, the characterization of biomass by solid-state NMR is not restricted to 1D techniques. Here, we refer to two-dimensional (2D) methods as both pseudo-2D, where the second indirect dimension is directly sampled time information, and true 2D, where frequency-encoded spectral information is obtained after Fourier transform of the indirect dimension.

Psuedo-2D relaxation: proton relaxation measurements and sensitivity to domain mixing

Relaxation times obtained by solid-state NMR are particularly useful for probing dynamical phenomena as well as providing a window into domain mixing on the nanoscale. Many of the previously mentioned 1D filtering methods become pseudo-2D if multiple time-points (tau delays) are collected such that the second dimension is directly sampled time information. As a reminder, spin–lattice (T₁) relaxation is maximally sensitive to molecular tumbling on the order of the Larmor frequency (MHz regime), while rotating frame (T_1ρ) relaxation rates are maximally sensitive to molecular motions on the order of the spin-locking frequency, typically on the order of 50 kHz [123]. ¹H T₁ rates for rigid organic solids are usually measured indirectly via ¹³C through the inversion recovery tactic by varying the recovery delay τ from short to long, and ¹H T_1ρ can be measured by varying the spin-locking pulse length (Fig. 11b). Fitting the signal intensity versus time to a known exponential function yields the desired information. Importantly, ¹H relaxation properties in the solid state are sensitive to proton spin diffusion resulting in an averaging of ¹H relaxation times, and thus only domain-averaged information is usually accessible. Since ¹H T₁ times are typically on the order of seconds, and proton spin diffusion rate constants are on the order of 0.8 nm²/ms for rigid polymer systems [145], ¹H T₁ relaxation rates are expected to be averaged for homogeneous systems with domain sizes much smaller than ~ 30–50 nm. This can be both a feature and a bug. While site-specific ¹H relaxation rates are not often obtainable, domain sizes and polymeric interactions can be loosely investigated through an understanding of ¹H relaxation time averaging. For example, Ahvazi measured the ¹H T₁ for isolated lignin and bleached cellulose from cuoxam as a function of pH. The results showed very different T₁(H) vs pH curves for cellulose and for lignin when isolated, but similar curves for the same lignin and cellulose signals within native wood, suggesting relaxation averaging due to polymer proximity [146]. Similarly, by comparing ¹H T₁ relaxation rates of native and denatured Japanese cypress wood in both the humid and heat-dried states, Nishida demonstrated that removal of lignin (and to a lesser degree hemicellulose) results in increased cellulose T₁(H) times [147]. This observation is explained if lignin acts as the primary ¹H relaxation sink for cellulose protons thanks to proton spin diffusion between the polymers. T₁(H) analysis performed on native and steam-treated bamboo also showed a decrease in ¹H T₁ values with increasing temperature and treatment times, consistent with an increase in smaller and more dynamic pyrolysis products [108]. Proton T₁ relaxation measurements have also demonstrated an increase on molecular mobilities of sulfonated and methylated softwood pulps when the sodium salt was used but the opposite for when the calcium cation was used [148]. As a cautionary note, it is clear that water hydration has a marked effect on ¹H relaxation times [149], therefore moisture state should be reported if directly interpreting T₁(H) for characterizing lignocellulose and derived materials.

Recall that spin–lattice relaxation times in the rotating frame (¹H T_1ρ) are in principle sensitive to molecular motions on the kilohertz timescales, with faster T_1ρ generally associated with increased dynamics for lignocellulose. To highlight this phenomenon, ¹H T_1ρ analysis of relatively dry and hydrated onion primary cell walls revealed a substantial decrease in T_1ρ for pectic sites but a minor decrease in T_1ρ for cellulose sites upon hydration [150]. These results indicate water penetrates and plasticizes pectin to a much greater degree than cellulose. Also, T_1ρ measurements help distinguish between Norway spruce (Picea abies) wood grown in three different geological regions. Samples from Finland showed more uniform and lower T_1ρ values compared to those from Poland followed by Italy, and the authors suggested increased polymer mobility in the same order. Further, principal component analysis (PCA) was able to distinguish between the plot sights based on T_1ρ rates [151].

Polymer mixing and spin-diffusion also play a role in rotating frame relaxation time averaging but on a much shorter length scale in the range of ~ 2 nm [130, 145]. Considering that polymers within lignocellulose are known to have sub-nanometer spatial interactions [30, 34, 40, 41], one must interpret T_1ρ results from intact lignocellulose cautiously and expect some degree of averaging. For example, Newman et al. [152] showed similar T_1p for crystalline and amorphous cellulose, and of hemicelluloses with lignin, suggesting intimate close-range interactions of the carbon types. T_1p averaging was captured more indirectly by Leroy et al. [153] who studied the effects of hot water pretreatment (HWP) on maize stems and showed that both enzymatic saccharification efficiency and ¹H T_1ρ relaxation times were positively correlated with increased pretreatment time. Since for lignocellulose ¹H T_1ρ generally will increase with rigidity, at first it may seem counterintuitive that an increase in ¹H T_1ρ would be associated with increased enzymatic saccharification. But as matrix polymers are increasingly removed with pretreatment, ¹H T_1ρ averaging between more-rigid cellulose and less-rigid matrix polymers is reduced, thus increasing the observed ¹H T_1ρ of cellulose. Therefore, for intact lignocellulose an increase in ¹H T_1ρ may serve as a proxy for increased cellulose accessibility.

Relaxation: site-specific dynamics of lignocellulose

To obtain site-specific dynamics on the kilohertz timescales via ¹H T_1ρ, spin-diffusion effects must be quenched or absent. This is most commonly achieved using the Lee–Goldburg condition; by applying an LG spin-lock field instead of a standard spinlock to the ¹H channel before the CP step. LG-CP is then used to transfer the ¹H polarization to ¹³C in a site-specific manner. This approach ensures selective polarization transfer, so that ¹H magnetization is efficiently relayed to directly bonded ¹³C nuclei, minimizing contributions from distant or weakly coupled spins. While we have not identified any examples of ¹H T_1ρ measurements applied to lignocellulose using the LG condition at natural isotopic abundance, its utility to ¹³C-enriched materials has proven enlightening [154, 155]. For example, in one of the earlier instances of advanced ssNMR of ¹³C-enriched plant materials, Dick-Perez and co-workers demonstrated that site-specific ¹H T_1ρ relaxation measurements reveal distinct dynamics of pectic polysaccharides in Arabidopsis thaliana primary cell walls [155]. Among the many findings from advanced ssNMR techniques, dynamics measurements highlighted the high mobility of pectins relative to cellulose and hemicellulose, and further demonstrated how depectination reduces molecular flexibility and increases polysaccharide packing density. Such techniques could be applicable at natural isotopic abundance.

Site-specific dynamics in the nanosecond/MHz timescales are also accessible through ¹³C since at natural abundance the opportunity for ¹³C–¹³C spin-diffusion and averaging is absent. Due to typically longer ¹³C spin–lattice relaxation rates compared to protons, measurement of ¹³C T₁ by direct methods like saturation recovery or inversion recovery are often untenable. ¹³C spin–lattice relaxation times are therefore measured most commonly using the method developed by Torchia (Fig. 13a) [156], which uses ¹H–¹³C CP for initial polarization along with phase cycling to achieve ¹³C signal decay based on ¹³C T₁, but the repetition rate (time between scans) is dictated by the much faster proton relaxation.

Here, we describe some key examples wherein ¹³C T₁ rates are utilized to understand lignocellulose and its deconstruction at natural abundance. Ghosh used site-specific ¹³C T₁ measurements on untreated cellulose from rice straw and sugarcane bagasse, and on their residuals post-enzymatic digestion [157]. The results suggested an overall decrease in ¹³C T₁ times for all carbon types post digestion, pointing to an increase in disorder and possibly reduction in polymeric size due to enzymatic activity. Since the ratios of crystalline-like and amorphous-like cellulose appear unaltered, the authors argue that tracking ¹³C relaxation throughout lignocellulose deconstruction may be a more effective way of tracking changes in the cellulose structure than routine 1D ¹³C ssNMR. Martin and co-workers studied the chemical and ultrastructural effects of lignin-modified rice cell walls [158, 159]. For rice deficient in cinnamyl alcohol dehydrogenase (CAD2), site-specific ¹³C T₁ relaxation times suggested a complex alteration of cellulose mobility and dynamics for the cad2 mutant compared to wildtype (Fig. 13b). Specifically, biexponential T₁ analysis was applied to extract faster- and slower-relaxation components, and overall longer T₁ relaxation was observed for mutant lines but also an increase in the relative fraction of faster-relaxing component was increased [158]. The authors propose that a chemical alteration of lignin via CAD deficiency has downstream mobility effects on cellulose due to an altered lignin–hemi-cellulose interplay. In a follow-up study, the authors added additional lignin-modified rice lines including 5-hydroxyconiferaldehyde O-methyltransferase (CAldOMT) and stacked CAldOMT1/CAD2 deficient lines, and showed via ¹³C T₁ measurements that CAldOMT deficient and stacked CAldOMT/CAD deficient lines had an increase in cellulose mobility (reduced ¹³C T₁) compared to wildtype [159]. Both CAldOMT1 and CAD2 lines demonstrated substantially enhanced enzymatic saccharification efficiencies relative to wildtype, although CAldOMT1 lines outperformed CAD2, suggesting a possible relationship between cellulose mobility and scarification efficiency. Other examples of applying ¹³C T₁ analysis for understanding native and altered lignocellulose at natural abundance include probing the dynamic effects of dilute sulfuric acid pretreatment on poplar wood [160, 161], native and denatured bamboo [162], understanding water accessibility to cellulose [163], understanding of cellulose crystallinity [126], and using ¹³C T₁ as a source of variability for multivariate analysis [164], to name a few.

Leveraging T₂ relaxation for water proximity and domain sizes

The ¹H T₂ relaxation times for water and biomass vary significantly, with biomass protons showing much shorter ¹H T₂ times due to its more rigid and heterogeneous environment. By exploiting this difference, it is possible to selectively suppress the proton bath from biomass, leaving the initial magnetization primarily in water. If a ¹H spin-diffusion period is included after ¹H T₂ filter, one can transfer ¹H magnetization from water molecules to neighboring biomass components, then observe ¹³C signal after CP (Fig. 13c). Short t_SD periods will highlight the carbon types of polymeric units immediately proximal to water. This approach can be used to probe the “hydration level” by mapping how closely water is associated with different biomass domains based on proximity-dependent polarization transfer [165,166,167]. As the spin diffusion period t_SD increases, polarization is then transferred to adjacent, less dynamic domains, enabling the identification of molecular domains based on their spatial proximity and mobility. This is demonstrated in Fig. 13d for water-hydrated switchgrass at natural isotopic abundance: as the spin diffusion period is increased, magnetization that originated on from water penetrates first into water-hydrated regions and then into water-deficient areas, like cellulose microfibrils, and spin-diffusion buildup curves are obtained. This method provides insights into the microstructural organization and relative sizes of different regions within biomaterials [168].

Variable contact time CP-MAS

The variable contact time (VCT) experiment represents a very basic pseudo-2D approach in ssNMR, in which the time-dependent dynamics of the polarization transfer from abundant nuclei like ¹H to less sensitive ones such as ¹³C is monitored. Variation of the contact time in a CP pulse sequence results in a buildup then decay of ¹³C signals corresponding, respectively, to the T_CH constant and T_1ρ(H) rotating frame relaxation time for each resolved carbon. Interpretation of both T_CH and T_1ρ can yield important insights into the efficiency of polarization transfer, the strength of dipolar couplings, inter-nuclear distances, and local molecular dynamics. The shorter the T_CH, the stronger the dipolar interactions and the more effective the transfer [169]. Kobayashi et al. used T_CH measurements to show that ether linkages between syringyl and guaiacyl units are broken and new linkages formed, reflecting the recondensation of lignin, especially for dilute H₂SO₄ treatment at higher temperatures for powdered poplar wood [161]. Their data revealed a small rise in T_CH values with time at the 120 and 130 °C treatments, indicating a slight increase in atomic-level mobility. In contrast, the treatments at 140 and 150 °C revealed a slight decrease in T_CH values with time that reflected reduced atomic-level mobility during these conditions. In a study by Santoni et al. [151] T_CH measurements were applied to untreated wood of Picea abies from Finland, Poland, and Italy. The study found that the chemical and physical properties of the wood varied by provenance, allowing classification based on growth conditions. Although no consistent trend in T_CH values was identified across the wood samples, origin-dependent differences in T_1ρ(H) relaxation times were observed. Specifically, the Finnish samples exhibited shorter T_1ρ(H) times, possibly indicating more dynamic molecular structures, which may reflect the local climatic conditions and environmental influences on wood properties.

Detect strength of heteronuclear dipolar couplings

Another valuable pseudo-2D technique for elucidating the motional amplitudes of lignocellulose, measured using motionally averaged ¹³C–¹H dipolar couplings, is the dipolar DIPSHIFT experiment (Fig. 13e) [170]. The ¹H–¹³C DIPSHIFT is a type of separated local field (SLF) ssNMR experiment aimed at correlating chemical shifts with the strength of the heteronuclear dipolar coupling interactions [171, 172]. In this experiment, the initial ¹³C magnetization is generated via cross-polarization, followed by a variable evolution time t₁ during which the ¹³C magnetization evolves under the ¹H–¹³C dipolar interaction. Throughout this period, ¹H–¹H homonuclear interactions are eliminated using homonuclear decoupling sequences such as frequency-switched Lee–Goldburg (FSLG) and MREV-8 [173, 174]. The chemical shift is then refocused using a π pulse placed after one rotor period; as a result, the ¹³C spins evolve exclusively under the ¹H–¹³C dipolar coupling. The resulting spectrum displays ¹³C isotropic chemical shifts in the direct dimension and ¹H–¹³C dipolar dephasing curves in the indirect dimension, which can be fitted to extract the apparent ¹H–¹³C dipolar couplings. Dipolar curves are generated for one rotor period, and the strength of the ¹H–¹³C coupling is reflected in the modulation depth of the curve. In fully rigid systems, the strength of the ¹H–¹³C coupling remains unaffected by molecular averaging, leading to the maximum modulation depth of the DIPSHIFT curve at half the rotor period. However, when dynamic processes emerge in the intermediate regime, the effective ¹H–¹³C coupling interaction diminishes, resulting in reduced depth of the DIPSHIFT curve. By comparing the observed dipolar coupling strengths to those from a fully rigid system (23 kHz for a rigid 1.1 Å aliphatic C–H bond), an order parameter S can be derived, representing the degree of reduction in dipolar coupling due to intermediate molecular motion. The apparent coupling is then divided by the scaling factors associated with the homonuclear decoupling sequences—0.577 for FSLG and 0.47 for MREV-8—to obtain the true coupling.

Zhao et al. demonstrated the utility of dynamic NMR studies on unlabeled rice (Oryza sativa) materials, rich in cellulose, hemicelluloses (primarily xylan), and lignin, to assess their native states [47]. Their work effectively combined room-temperature measurements of polymer dynamics with DNP-enhanced natural-abundance 2D ¹³C-¹³C correlation experiments, facilitating efficient screening of a diverse array of lignocellulosic materials found in nature or engineered in vitro. Employing the DIPSHIFT experiment, they investigated the CH dipolar coupling strengths between wild-type rice and double mutants of the homolog CTL2, which is implicated in cellulose biosynthesis regulation. Notably, their data revealed that all polysaccharides exhibited high rigidity, characterized by large order parameters exceeding 0.90 (Fig. 14a).

2D (¹³C–¹H) wideline separation (WISE)

Another form of separated local field ssNMR technique is 2D wide-line separation (WISE) (Fig. 13e) [145, 179], which is designed to correlate biopolymer structure/conformation in the direct dimension (¹³C chemical shift) with mobility and dynamics via the ¹H linewidth in the indirect dimension. The ¹H wideline spectrum is a reflection of ¹H–¹H dipolar couplings of the protons in the proximity of the observed ¹³C site, and thus is a proxy for molecular dynamics. A broad (~ 50–70 kHz FWHM) ¹H profile signifies a rigid proton system with strong homonuclear dipolar couplings, while narrowing of the ¹H wideline spectrum reveals averaging of the dipolar coupling network due to segmental motion. Assuming site-specific information is requested, short cross-polarization contact times are usually employed to select ¹H–¹³C spin pairs and limit the effects of spin-diffusion and averaging of dynamics environments [123]. 2D ¹³C–¹H WISE spectra (without spin-diffusion) on native poplar wood was compared to poplar wood/silica composites prepared under basic and acidic conditions [175]. ¹H wideline traces associated with bulk structural polysaccharides (73 ppm) revealed broad ~ 62 kHz profiles for native and wood–silica composites prepared under basic conditions, but under acidic conditions a narrow ~ 10 kHz FWHM component is superimposed with the broad profile (Fig. 14b). This observation of a subfraction of dynamic cellulose in the acid-prepared wood/silica composite material was correlated with a removal of lignin as observed by CP-MAS, and was attributed to a partial disruption of cellulose and better penetration of silica precursors into the cell wall interior. 2D WISE was also used to investigate ¹H lineshape changes to Whatman filter paper cellulose under differing moisture states. Results showed narrowing of the ¹H profiles for amorphous/surface cellulose signals (84 ppm) with increased humidity, but no narrowing is observed for crystalline/interior signals (89 ppm) [149].

A proton spin-diffusion element added to the 2D WISE experiment allows one to monitor the time-dependent equilibration of phase-separated ¹H spin baths of differing dynamics, which can yield morphology or domain-size information [145]. For example, Capitani et al. [176] used 2D WISE with spin-diffusion to reveal the spatial distribution of nanoporous water in paper cellulose. The authors leverage the dynamics difference between water and cellulose; protons from water have a narrow ¹H spectrum of ~ 1.5 kHz while protons associated with cellulose are ~ 62 kHz FWHM (Fig. 14c). At short spin-diffusion mixing times the narrow ¹H profile of water is not visible in the WISE spectrum at ¹³C sites associated with cellulose, which instead show characteristically broad ¹H wideline profiles. But as the mixing time increases, ¹H magnetization from the mobile water regions transfers to the cellulose and is readable as a narrow ¹H component in the 2D WISE spectrum. Tracking the time-dependent equilibration of mobile and rigid proton spin baths ultimately led to an estimate of ~ 3 nm separation between crystallite cellulose cores within a water-rich amorphous matrix. 2D WISE has also used to investigate spatial proximities and local dynamics of suberin in potato skins [180, 181], cutin in tomato skins [182], and to probe hydration effects of polysaccharides within onion primary cell walls [150].

2D (¹³C–¹H) heteronuclear correlation (HETCOR)

Arguably the typical NMR method for whole biomass analysis is whole-cell gel-state HSQC NMR pioneered by the John Ralph lab, in which biomass is pulverized (ball-milled) and swollen in a plasticizing NMR solvent like d6-DMSO and analyzed by HSQC NMR to reveal directly bonded ¹H–¹³C pairs [65]. Although the resolution-enhancement is impressive, limitations include only protonated carbons detected, physical modifications to the cell wall caused by mechanical milling, sample heating, as well as issues like heterogeneous solvent penetration, leaving room for ¹H–¹³C correlation experiments of unaltered materials in the solid state. In the native solid-state however, observing correlation of ¹³C with associated ¹H nuclei is severely hindered by strong homonuclear dipolar couplings in a rigid proton network. This is exemplified in the above-discussed 2D WISE experiment, which correlates ¹³C with associated protons but chemical shift information is impossible to interpret due to the breadth of the ¹H profile. One can partially overcome these issues by implementing homonuclear decoupling methods like FLSG decoupling where during ¹H chemical shift evolution ¹H–¹H homonuclear dipole–dipole interactions are thereby suppressed (Fig. 13e) [173, 183]. This is the basis of the 2D ¹H–¹³C FSLG-HETCOR, which correlates the chemical shifts of ¹³C with proximal ¹H in the solid state [184]. Like the 2D WISE experiment, 2D ¹H–¹³C HETCOR can be collected without spin-diffusion for site-specific information or with an added ¹H spin-diffusion block to probe the spatial proximities of differing environments.

In the same study highlighted previously for spectral editing ssNMR tools, Mao and colleagues applied ¹H–¹³C FSLG-HETCOR on corn stover residuals after DAP and enzymatic hydrolysis [144]. These data revealed that cellulose and/or hemicellulose within the corn stover residue after conversion is tightly associated with lignin, as evident by the observed correlation between ¹³C of 89 and 105 ppm with aromatic protons in the 7 ppm range [144]. Adding a dipolar dephasing element to the 2D HETCOR also helped correlate non-trivial unprotonated aromatic signals with nearby non-bonded protons (Fig. 14d).

In a related study, 2D ¹H–¹³C HETCOR was used to compare residual enzymatic lignin (REL) and traditional milled wood lignin (MWL) from Loblolly pine [185]. Here, a ¹H spin-diffusion element was included in the 2D HETCOR, which revealed REL is more heterogeneous compared to MWL. In a unique example, 2D FSLG-HETCOR ssNMR was used to show resin/lignin interactions within a roman warship wooden archeological artifact after consolidation (Fig. 14e) [177]. In particular, ¹H–¹³C correlations were observed between Kauramin carbons and lignin aromatic protons, providing direct evidence of pi–pi stacking between lignin and the triazine ring of the Kauramin binder. 2D ¹H–¹³C FSLG HETCOR ssNMR is also highlighted as a key tool for characterizing charred organic materials and revealing the mechanisms at play during biochar production, in particular because solid materials are analyzed in their unaltered state without any sample preparation needs (Fig. 14f) [178, 186, 187]. Brech et al. demonstrated that below 300 °C, xylan is the primary source of aromatic compounds, whereas above this temperature, cellulose contributes to aromatization in silvergrass (Miscanthus). This transition is indicated by the cross peaks observed at 173–172/3.7–3.1 ppm in ¹H–¹³C HETCOR, suggesting interactions between lignin and carbohydrates within the biomass network. For an expanded reading on utilizing 2D HETCOR for understanding cellulose-based pyrolysis residues, please see the tutorial article by Knicker.

Future directions: fast MAS, ¹H detection, dynamic nuclear polarization (DNP)

On the bleeding edge of natural abundant ssNMR capabilities include ultrafast MAS and DNP, both requiring specialized hardware. Ultrafast MAS ssNMR improves the spectral resolution by spinning samples with high frequencies (40–150 kHz), which effectively averages out anisotropic interactions such as dipolar couplings and chemical shift anisotropy [188]. In that way, proton–proton dipolar couplings—the major source of broadening—are eliminated (Fig. 15a), improving sensitivity and clarity, especially for the low-abundance nuclei like ¹³C and ¹⁵N. Fast MAS also suppresses the broadening due to quadrupolar interactions in nuclei such as ¹⁴N and ¹⁷O, making this technique particularly suitable for high-resolution work on biomass, proteins, nucleic acids, and materials. This allows much better quantification and structural work in natural abundance samples, although this comes with serious drawbacks of reduced sample volume from smaller rotors. Direct measurement of ¹H spectra from rigid organic solids also enables ¹H-detected 2D correlation experiments which bring substantially improved sensitivity. In a recent first, Yuan et al. utilized ultrafast MAS at 150 kHz combined with high magnetic field (800 MHz) NMR to investigate spruce and maple wood as well as cotton cellulose [105, 187]. This approach enabled ¹H-detected 2D ¹H–¹³C HETCOR experiments at natural abundance for a detailed analysis of cellulose conformations in native and heat-treated wood samples (Fig. 15b). The improved resolution, particularly in the ¹H dimension, distinctly revealed three cellulose conformations assigned to tg, gt, and gg. Results demonstrated that softwoods like spruce exhibit higher packing order and crystallinity compared to hardwoods like maple. Furthermore, thermal treatments were shown to result in hemicellulose loss, increased cellulose disorder, and greater packing heterogeneity. As ultrafast MAS hardware becomes more prevalent, similar applications to understanding lignocellulose and its deconstruction can be expected.

DNP has revolutionized NMR spectroscopy by significantly enhancing the sensitivity of measurements, especially for natural abundance biomass materials with inherently low signal intensities. By leveraging biradicals such as AMUPol and AsymPolPOk (Fig. 15c), DNP dramatically amplifies signal intensity, reducing acquisition times by orders of magnitude and substantially improving signal-to-noise ratios [190]. This enhancement makes DNP an indispensable tool for studying complex systems and heterogeneous materials. DNP is particularly well-suited for acquiring high-resolution ¹³C–¹³C correlation spectra, which are essential for unraveling structural details in challenging systems like lignocellulosic biomass. It enables a broad range of advanced NMR experiments, including single quantum coherence transfer methods such as PDSD, CORD, and DARR, as well as double quantum coherence transfer techniques like INADEQUATE, facilitating in-depth structural characterization with unparalleled efficiency. Innovative developments have further extended the utility of DNP beyond its conventional framework. A major breakthrough came from the use of a sorbitol-based glass matrix, which enables DNP experiments to be conducted at elevated temperatures above 200 K [191]. This advancement broadens the range of experimental conditions, providing enhanced flexibility and enabling studies under more biologically or industrially relevant conditions. Another milestone in DNP is the introduction of matrix-free protocols, which address longstanding challenges related to the homogeneity of traditional DNP matrices, typically mixtures of d₈-glycerol, D₂O, and H₂O. Zhao et al. demonstrated that a matrix-free approach using minimal D₂O achieved an unprecedented enhancement factor (ε) of 57 (Fig. 15d), corresponding to a timesaving of 3249-fold. This approach mitigates issues such as aggregation and phase separation of glass-forming solvents at cryogenic temperatures, ensuring superior sensitivity, resolution, and reproducibility. These advancements simplify the experimental setup while significantly lowering operational complexity and cost, making DNP a more accessible and versatile technique for studying lignocellulosic components. In another example, Sluiter et al. confirm selective starch removal in biomass, with DNP-enhanced ¹³C INADEQUATE showing the disappearance of starch signals (~ 100–102 ppm) while retaining characteristic cellulose peaks (89 and 105 ppm) (Fig. 15e) [116]. The observed 101–102 ppm single quantum and 176 ppm double quantum correlation matches threefold helical screw xylan, further validating the method’s accuracy in distinguishing cellulosic glucan from starch.

Conclusions and perspectives

Solid-state NMR has emerged as an indispensable tool for understanding the intricate molecular structure and dynamics of lignocellulosic biomass and related bioenergy materials. Its unique capability to provide non-destructive, high-resolution insights into the composition, morphology, and transformations of rigid solids at natural abundance sets it apart from conventional analytical techniques. Throughout this review we cover common and routine ssNMR methods in addition to more advanced techniques including spectral editing, accessing dynamics-based information, and select 2D ssNMR methods practical at natural abundance. Our goal is to provide fundamental information and highlight the application of ssNMR methods at natural abundance to understand lignocellulosic biomass composition, structure and conversion and for bioenergy and carbon management applications. Solid-state NMR data provide essential insights of lignocellulosic biomass that unite our fundamental understanding of plant cell wall synthesis and architecture, as well as its deconstruction, decay and conversion to solids and liquids relevant in the bioeconomy. Further, the frontier of using natural abundance ssNMR for applications in carbon management and a sustainable economy is immense. Conversion and deconstruction kinetics, biosynthesis processes, system-scale ecosystem dynamics among lignocellulose and microbes, as well as biomass and biochar durability related to long-term carbon sequestration and storage could benefit from the information obtainable by ssNMR.

A central theme of this review is the unparalleled advantage of ssNMR in maintaining the integrity of the sample during analysis, preserving its natural state and complex structure. This advantage is particularly crucial in bioenergy research, where understanding the native organization of plant cell walls, the interactions between cellulose, hemicellulose, and lignin, and the changes induced during biotic and abiotic conversion processes are key to improving efficiency. The ability to observe these materials in their unaltered state has revealed fundamental insights into the mechanisms of biomass deconstruction, guiding the development of more effective pretreatment and conversion strategies.

Despite its numerous advantages, solid-state NMR also presents notable challenges and limitations. One primary drawback is its inherently low sensitivity, particularly for nuclei like ¹³C at natural abundance, which necessitates long acquisition times and can limit throughput. Additionally, the cost and complexity of high-field ssNMR instrumentation, coupled with the need for specialized expertise in experimental setup and data interpretation, can be prohibitive for some research groups. Another limitation is the difficulty in resolving overlapping signals in complex mixtures, which can obscure detailed compositional or structural insights without the use of advanced methods such as multi-dimensional NMR or isotopic labeling. Finally, ssNMR data acquisition is resource-intensive, requiring significant time and computational efforts, which may constrain its routine application in large-scale or high-throughput studies. Addressing these challenges through technological advancements, such as faster spinning rates, DNP, and artificial intelligence-driven data analysis, could expand the accessibility and applicability of ssNMR.

In conclusion, ssNMR not only enhances our fundamental understanding of biomass, but also provides critical data to inform bioenergy solutions and carbon-efficient practices. As the global community intensifies efforts to achieve sustainable energy solutions, ssNMR stands poised to play a pivotal role. Its application extends beyond lignocellulosic biomass to include biochar, carbon sequestration materials, and other bio-derived polymers, underscoring its versatility in addressing carbon management challenges.

Availability of data and materials

The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Data is available in the manuscript and any data not provided is available upon request from the coauthors. No datasets were generated or analysed during the current study.

Abbreviations

ssNMR:: Solid-state nuclear magnetic resonance
1D:: One dimensional
2D:: Two dimensional
3D:: Three dimensional
CBP:: Consolidated bioprocessing
NREL:: National Renewable Energy Laboratory
DMR:: Deacetylation and mechanical refining
RCF:: Reductive catalytic fractionation
HPLC:: High-pressure liquid chromatography
GC:: Gas chromatography
FTIR:: Fourier transform infrared
UV–Vis:: Ultraviolet–visible
py-MBMS:: Pyrolysis-molecular beam mass spectroscopy
NMR:: Nuclear magnetic resonance
SEM:: Scanning electron microscopy
TEM:: Transmission electron microscopy
TGA:: Thermogravimetric analysis
DSC:: Differential scanning calorimetry
XRD:: X-ray diffraction
LAP:: Laboratory analytical procedures
µ :: Magnetic moment
µ ₀ :: Net magnetic moment
M ₀ :: Net magnetization
N :: Number of detectable spins
B ₀ :: External magnetic field
γ :: Gyromagnetic ratio
I :: Spin quantum number
DP:: Direct polarization
CP:: Cross-polarization
HH:: Hartmann–Hahn
VACP:: Variable-amplitude cross-polarization
RAMP-CP:: Ramped-amplitude cross-polarization sequence
DNP:: Dynamic nuclear polarization
MAS:: Magic angle spinning
rf:: Radiofrequency
B ₁ :: Field strength
J :: Scalar coupling
CS:: Chemical shielding
Q :: Quadrupolar interactions
σ :: Shielding constant
δ :: Chemical shifts
ppm:: Parts per million
TMS:: Tetramethylsilane
DSS:: Sodium trimethylsilyl propane sulfonate
TOSS:: TOtal Suppression of Spinning Sidebands
d1:: Recycle delay
FID:: Free induction decay
GAX:: Glucuronoarabinoxylan
GlcA:: Glucuronic acid
GX:: Glucuronoxylan
HG:: Homogalacturonan
RG:: Rhamnogalacturonan
G:: Guaiacyl
S:: Syringyl
H:: p-Hydroxyphenyl
TMSP:: Sodium-3-trimethylsilylpropionate
SB:: Sugarcane bagasse
AH:: Acid hydrolysis
HSQC:: Heteronuclear single quantum coherence
CrI:: Cellulose crystallinity index
CI:: Crystallinity index
ORNL:: Oak Ridge National Laboratory
ILs:: Ionic liquids
EimCl:: Ethylimidazolium chloride
HPAC:: Hydrogen peroxide-acetic acid
COSLIF:: Cellulose solvent- and organic solvent-based lignocellulose fractionation
TCS:: Total carbohydrate solubilization
HTT:: Heat treatment temperatures
CSA:: Chemical shift anisotropy
PSRE:: Proton Spin Relaxation Editing
LG:: Lee–Goldburg
kHz:: Kilo hertz
MHz:: Mega hertz
DD:: Dipolar dephasing
DAP:: Dilute acid pretreatment
PDSD:: Proton driven spin diffusion
PCA:: Principal component analysis
HWP:: Hot water pretreatment
CAD:: Cinnamyl alcohol dehydrogenase
DIPSHIFT:: Dipolar chemical shift correlation
WISE:: Wideline separation
HETCOR:: Heteronuclear correlation
SWG:: Switchgrass
VCT:: Variable contact time
SLF:: Separated local field
FSLG:: Frequency switched Lee–Goldburg
S :: Order parameter
FWHM:: Half width half maximum
REL:: Residual enzymatic lignin
MWL:: Milled wood lignin

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Acknowledgements

The authors thank Besiki Kazaishvili of NREL's Communications Team for assistance with Figure 1 illustration. The authors also thank Renee Happs and Mark Davis for insightful discussions and historical direction of NREL's NMR Facility.

Funding

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This material is based upon work supported by the Center for Bioenergy Innovation (CBI), U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number ERKP886. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Author information

Bennett Addison and Malitha C. Dickwella Widanage contributed equally to this work.

Authors and Affiliations

Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO, 80401, USA
Bennett Addison, Malitha C. Dickwella Widange & Anne E. Harman-Ware
Joint Institute for Biological Sciences, Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Yunqiao Pu & Arthur J. Ragauskas
Department of Chemical and Biomolecular Engineering, The University of Tennessee, Knoxville, TN, 37996-2200, USA
Arthur J. Ragauskas
Center for Renewable Carbon, The University of Tennessee Knoxville, Institute of Agriculture, Knoxville, TN, 37996, USA
Arthur J. Ragauskas

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Malitha C. Dickwella Widange
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Yunqiao Pu
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Arthur J. Ragauskas
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BA conceptualized the manuscript. BA and MCD wrote and edited the manuscript, prepared all figures, and provided data. YP provided specific sections, edited the full manuscript, and provided data. AEW and AR provided crucial review and contributed to the editing of all sections. All authors read and approved the final manuscript.

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Correspondence to Bennett Addison.

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Addison, B., Dickwella Widange, M.C., Pu, Y. et al. Solid-state NMR at natural isotopic abundance for bioenergy applications. Biotechnol. Biofuels Bioprod. 18, 46 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13068-025-02648-z

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Received: 21 February 2025
Accepted: 11 April 2025
Published: 28 April 2025
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13068-025-02648-z

Solid-state NMR at natural isotopic abundance for bioenergy applications

Abstract

Background

Main text

Fundamentals of solid-state NMR

Challenges of NMR of rigid solids

Key ssNMR techniques (MAS, CP, decoupling)

Biopolymers of lignocellulose and related bioenergy materials

Routine 1D 13C solid-state NMR of lignocellulose

Holistic overview of chemical shift regions and spectral features

Lignin quantification and characterization

Cellulose crystallinity

Tracking biomass deconstruction and conversion

Chemical pretreatments

Physical impacts

Thermal conversion processes—biochar

Biological treatments

Advanced 1D: spectral editing techniques

Dynamics-based filtering

Proton spin relaxation editing (PSRE)

Dipolar dephasing

Exploring 2D and pseudo-2D ssNMR at natural abundance

Psuedo-2D relaxation: proton relaxation measurements and sensitivity to domain mixing

Relaxation: site-specific dynamics of lignocellulose

Leveraging T2 relaxation for water proximity and domain sizes

Variable contact time CP-MAS

Detect strength of heteronuclear dipolar couplings

2D (13C–1H) wideline separation (WISE)

2D (13C–1H) heteronuclear correlation (HETCOR)

Future directions: fast MAS, 1H detection, dynamic nuclear polarization (DNP)

Conclusions and perspectives

Availability of data and materials

Abbreviations

References

Acknowledgements

Funding

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Keywords

Biotechnology for Biofuels and Bioproducts

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Routine 1D ¹³C solid-state NMR of lignocellulose

Leveraging T₂ relaxation for water proximity and domain sizes

2D (¹³C–¹H) wideline separation (WISE)

2D (¹³C–¹H) heteronuclear correlation (HETCOR)

Future directions: fast MAS, ¹H detection, dynamic nuclear polarization (DNP)