# Complementary NMR analyses of cellulose-based assembly reveal how multi-scale structure governs saccharification efficiency in steam-exploded wood

Edwige Audibert, Xavier Falourd, Adriana Quintero, Frédéric Martel, Gabriel Paës, Caroline Rémond

► To cite this version:

Edwige Audibert, Xavier Falourd, Adriana Quintero, Frédéric Martel, Gabriel Paës, et al. Complementary NMR analyses of cellulose-based assembly reveal how multi-scale structure governs saccharification efficiency in steam-exploded wood. Carbohydrate Polymers, 2026, 377, pp.124898. 10.1016/j.carbpol.2026.124898. hal-05458312

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Submitted on 23 Jan 2026

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# Complementary NMR analyses of cellulose-based assembly reveal how multi-scale structure governs saccharification efficiency in steam-exploded wood

Edwige Audibert<sup>a,b,1</sup>, Xavier Falourd<sup>c,d,e,f,1</sup>, Adriana Quintero<sup>b</sup>, Frédéric Martel<sup>b</sup>, Gabriel Paës<sup>a,**</sup>, Caroline Rémond<sup>a,**</sup>

## ARTICLE INFO

### Keywords:

Enzymatic saccharification
lignocellulosic biomass
multiscale characterization
solid-state NMR
steam explosion
time domain NMR

## ABSTRACT

Lignocellulosic biomass is a promising alternative to fossil resources to produce biobased products. However, this biomass is highly recalcitrant to enzymatic processes and requires a pretreatment step to overcome it. To understand the role of lignocellulose polymers organization on recalcitrance, characterization at different scales is critical, preferentially by employing non-degradative techniques which maintain sample physical integrity. This requires to combine several techniques thus limiting their application. In this study, structural and morphological modifications caused by steam explosion pretreatment on oak, poplar and spruce wood were enlightened using a combination of advanced ¹³C solid-state NMR and time-domain NMR techniques. Over increasing pretreatment severity, hemicelluloses content was decreased and amorphous cellulose was partially depolymerized, leading to higher cellulose crystallinity: a swelling of the overall polymer matrix was thus observed. A relatively constant water mobility associated to a reduction in pore size were observed, revealing that only a specific pore range of 5–15 nm generated at middle severity pretreatment is favorable to enzyme diffusion and hydrolysis reaction. Interestingly, β-O-4 bonds content and enzymatic hydrolysis yields were highly correlated, confirming the peculiar role of lignin in recalcitrance. Overall, this work demonstrates that structural information based on NMR approaches can reveal biomass species-dependent features explaining the variable saccharification efficiency over steam explosion pretreatment severity.

## 1. Introduction

Biorefinery processes have been developed for the past decades to produce environmentally friendly biobased materials, chemicals and fuels. (Ashokkumar et al., 2022) Lignocellulosic biomass (LB), including agriculture co-products, energy crops and wood, is one of the most promising feedstocks currently utilized to supplant fossil resources. LB is rich in polysaccharides, namely cellulose (40–60 %) and hemicelluloses (10–30 %), but these constituents form together with lignin (15–25 %), a complex network embedded in the plant cell walls that leads to recalcitrance of LB towards biological conversion. (Banu et al., 2021) This

recalcitrance is linked to limitations in cellulose accessibility and diffusion of enzymes in the lignocellulosic matrix due to long and inflexible cellulose chains and hemicelluloses and lignin acting as a natural barrier. (Chakraborty et al., 2024) A pretreatment step is therefore required to deconstruct the lignocellulosic cell walls, enhancing access to cellulose and thus improve LB reactivity.

Pretreatments can be physical, chemical, biological or mixed processes. (Mankar et al., 2021; Periyasamy et al., 2022) Steam explosion (SE) is a physicochemical pretreatment which consists in putting LB in contact with steam at high pressure (1–3.5 MPa) and high temperature (180–240 °C) for a short residence time (2–20 min), then pressure is

<footnote><sup>a</sup> Université de Reims Champagne-Ardenne, INRAE, FARE, UMR A 614, AFERE, Reims, France</footnote>

<footnote><sup>b</sup> Européenne de Biomasse, Paris-Pomacle, France</footnote>

<footnote><sup>c</sup> INRAE, BIA, UR1268, Nantes, France</footnote>

<footnote><sup>d</sup> INRAE, BIBS facility, PROBE infrastructure, Nantes, France</footnote>

<footnote>* Correspondence to: X. Falourd, INRAE, BIA, UR1268, Nantes, France.</footnote>

<footnote>** Corresponding authors.</footnote>

<footnote>E-mail addresses: xavier.falourd@inrae.fr (X. Falourd), gabriel.paes@inrae.fr (G. Paës), caroline.remond@univ-reims.fr (C. Rémond).</footnote>

<footnote n="1">¹ First authors.</footnote>
reduced to atmospheric pressure leading to the explosion of the material. (Yu et al., 2022) Physical and chemical modifications of LB occur during SE pretreatment, such as hemicelluloses and amorphous cellulose degradation (Pérez-Pimienta et al., 2020), lignin depolymerization (Besserer et al., 2022) and morphological modifications. (Antczak et al., 2022).

Improved reactivity of LB and better enzymatic saccharification efficiency are linked to various physicochemical features (Zoglami & Paës, 2019). That is why a thorough characterization is required to monitor the modifications at different length-scales. Among usual techniques of characterization, chemical degradation followed by chromatographic quantitation assesses the lignocellulosic content (Tan et al., 2019), infrared spectroscopy provides qualitative and semi-quantitative structural information (Xu et al., 2013), spectrofluorimetry gives information about lignin environment (Auxenfans et al., 2017) and mercury pycnometry evaluates porosity (Muzamal et al., 2015).

The absence of any modifications in the sample during its preparation or analysis provides more relevant results and is thus preferred to investigate pristine polymer architecture. Nuclear Magnetic Resonance spectroscopy (NMR) can relieve this challenge as it helps to elucidate the chemical structure without previous separation of the components from the lignocellulosic matrix. (Foston et al., 2016) Solution-state ¹H/¹³C NMR has been proved to be efficient for quantification of some functional groups in underivatized lignin (Rodrigues et al., 2022; Wen et al., 2013) and this technique has the advantage to offer high signal-to-noise ratio which leads to high resolution spectra with reasonable detection times. But solution-state NMR requires complex sample preparation, including isolation and solubilization of compounds using organic solvents. At solid-state, ¹³C NMR is used to investigate the supramolecular organization and the interactions of LB constituents. However, dipolar interactions that predominate in the lignocellulosic matrix are increased at solid-state, leading to line broadening and a reduced spectral resolution. This can be improved with the Magic-Angle Spinning (MAS) technique: in practice, tilting samples to the magic-angle (i.e. 54.74°) and increasing the spinning rate allows to reduce the broadening of signals, and it has been found to be efficient for the analysis of complex assemblies such as cell walls of algae, bacteria, fungi, plants and barks. (Ghassemi et al., 2022; Xue et al., 2024) Moreover, ¹³C NMR requires long acquisition times to obtain sufficient spectra resolution, due to the low abundance of ¹³C isotopes in nature (i.e. 1.1 %), which are 100 times less abundant than ¹H and 4 times less sensitive. (Rodrigues et al., 2022; Wen et al., 2013) In order to improve the resolution due to the low abundance and low sensitivity of ¹³C, the commonly used cross-polarization (CP) sequence consists of generating a proton magnetization and transfer it to the carbon, which increases the carbon signal and reduces operating cost. (Conte et al., 2004) In LB, interactions between cellulose, hemicelluloses and lignin are heterogeneous and CP data are usually not quantitative and cannot be properly compared. (Johnson & Schmidt-Rohr, 2014) The multiCP pulse sequence however makes it possible to obtain spectra from which quantitative data can be extracted by performing consecutive CP separated by a time of proton magnetization recovery. (Johnson & Schmidt-Rohr, 2014) So, solid-state NMR (ssNMR) using multiCP sequences is a comprehensive approach to characterize the structure of LB constituents (Cipriano et al., 2020; Simmons et al., 2016; T. Wang & Hong, 2016) and assess ultrastructure features such as cellulose crystallinity. (Bernardinelli et al., 2015; Salem et al., 2023).

The macromolecular environment of the polymers can also be assessed with ssNMR using the spin-lattice in the rotating-frame relaxation time of proton $T_{1p}^H$. It can be obtained with the Variable Spin Lock (VSL) sequence, which records the decay of the carbon signal as a function of a spin-lock time. The curve obtained is a decreasing exponential function, enabling to determine the $T_{1p}^H$. (Falourd et al., 2024) $T_{1p}^H$ provides information on the molecular order, i.e. organization and homogeneity, of the surroundings of analyzed carbons. (Falourd et al.,

2024) It can be seen as a tool for probing molecular order in a volume ranging from 2 to 30 nm around the signal under consideration (Newman, 1992). This technique was used in the past on complex objects, for instance to study charred and uncharred wood (Smernik et al., 2002) or to investigate the polymer chain dynamics of polymer/clay nanocomposites, (Lorthioir et al., 2009) and more recently to characterize the structures of glucans (Kono et al., 2020), cellulose/glucosmannan blends (Kakutani et al., 2022) and starch. (Falourd et al., 2024).

In addition to these modifications, porosity and LB polymers-water interactions are also impacted by the pretreatment and play a key role in recalcitrance. NMR relaxometry assesses the mobility of water in LB and its interactions with the molecular environment (Felby et al., 2008) by determining the distribution of ¹H spin-spin relaxation time ($T_2$). The shorter the $T_2$is, the less mobile the water is due to molecular interactions. It has been demonstrated that $T_2$ values could be related to an estimation of the porosity of the material. (Meng & Ragauskas, 2014) This technique has been used recently to evaluate porosity evolution in wood (Jin et al., 2024; Yang et al., 2024) and several studies demonstrated that NMR relaxometry and investigation of polymer-water interactions are useful to understand and predict recalcitrance and enzymatic degradation of LB, such as maize, (Barron et al., 2021) wheat straw, (Thomsen et al., 2021; Weiss et al., 2019) switchgrass and cornstover. (Williams et al., 2017) This porosity assessment method can be applied to samples in an aqueous medium, bringing the measurement closer to saccharification conditions than to more conventional methods using gas absorption (Yin et al., 2017) or mercury intrusion porosimetry. (Sun et al., 2024) It also stands as a complementary technique to ssNMR to understand how the chemical structure modifications affect the LB morphology.

ssNMR has been used to characterize LB which was thermally treated by torrefaction (Park et al., 2013) and pyrolysis (Haddad et al., 2017). But only a few studies proposed to combine different NMR approaches to assess structural and morphological features of complex lignocellulosic matrices. (Barron et al., 2021; Herbaut, Zoglami, Habrant, et al., 2018; Leroy et al., 2021).

We made the assumption that a panel of different NMR techniques would complete the already described approaches to understand how LB components and their interactions modifications are affected by pretreatment severity conditions. The present study focuses on the multi-scale characterization of three different steam exploded woody biomass (oak, poplar and spruce) to assess chemical and physical changes occurring in LB after different SE pretreatment severity conditions. Previously, particle size and shape, structural features such as cellulose crystallinity and lignin condensation, and hydrophobic behavior of pretreated wood were assessed using microscopy, fluorescence and infrared spectroscopy and dynamic vapor sorption, (Audibert, Quintero, et al., 2025). This revealed the impact of such features on enzymatic saccharification. Here, several advanced ssNMR approaches were combined to further investigate the relationships between enzymatic saccharification and structural properties at different scales. Supramolecular organization of LB components and their interactions was evaluated through probes such as cellulose fibrils dimension, lignin depolymerization and pretreated wood porosity. Polymer ultrastructure was assessed using multiCP and interactions between cellulose, hemicelluloses and lignin using VSL, while interactions between LB polymers and water was studied using NMR relaxometry.

## 2. Materials and methods

### 2.1. Wood materials and steam explosion pretreatment

Oak, poplar and spruce were collected from forests in the Marne Department (Great-East region, France).

Continuous SE was carried out at pilot-scale with the equipment (Valmet AB, Sweden) described previously. (Audibert, Floret, et al., 2025) Wood was chopped and sieved at 16 mm before entering the SE
reactor with an initial moisture content between 20 % and 50 %. Wood particles were conveyed horizontally along the reactor with a screw at a rotating speed to reach a residence time of 5, 12.5 or 20 min. Pretreatment temperature (190–207.5 °C) and residence time (5–20 min) were defined as presented in Table 1 and a combined severity factor (CSF) was calculated (Supplementary Eq. 1) for each couple of condition. (Overend & Chornet, 1987) The solid fraction was recovered and dried to reach 5 % moisture content.

## 2.2. $^{13}$C solid-state NMR

For ssNMR analyses, untreated wood chips were cut with scissors to reduce granulometry. This step was not necessary for steam-exploded wood samples, already powdered when exiting the pretreatment device. Wood and ultrapure water were accurately weighed so that 140 mg of each wood sample were mixed with 40 mL of ultrapure water to reach 22.2 % ± 0.3 % moisture content. Rotors were packed with the mixture (Supplementary Table 1). $^{13}$C solid state NMR (ssNMR) measurements were carried out on a Bruker AVANCE NEO 400 MHz spectrometer (Wissembourg, France) operating at a carbon frequency of 100.62 MHz using multi cross-polarization magic angle spinning (multiCPMAS) technique. (Bernardinelli et al., 2015) The MAS rate was set at 12 kHz and each spectrum was acquired at 293 K. Sequence parameters were: 90° proton pulse of 3.8 ± 0.2 µs, A total of seven CP blocks were implemented with 1.1 ms of contact time. The recycle delay was 5 s and the duration of the repolarization period tz was 0.9 s. Each spectrum resulted from the accumulation of 2048 scans. Glycine was used as an external reference for the calibration of chemical shifts with assignment of the carbonyl at 176.03 ppm. The number of blocks and tz values were chosen according to Bernardinelli et al. (2015) and confirmed by Zhao et al. (2021) and Addison et al. (2024).

Spectra were deconvoluted using a least-squares fitting method (Gaussian and Lorentzian sum) with Peakfit® software (Systat Software Inc., USA) and chemical shifts, peak area and peak width were determined.

Cellulose crystallinity was calculated from the deconvoluted peaks in the C4 region using the method of Larsson et al. (Larsson et al., 1997) (Eq. 1):

$$CrI = \frac{\Sigma peak area in C4 crystalline region}{\Sigma peak area in C4 region} \quad (1)$$

In the C4 crystalline region, three Lorentzian peaks are associated with cellulose: Cr ($I_α$) at 88.9 ± 0.2 ppm, Cr ($I_{α+β}$) at 88.2 ± 0.1 ppm and Cr ($I_β$) at 87.1 ± 0.4 ppm. An additional Gaussian peak represents the para-crystalline (PCr) contribution at 87.7 ± 0.2 ppm. In the C4 amorphous region, two Gaussian peaks are associated with accessible surface (AS) at 82.6 ± 0.1 and 83.6 ± 0.1 ppm and one corresponds to inaccessible surface (IAS) at 83.2 ± 0.1 ppm. Lateral fibril dimension (LFD) and lateral fibril aggregate dimension (LFAD) were also determined from the signal strengths with the width of cellulose chains estimated at 0.57 nm as introduced previously. (P. Liu et al., 2022; Moreau et al., 2022; Newman, 1999; Pääkkö et al., 2007).

**Table 1**

Steam explosion experimental conditions and combined severity factor (CSF) associated.

<table><thead><tr><th></th><th>CSF</th><th>Temperature (°C)</th><th>Residence time (min)</th></tr></thead><tbody><tr><td rowspan="3">Oak</td><td>3.35</td><td>190</td><td>5</td></tr><tr><td>3.97</td><td>197.5</td><td>12.5</td></tr><tr><td>4.39</td><td>205</td><td>20</td></tr><tr><td rowspan="3">Poplar</td><td>3.57</td><td>197.5</td><td>5</td></tr><tr><td>3.97</td><td>197.5</td><td>12.5</td></tr><tr><td>4.39</td><td>205</td><td>20</td></tr><tr><td rowspan="3">Spruce</td><td>3.57</td><td>197.5</td><td>5</td></tr><tr><td>4.17</td><td>197.5</td><td>20</td></tr><tr><td>4.47</td><td>207.5</td><td>20</td></tr></tbody></table>

The relative content of β-O-4 linkages in lignin was also estimated from deconvoluted spectra using the ratio between the peak area at 151.9 ± 0.2 ppm and the peak area at 147 ± 0.3 ppm (Eq. 2). The peak at 152 ppm is attributed to aromatic carbons S3/5 in syringyl units with etherified bonds in S4 and the peak at 147 ppm corresponds to the same carbons in non-etherified S-units but also with a contribution of G3/4 in etherified and non-etherified guaiacyl units. (Xue et al., 2024; Zheng & Schmidt-Rohr, 2024)

$$ \%_{\beta-O-4} = \frac{\text{Peak area 152 ppm}}{\text{Peak area 152 ppm} + \text{Peak area 147 ppm}} \times 100 \quad (2) $$

Molecular order of the samples was characterized using the Variable Spin Lock (VSL) sequence. Spin locktime ($τ$) was varied between 100 µs and 10,000 µs using 100 µs steps until 500 µs and then 500 µs steps. 24 contact times in total were recorded with 256 accumulations for each spectrum. The recovery time was 3 s. (Falourd et al., 2022b) Changes in the peaks intensity of the C4 crystalline region and the methoxy group from lignin (55 ppm) were fitted with the following equation (Eq. 3):

$$ I(\tau) = \sum_{i=1}^{n} I_{0,n} \cdot e^{-\tau/T_{1\rho,n}^{H}} \quad (3) $$

where $I(τ)$ is the peak intensity for a given contact time ($τ$), $n$ is the number of populations of different molecular order in the sample, $I_0$ is the percentage of a population, and $T_{1ρ,n}^H$ is the relaxation time associated for a population.

## 2.3. Time-domain NMR

140 mg of each wood sample were analyzed at two hydration levels, around 20 % (dry basis) and water saturation, to study the water mobility and interactions with the wood matrix, which can also be related to the porosity of the samples. Relaxation times ($T_2$) were measured at 4 °C using a Bruker Minispec mq20 (0.47 T), equipped with a thermostated $^{1}$H probe, with the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence. For 20 % hydration, echo time was 80 µs, 500 even echoes were recorded, 512 scans were acquired and the recycle delay was 3 s. For saturated hydration, echo time was 300 µs, 5000 even echoes were recorded, 128 scans were acquired and the recycle delay was 10 s. All assays were performed as biological triplicates.

The relaxation signals from the samples were converted into a continuous distribution of relaxation times $T_2$ using software partly developed in the laboratory (inverse Laplace transformation, L1 regularisation, modelling of the peaks in the distribution using an asymmetric normal distribution). (Berman et al., 2013; Saunders, 2017).

## 2.4. Enzymatic saccharification

Enzymatic hydrolyses were carried out as described previously (Audibert, Floret, et al., 2025) using Cellic® CTec2 cocktail (Novozymes, Denmark) with the following operational conditions: 10 % w/v biomass loading, 10 FPU/g biomass, 72 h of reaction at pH 5, 50 °C, 150 rpm stirring.

# 3. Results and discussion

## 3.1. Polymers ultrastructure

As shown in Fig. 1, the spectra allow to discriminate between the main components of the wood samples. Characteristic signals from cellulose (in red), lignin (in purple), and hemicelluloses and/or pectins are clearly identifiable. (Sievers et al., 2009) Notable differences are already visible in the polyphenolic signals associated with lignin (from 110 to 160 ppm). As expected, the spectral profile of spruce differs significantly from the two others, spruce lignin consisting almost entirely of G-units (Bogolitsyn et al., 2021) and being more condensed
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** $^{13}$C multiCPMAS spectra of poplar, spruce and oak without steam explosion treatment.</figcaption></figure>

than hardwoods lignin. (Z. Wang & Deuss, 2023).

After normalization of the spectra (Supplementary Fig. 1–3), hemi-celluloses (20 ppm and 172 ppm) and amorphous cellulose (83–84 ppm) signals intensities are clearly diminishing for pretreated samples, meaning hemicelluloses content is decreased and amorphous cellulose is partially depolymerized with increasing pretreatment severity. Crystalline cellulose (88 ppm) and lignin (120–150 ppm) signals are concomitantly increased, resulting from loss of hemicelluloses in wood. This confirms previous observations carried out using acid hydrolysis and HPAEC-PAD to quantify cellulose, hemicelluloses and lignin contents, (Audibert, Floret, et al., 2025) and infrared spectroscopy to assess wood crystallinity. (Audibert, Quintero, et al., 2025).

### 3.1.1. Cellulose

Changes in the supramolecular structure of cellulose were assessed using the deconvolution of C4 region (Supplementary Fig. 4). The crystallinity index was calculated from deconvoluted peaks for all samples (Table 2). The crystallinity of wood significantly increased already from the lowest severity of pretreatment and a slight increase of crystallinity could be seen between the lowest and the highest pretreatment severity. The crystallinity index increased by 20 points, 18 points and 17 points from the initial value for oak, poplar and spruce, i.e. 53 %, 45 % and 41 % increase respectively. Pretreatment effect was quite similar for all wood species and is also comparable to steam exploded poplar characterized with XRD (Haddis et al., 2024) as well as the effect of hydrothermal pretreatment on corn stover. (Yao et al., 2018) The same samples were previously characterized with FTIR (Audibert, Quintero, et al., 2025) and the lateral order index, which can be associated to crystallinity, was also found to increase with pretreatment severity thus confirming present results. This can be explained by

the partial degradation of amorphous cellulose due to the autohydrolysis reaction taking place during pretreatment and related to the deacetylation of hemicelluloses. (Li et al., 2017).

Changes in ultrastructure were assessed by calculating LFD (lateral fibril dimension) and LFAD (lateral fibril aggregate dimension) from deconvoluted spectra (Table 2). Both significantly increased after pretreatment compared to untreated woods. Like crystallinity, there was a slight increase between low and high severities, but not completely significant for LFAD given the repeatability of measurements assessed in a previous study. (Leroy et al., 2021) As a reference, microfibril dimension (LFD) in *Pinus radiata* is 3–4 nm, (Donaldson & Xu, 2005) and macrofibril dimensions, i.e. aggregates, are 14–23 nm in poplar and 19–27 nm in *Pinus radiata*. (Donaldson, 2007) Here, LFD increased from 3 nm to 4.8 nm for all species, while LFAD increased from 21.5 nm to 34.8 nm for oak, 25 nm to 41.6 nm for poplar and 23 nm to 42.2 nm for spruce, i.e. 62 %, 66 % and 83 % increase respectively. However, the number of fibrils per aggregate remained constant after pretreatment. Thus, SE caused less modifications in oak's cellulose ultrastructure after pretreatment compared to poplar and spruce.

The increase of LFD indicates a swelling of cellulose due to some breakdown and disruption of hydrogen bonds that take place because of water-cellulose interactions. (Yao et al., 2018) It is also possible that the smallest fibrils were degraded or dragged along with volatile compounds during pretreatment. The increase of LFAD could come from the aggregation of cellulose fibrils into larger bundles from the creation of new cellulose-cellulose interactions. (Foston & Ragauskas, 2010; Silveira et al., 2016) But, the constant LFAD/LFD ratio suggests only a swelling of the overall polymer matrix. A schematic representation of fibrils and aggregates organization was built from calculated percentages of crystalline (Cr) and para-crystalline (PCr) cellulose, accessible (AS) and inaccessible (IAS) surfaces (Fig. 2, where oak wood is taken as a representative example; numeric values in Supplementary Table 2). After SE pretreatment, the para-crystalline cellulose part increased while AS and IAS decreased due to the degradation of amorphous cellulose. Breaking inside IAS could cause a loosening of the polymer matrix and thus the aggregates swelling.

Regarding the enzymatic saccharification efficiency, LFAD (aggregates size) and saccharification yields are negatively correlated for highly pretreated wood samples (R = -0.63). It seems that larger aggregates hinder enzymatic hydrolysis. Pretreated oak LFAD is smaller than poplar and spruce. This could contribute to higher saccharification efficiency for oak.

Such created large cellulose aggregates could prevent cellulases access to glucan chains forming cellulose, thus limiting enzymatic hydrolysis and fermentable sugars release. But, at the same time cellulose conformation seems to be changing and evolving from a *tg* (trans-gauche) to *gg* (gauche-gauche) and *gt* (gauche-trans) conformations (Fig. 3). Contrary to the *tg* conformation of the C6 in cellulose which is the most suitable for hydrogen bonding and stabilization of crystalline cellulose,

<caption>**Table 2**</caption>

Values of parameters extracted from NMR spectra and enzymatic saccharification yields. Crystallinity, lateral fibril dimension (LFD), lateral fibril aggregate dimension (LFAD) and estimated part of β-O-4 bonds were calculated from the deconvolution of the $^{13}$C multiCP spectra.

<table><thead><tr><th></th><th>CSF</th><th>Crystallinity (%)</th><th>LFD (nm)</th><th>LFAD (nm)</th><th>LFAD/LFD</th><th>Estimated β-O-4 bonds (%)</th><th>Enzymatic hydrolysis yield (%) <sup>*</sup></th></tr></thead><tbody><tr><td rowspan="4">Oak</td><td>Untreated</td><td>38</td><td>3.0</td><td>21.5</td><td>7.2</td><td>57</td><td>8.9 ± 2.8</td></tr><tr><td>3.35</td><td>50</td><td>3.9</td><td>32.5</td><td>8.3</td><td>52</td><td>26.6 ± 4.7</td></tr><tr><td>3.97</td><td>56</td><td>4.5</td><td>30.7</td><td>6.8</td><td>23</td><td>69.9 ± 6.4</td></tr><tr><td>4.39</td><td>58</td><td>4.8</td><td>34.8</td><td>7.3</td><td>18</td><td>67.9 ± 3.2</td></tr><tr><td rowspan="4">Poplar</td><td>Untreated</td><td>40</td><td>3.1</td><td>25.0</td><td>8.1</td><td>63</td><td>10.7 ± 0.6</td></tr><tr><td>3.57</td><td>55</td><td>4.4</td><td>34.8</td><td>7.9</td><td>46</td><td>40.2 ± 2.7</td></tr><tr><td>3.97</td><td>54</td><td>4.3</td><td>33.7</td><td>7.8</td><td>35</td><td>61.5 ± 4.0</td></tr><tr><td>4.39</td><td>58</td><td>4.8</td><td>41.6</td><td>8.7</td><td>28</td><td>61.3 ± 1.0</td></tr><tr><td rowspan="4">Spruce</td><td>Untreated</td><td>41</td><td>3.1</td><td>23.0</td><td>7.4</td><td>NA</td><td>4.7 ± 0.2</td></tr><tr><td>3.57</td><td>54</td><td>4.3</td><td>37.9</td><td>8.8</td><td>NA</td><td>22.1 ± 0.7</td></tr><tr><td>4.17</td><td>56</td><td>4.5</td><td>37.6</td><td>8.4</td><td>NA</td><td>25.8 ± 0.6</td></tr><tr><td>4.47</td><td>58</td><td>4.8</td><td>42.2</td><td>8.8</td><td>NA</td><td>22.4 ± 3.0</td></tr></tbody></table>

<footnote>* Enzymatic saccharification yields were previously determined. (Audibert, Floret, et al., 2025).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Schematic representation of untreated and pretreated (CSF 4.39) oak fibrils (LFD: Lateral Fibril Dimensions) and aggregate (LFAD: Lateral Fibril Aggregate Dimension) with organization of crystalline cellulose (Cr), para-crystalline cellulose (PCr), accessible (AS) and inaccessible surfaces (IAS).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** C6 region of the solid state ¹³C multiCPMAS NMR spectra for untreated and pretreated oak (A), poplar (B) and spruce (C). Peaks characteristic of changes in cellulose conformations are identified (tg: trans-gauche, gt: gauche-trans, gg: gauche-gauche).</figcaption></figure>

gg and *gt* conformations lead to cellulose conformers with weakened intra- and inter-hydrogen bonds, due to a rotation moving O6 too far from O2 and O3 as described in Wang et al. (2016) and Yao et al. (2018) works. Due to hydrothermal pretreatment, some hydrogen bonds can be broken in crystalline cellulose which would turn part of the cellulose to an amorphous state, as testifies the appearance of the peaks specific to the *gg* and *gt* conformations of cellulose in the C6 region, at 60.5 and 61.5 ppm, respectively (Fig. 3). Simultaneously, the peak at 63 ppm, which can be associated to the *tg* conformation of crystalline cellulose, disappears at mid-range severity for oak and high severity for poplar, which is concomitant with higher enzymatic saccharification yields for these two species. Modification of cellulose from *tg* conformation to *gt* and *gg* conformations has been shown to help reaching high enzymatic saccharification yields, especially due to the higher number of water-exposed glycosidic bonds no longer restricted by hydrogen bonding. (Yao et al., 2018) Finally, the emergence of a shoulder/peak can be noticed at 65 ppm. Wang et al. (2016) attributed it to a type of cellulose, different from *I*<sub>α</sub> and *I*<sub>β</sub>, consisting of a glucan chain interacting with xyloglucans and mainly under the form of the *gt* conformation. Another characteristic peak is 87 ppm in the C4 region, which can also be seen for pretreated samples. Wang et al. also deduced that this type of cellulose specifically played a role in the cell wall loosening. (T. Wang et al., 2016) Appearance of this peak for all pretreated woods is therefore consistent with increasing LFD and LFAD.

Increased crystallinity and swelling of cellulose aggregates point towards a reduced enzymatic activity and enzymes mobility. However, when comparing these structural observations to enzymatic saccharification (Table 2), hydrolysis yields go up with increasing pretreatment severity. Conformation changes could thus counter the negative effects of cellulose structural modifications. Moreover, it is noticed that oak saccharification is higher than that of other wood species. Once again, when relating this to the structural data, oak samples display smaller LFAD, hence smaller cellulose aggregates, than poplar and spruce. This could cause less steric hindrance for the enzymes mobility. Also, an increase of the signal of *gg* conformation in the C6 region (Fig. 3) predominates for pretreated oak samples compared to poplar and spruce. It was previously established that the differences of saccharification efficiency between species were not due to inhibitors, as relatively few are generated during SE pretreatment. (Audibert, Floret, et al., 2025) Therefore, oak reduced aggregates that could favor the enzymes diffusion and modifications in oak cellulose conformation are possible

explanations for its better saccharification compared to other wood species.

### 3.1.2. Lignin

Lignin ultrastructure was also modified after SE pretreatment, as it can be assessed from the 140–160 ppm lignin aromatics region of the spectra (Fig. 4). For oak and poplar (Fig. 4A and B), an inversion of the ratio between the peaks at 152 ppm (S3/5 in etherified S-units) and 147 ppm (S3/5 in non-etherified S-units) can be seen. This testifies of the changes occurring in lignin structure during pretreatment, i.e. lignin depolymerization. For spruce, the percentage of β-O-4 linkages could not be estimated as the calculation is based on the peak at 152 ppm which is characteristic for syringyl structures, (Akim et al., 1993) and spruce, as a softwood, contains mostly guaiacyl structures. (Pereira et al., 2017).

Percentage of β-O-4 linkages, which are the most abundant in lignin, (Ren et al., 2024) was estimated from oak and poplar lignin peaks on the ¹³C ssNMR spectra (Table 2). Relative content of β-O-4 linkages decreased by 39 points for oak and 35 points for poplar between untreated wood and the most severely treated sample. For low pretreatment intensity, oak lignin was less degraded than poplar lignin, then depolymerization occurred more quickly for oak than poplar as there was more depletion in oak at mid-range severity compared to untreated wood. β-aryl ether degradation was reported to occur under moderate acidic conditions due to homolytic cleavage of lignin. (Huang et al., 2022) Indeed, acetic acid content is known to increase with increasing pretreatment severity (Chadni et al., 2019), which could favor lignin degradation and explain the increasing depletion of β-O-4 linkages for high severities of pretreatment.

β-O-4 bonds content and enzymatic hydrolysis yields are highly correlated: *r* = −0.98 and *r* = −0.92 for oak and poplar, respectively. As previously observed, strong lignin depolymerization after SE likely opens the polymer matrix and favors enzymatic hydrolysis. (Auxenfans et al., 2017).

## 3.2. Molecular order of LB

The interactions of the polymers with their environment were investigated using the Variable Spin Lock (VSL) sequence (raw data and fits in Supplementary Fig. 5 and 6). The time constant $T_{I_p}^H$ extracted from the kinetics provides information about the molecular order of cellulose (Table 3) and lignin (Table 4) in their surroundings. Indeed, the longer
<figure><img src="image_6.png" /><figcaption>Fig. 4. Region of the solid state ¹³C multiCPMAS NMR spectra characteristic of S and G-units in untreated and pretreated oak (A), poplar (B) and spruce (C).</figcaption></figure>

<table><caption>**Table 3**
VSL analysis of untreated and pretreated wood: cellulose.</caption><thead><tr><th colspan="7">Oak</th></tr><tr><th colspan="2">Untreated</th><th colspan="2">CSF 3.35</th><th colspan="2">CSF 3.97</th><th colspan="2">CSF 4.39</th></tr></thead><tbody><tr><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P<sup>**</sup><br>(%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>26.5</td><td>100</td><td>1.3</td><td>4</td><td>6.1</td><td>47</td><td>11.6</td><td>81</td></tr><tr><td>-</td><td>-</td><td>13.5</td><td>96</td><td>22.6</td><td>53</td><td>72.9</td><td>19</td></tr><tr><th colspan="7">Poplar</th></tr><tr><td>Untreated<br>T<sup>H</sup><sub>Iρ</sub> (ms)</td><td>P (%)</td><td>CSF 3.57<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 3.97<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.39<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>28.5</td><td>100</td><td>22.8</td><td>100</td><td>20.7</td><td>100</td><td>24.1</td><td>56</td></tr><tr><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>7.3</td><td>44</td></tr><tr><th colspan="7">Spruce</th></tr><tr><td>Untreated<br>T<sup>H</sup><sub>Iρ</sub> (ms)</td><td>P (%)</td><td>CSF 3.57<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.17<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.47<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>3.9</td><td>12</td><td>4.6</td><td>24</td><td>8.1</td><td>43</td><td>11.1</td><td>86</td></tr><tr><td>16.1</td><td>88</td><td>16.4</td><td>76</td><td>23.2</td><td>57</td><td>1.6</td><td>14</td></tr></tbody></table>

<table><caption>**Table 4**
VSL analysis of untreated and pretreated wood: lignin methoxy groups.</caption><thead><tr><th colspan="7">Oak</th></tr><tr><th colspan="2">Untreated</th><th colspan="2">CSF 3.35</th><th colspan="2">CSF 3.97</th><th colspan="2">CSF 4.39</th></tr></thead><tbody><tr><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P<sup>**</sup><br>(%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>15.8</td><td>100</td><td>10.3</td><td>100</td><td>10.0</td><td>100</td><td>10.1</td><td>100</td></tr><tr><th colspan="7">Poplar</th></tr><tr><td>Untreated<br>T<sup>H</sup><sub>Iρ</sub> (ms)</td><td>P (%)</td><td>CSF 3.57<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 3.97<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.39<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>15.5</td><td>100</td><td>13.1</td><td>100</td><td>13.4</td><td>100</td><td>23.1</td><td>39</td></tr><tr><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>5.9</td><td>61</td></tr><tr><th colspan="7">Spruce</th></tr><tr><td>Untreated<br>T<sup>H</sup><sub>Iρ</sub> (ms)</td><td>P (%)</td><td>CSF 3.57<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.17<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td><td>CSF 4.47<br>T<sup>H</sup><sub>Iρ</sub><br>(ms)</td><td>P (%)</td></tr><tr><td>9.1</td><td>100</td><td>9.9</td><td>100</td><td>9.7</td><td>100</td><td>7.7</td><td>100</td></tr></tbody></table>

$T_{I\rho}^H$ is, the more organized and homogeneous the structure is illustrated by the positive correlation determined between $T_{I\rho}^H$ and the crystallinity index of cellulose in a previous study (Falourd et al., 2022a).

Untreated oak and untreated poplar present similar organization. For both species, the surroundings of cellulose and lignin are quite homogeneous, since only one population with $T_{I\rho}^H$ equal to 26.5 and 28.5 ms for cellulose, similar to that of Avicel cellulose obtained from a previous study (Falourd et al., 2022a), and around 15.5 ms for the methoxy groups, associated to lignin. As for spruce, its structure is less homogeneous even in untreated samples: two populations for cellulose surroundings are detected, meaning that 88 % of ¹³C nuclei are relaxing slowly ($T_{I\rho}^H$ = 16.1 ms) and are evolving in a homogeneous environment, while a small portion of ¹³C nuclei (12 %) are relaxing more rapidly ($T_{I\rho}^H$ = 3.9 ms) and thus evolving in a different, less organized environment.

Pretreated oak displays high destructured state as already observed from the lowest pretreatment severity, with the appearance of two cellulose environments with shorter $T_{I\rho}^H$ (1.3 ms and 13.5 ms) than untreated oak. This shows the efficiency of the SE pretreatment on this wood species. $T_{I\rho}^H$ rises with increasing severity of pretreatment, and therefore cellulose molecular order grows for both populations. This is due to the degradation of amorphous cellulose and it is consistent with the increasing crystallinity index calculated previously. An inversion of populations with increasing pretreatment severity can be noticed: the population of short $T_{I\rho}^H$ becomes maximal while the population of long $T_{I\rho}^H$ is reduced, meaning more cellulose and cellulose environment destructuring for high severities. Concerning oak lignin, $T_{I\rho}^H$ from methoxy signals drops to 5 ms for pretreated oak compared to untreated. This could be linked to a slight disorganization of lignin environment. Regarding multiCPMAS quantifications, it seems that β-O-4 bonds are broken but there is no mobility of lignin in the lignocellulosic matrix, since the $T_{I\rho}^H$ is steady for all pretreatment severities.

Contrary to oak, pretreated poplar shows a different macromolecular organization evolution. Here, cellulose surrounding seems less affected by SE than in oak wood as it is not modified until reaching the highest severity and only half of the total cellulose environment is associated to a shorter $T_{I\rho}^H$. Contrarily, a second population (61 %) of shorter $T_{I\rho}^H$ (5.9 ms) appears at CSF 4.39 for poplar. This suggests that poplar lignin environment is more strongly affected than oak lignin and an important destructuring accompanies the β-O-4 bonds cleavage. Mobility and redistribution of lignin in the lignocellulosic matrix during pretreatment is known to have an inhibitory effect on cellulases by unproductive binding and thus it limits the saccharification efficiency. (Ko et al., 2015)

<footnote>* Relaxation time.</footnote>

<footnote>** Population associated to a relaxation time.</footnote>

<footnote>* Relaxation time.</footnote>

<footnote>** Population associated to a relaxation time.</footnote>
This could therefore be an explanation to the limiting enzymatic yield obtained for poplar pretreated at high SE severity.

No detectable lignin reorganization occurred during pretreatment of spruce as $T_{I\rho}^H$ remains steady with increasing SE severity. However, some disorganization in the cellulose environment can be seen for lowest and mid-range severities, as the population for shortest $T_{I\rho}^H$ (between 3.9 and 8.1 ms) increases from 12% to 43% accompanied with a slight increase of molecular order. At the highest severity, the population of highest $T_{I\rho}^H$

has been redistributed in shorter ones: notably, a third population appears with the shortest $T_{I\rho}^H$ of 1.6 ms. It seems therefore that the spruce cellulose environment is totally destructured. So, while cellulose is modified, the surrounding lignin shows few changes, which likely explains the strong recalcitrance of spruce even at high SE severity.

Regarding enzymatic saccharification efficiency depending of wood species, it seems that the combination of strong destructuring in cellulose environment and slight destructuring in lignin environment offer

<figure><img src="image_7.png" /><figcaption>Fig. 5. T₂ distribution in wood samples at 20 % relative humidity (A-C) and under water saturation (D-F).</figcaption></figure>
the best conditions for enzymes diffusion, explaining high yields obtained for pretreated oak samples. While for poplar, reorganization of lignin environment, such as condensation which was previously assessed through FTIR (Audibert, Quintero, et al., 2025), and slight cellulose destructuring would limit enzymes accessibility. As for spruce, enzymes accessibility seems hindered because pretreatment lead to no lignin destructuring even when cellulose environment is highly destructured.

### 3.3. Water mobility and wood porosity assessment

As the enzymatic saccharification is performed with glycoside hydrolases that need water to catalyze the hydrolysis reaction, the study of the water mobility is important. Changes in water mobility within the lignocellulosic matrix were investigated using time-domain NMR (TD-NMR) by determining relaxation times ($T_2$) of proton in untreated and SE wood samples.

Water mobility at 20 % water content (Fig. 5 A-C) was studied as a complement to multiCPMAS and VSL measurements to help understanding how the structural changes affect the affinity between pretreated wood and water. Relaxation time values and associated population are presented in Supplementary Table 3. For all wood species, pretreated samples display shorter $T_2$ which means there are more water constraints and thus more interactions, such as hydrogen bonding, between water and LB polysaccharides components. (Bianquini et al., 2020) Similar observations were made on spruce where decreasing $T_2$ values related to increasing severities of pretreatment were associated to higher saccharification yields (Weiss et al., 2017).

For oak (Fig. 5A), one major population at $T_{2a} = 4.1$ ms is observed as well as some water with less constraint ($T_{2b-d}$). After pretreatment, two populations of shorter $T_2$ (labelled a and b) emerge and display more affinity with water. However, a decreasing population for $T_{2a}$ and an increasing population for $T_{2b}$ can be seen with increasing pretreatment severity. Less bound water is found in the sample pretreated at the highest severity. Oak pretreated at CSF 4.39 has less affinity with water compared to less treated samples, which could partially explain why enzymatic saccharification yield is not improved between CSF 3.97 and 4.39. For poplar too (Fig. 5B), two populations with shorter $T_2$ appear after pretreatment ($T_{2a}$ and $T_{2b}$), but here the proportion of bound water increases with increasing severity. Peaks are also broader, especially for the highest severity of pretreatment, which suggests heterogeneity in the matrix: this is consistent with the destructuring that was observed with VSL measurements. Pretreated spruce display slightly more bound water than untreated spruce (Fig. 5C). Overall, SE does not seem to have a strong effect on water mobility in spruce, which could relate to the stagnation of hydrolysis yields for this species.

Water mobility in the wood samples was also studied with a water saturated level of hydration (Fig. 5 D-F) to be in close conditions to enzymatic hydrolysis reaction and gain information about porosity evolution of wood samples, as relaxation times can be related to pore size. (Meng et al., 2013) The relationship between $T_2$ relaxation times and pore size was established as previously described in Barron et al. (2021). Pore size order of magnitude calculated from $T_2$ was consistent with the values Liu et al. obtained from cellulosic esters. (Y. Liu et al., 2024).

For all wood species, reduction of pore size was noticed with increasing severity, demonstrated by the shorter $T_2$. Globally, there is a stronger impact of SE pretreatment on the porosity of spruce than for oak and poplar in this order, as can be seen from the decrease of the $T_{2a}$ peak towards shorter times with increasing severity. The proportions of small pores (<15 nm) have more than doubled for all species (Supplementary Table 4), which results either from the creation of new pores or the collapsing of existing pores by SE pretreatment action. There is especially an increase of the proportion of pores between 5 and 15 nm. Untreated oak, poplar and spruce display 7%, 6.5% and 5.2% of pores in the 5-15 nm range, respectively. This proportion increases up to 20% for oak (CSF 3.97) and poplar (CSF 4.39), and up to 13.4% for spruce

(CSF 4.47). The average cellulase ellipsoidal diameter is in the 4-6.5 nm range, (Bubner et al., 2012) which means that an increase in the proportion of the pore size 5-15 nm range would favor the enzyme mobility and catalytic activity as it was already described, (Herbaut, Zoghlami, Habrant, et al., 2018; Leroy et al., 2021) considering that the diffusion of enzymes in larger pores is improved.

Consequently, SE pretreatment is beneficial at middle severity by creating porosity favorable to cellulase action. But at high severities, too much porosity is likely created (more pores <5 nm) which could limit the diffusion and thus the action of cellulases during enzymatic saccharification. For instance, only 14% of pores in 5-15 nm range are left for oak pretreated at CSF 4.39, and pores below 5 nm are created in spruce wood ($T_{2a}$) already from CSF 3.57. This lines up with a previous study which determined pores in range 5-20 nm governed enzymatic saccharification and smaller pores do not allow sufficient diffusion of enzymes. (Herbaut, Zoghlami, & Paës, 2018) These observations are consistent with the poor saccharification yields obtained for spruce and the limited yields of highly pretreated oak and poplar.

### 4. Conclusion

A combined solid-state NMR and relaxometry approach was employed to investigate how steam-explosion pretreatment alters the structure and morphology of wood, with the goal of better understanding its recalcitrance to enzymatic saccharification. By tracking ultrastructural changes, network polymer breakdown, and porosity evolution, these transformations were linked to saccharification efficiency and clarified how pretreatment severity and wood species influence the outcome (Fig. 6).

Our results show that increased cellulose crystallinity, driven by hemicellulose removal, and lignin depolymerization, evidenced by β-O-4 linkage cleavage, both enhance enzymatic saccharification. In contrast, cellulose swelling appears to reduce enzyme accessibility. Likewise, moderate destructuring of cellulose and lignin improves enzyme mobility within the biomass matrix, but excessive lignin disorganization becomes counterproductive. At the macroscale, strongly pretreated samples exhibit lower water affinity, which may further restrict enzymatic performance. Crucially, pore sizes in the 5-15 nm range emerge as a dominant factor, directly governing enzyme accessibility and therefore saccharification efficiency.

This study evidences that NMR-based approaches are powerful multi-scale structural techniques which can advantageously

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Summary of the evolution of structural properties and porosity evaluation with increasing pretreatment severity and their impact on enzymatic saccharification. In green: favorable to enzymatic saccharification, in orange: mitigated effect on enzymatic saccharification, in red: unfavorable to enzymatic saccharification. NA: not available. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
complement other more classically used techniques. Overall, these in-
sights strengthen existing characterizations and offer a clearer structure-
property framework for understanding how steam-exploded wood be-
haves during enzymatic conversion, highlighting the key structural
features that most effectively promote saccharification.

CRediT authorship contribution statement

**Edwige Audibert:** Writing – original draft, Investigation, Formal analysis, Data curation, Conceptualization. **Xavier Falourd:** Writing – review & editing, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Adriana Quintero:** Writing – review & editing, Resources. **Frédéric Martel:** Writing – review & editing, Supervision, Resources. **Gabriel Paës:** Writing – review & editing, Supervision, Conceptualization. **Caroline Rémond:** Writing – review & editing, Supervision, Conceptualization.

Declaration of competing interest

The authors declare no conflict of interest.

Acknowledgements

This work was supported by the French National Agency for
Research and Technology ANRT (PhD fellowship to EA) and the com-
pany Européenne de Biomasse. The authors acknowledge Sarah Rollet
(ARD, France) for carrying out the steam explosion pretreatment. The
authors acknowledge Ali Boulgsoa and Loïc Foucat (INRAE BIA, BIBS,
France) for providing the time-domain NMR spectra treatment software.

**Appendix A. Supplementary data**

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.carbpol.2026.124898.

Data availability

Data will be made available on request.

References

Addison, B., Bu, L., Bharadwaj, V., Crowley, M. F., Harman-Ware, A. E., Crowley, M. F., ... Cieselski, P. N. (2024). Atomistic, macromolecular model of the Populus secondary cell wall informed by solid-state NMR. *Science, Advances*, 10(1), eadi7965. https://doi.org/10.1126/sciadv.adi7965

Akim, L. G., Shevchenko, S. M., & Zarubin, M. Y. (1993). 13C NMR studies on lignins depolymerized with dry hydrogen iodide. *Wood Science and Technology*, 27(4). https://doi.org/10.1007/BF00195299

Antczak, A., Szadkowski, J., Szadkowska, D., & Zawadzki, J. (2022). Assessment of the effectiveness of liquid hot water and steam explosion pretreatments of fast-growing poplar (Populus trichocarpa) wood. *Wood Science and Technology*, 56(1), 87–109. https://doi.org/10.1007/s00226-021-01350-1

Ashokkumar, V., Venkatkarthick, R., Jayashree, S., Chuetor, S., Dharmaraj, S., Kumar, G., Chen, W.-H., & Ngamcharussrivichai, C. (2022). Recent advances in lignocellulosic biomass for biofuels and value-added bioproducts—A critical review. *Bioresource Technology*, 344, Article 126195. https://doi.org/10.1016/j.biortech.2021.126195

Audibert, E., Floret, J., Quintero, A., Martel, F., Rémond, C., & Paës, G. (2025). Determination of trade-offs between 2G bioethanol production yields and pretreatment costs for industrially steam exploded woody biomass. *Applied Energy*, 380, Article 125028. https://doi.org/10.1016/j.apenergy.2024.125028

Audibert, E., Quintero, A., Martel, F., Paes, G., & Rémond, C. (2025). Understanding how physicochemical features from steam exploded wood affect enzymatic saccharification efficiency for bioethanol production. *Sustainable Energy & Fuels*. https://doi.org/10.1039/D5SE00535C, 10.1039/D5SE00535C.

Auxenfans, T., Terryn, C., & Paës, G. (2017). Seeing biomass recalcitrance through fluorescence. *Scientific Reports*, 7(1), 8838. https://doi.org/10.1038/s41598-017-08740-1

Banu, J., R., Sugitha, S., Kavitha, S., Kannah, R., Y., Merrylin, J., & Kumar, G. (2021). Lignocellulosic Biomass Pretreatment for Enhanced Bioenergy Recovery: Effect of Lignocelluloses Recalcitrance and Enhancement Strategies. *Frontiers in Energy Research, 9*, Article 646057. https://doi.org/10.3389/fenrg.2021.646057

Barron, C., Devaux, M.-F., Foucat, L., Falourd, X., Looten, R., Joseph-Aime, M., Durand, S., Bonnin, E., Lapierre, C., Saulnier, L., Rouau, X., & Guillon, F. (2021). Enzymatic degradation of maize shoots: Monitoring of chemical and physical changes reveals different saccharification behaviors. *Biotechnology for Biofuels*, 14(1), 1. https://doi.org/10.1186/s13068-020-01854-1

Berman, P., Levi, O., Parmet, Y., Saunders, M., & Wiesman, Z. (2013). Laplace inversion of low-resolution NMR relaxometry data using sparse representation methods. *Concepts in Magnetic Resonance Part A*, 42(3), 72–88. https://doi.org/10.1002/cmr.a.21263

Bernardinelli, O. D., Lima, M. A., Rezende, C. A., Polikarpov, I., & deAzevedo, E. R. (2015). Quantitative 13C MultiCP solid-state NMR as a tool for evaluation of cellulose crystallinity index measured directly inside sugarcane biomass. *Biotechnology for Biofuels*, 8(1), 110. https://doi.org/10.1186/s13068-015-0292-1

Besserer, A., Obame, S. N., Safou-Tchima, R., Saker, S., Ziegler-Devin, I., & Brosse, N. (2022). Biorefining of Aucoumea klaineana wood: Impact of steam explosion on the composition and ultrastructure the cell wall. *Industrial Crops and Products*, 177, Article 114432. https://doi.org/10.1016/j.indcrop.2021.114432

Bianquini, T. S., Florencio, C., Dos Santos, H., Garcia, R., Colnago, L. A., Ceccato-Antonini, S. R., ... Ribeiro, T. S. (2020). Time domain NMR spectroscopy as a fast method for probing the efficiency of biomass pretreatments for second generation ethanol production. *Biomass and Bioenergy*, 142, Article 105734. https://doi.org/10.1016/j.biombioe.2020.105734

Bogolitsyn, K. G., Gusakova, M. A., & Krasikova, A. A. (2021). Molecular self-organization of wood lignin-carbohydrate matrix. *Planta*, 254(2), 30. https://doi.org/10.1007/s00425-021-03675-4

Bubner, P., Dohr, J., Plank, H., Mayrhofer, C., & Nidetzky, B. (2012). Cellulases Dig Deep. *Journal of Biological Chemistry*, 287(4), 2759–2765. https://doi.org/10.1074/jbc.M111.257717

Chadni, M., Grimi, N., Bals, O., Ziegler-Devin, I., & Brosse, N. (2019). Steam explosion process for the selective extraction of hemicelluloses polymers from spruce sawdust. *Industrial Crops and Products*, 141, Article 111757. https://doi.org/10.1016/j.indcrop.2019.111757

Chakraborty, P., Kumar, R., Chakraborty, S., Saha, S., Chattaraj, S., Roy, S., ... Jeon, B.-H. (2024). Technological advancements in the pretreatment of lignocellulosic biomass for effective valorization: A review of challenges and prospects. *Journal of Industrial and Engineering Chemistry*, 137, 29–60. https://doi.org/10.1016/j.jiec.2024.03.025

Cipriano, D. F., Chinelatto, L. S., Nascimento, S. A., Rezende, C. A., De Menezes, S. M. C., & Freitas, J. C. C. (2020). Potential and limitations of 13C CP/MAS NMR spectroscopy to determine the lignin content of lignocellulosic feedstock. *Biomass and Bioenergy*, 142, Article 105792. https://doi.org/10.1016/j.biombioe.2020.105792

Conte, P., Spaccini, R., & Piccolo, A. (2004). State of the art of CPMAS 13C-NMR spectroscopy applied to natural organic matter. *Progress in Nuclear Magnetic Resonance Spectroscopy*, 44(3-4), 215-223. https://doi.org/10.1016/j.pnmrs.2004.02.002

Donaldson, L. (2007). Cellulose microfibril aggregates and their size variation with cell wall type. *Wood Science and Technology*, 41(5), 443. https://doi.org/10.1007/s00226-006-0121-6

Donaldson, L., & Xu, P. (2005). Microfibril orientation across the secondary cell wall of Radiata pine tracheids. *Trees*, 19(6), 644–653. https://doi.org/10.1007/s00468-005-0428-1

Falourd, X., Lahaye, M., & Rondeau-Mouro, C. (2022a). Assessment of cellulose interactions with water by ssNMR: 1H->13C transfer kinetics revisited. *Carbohydrate Polymers*, 298, Article 120104. https://doi.org/10.1016/j.carbpol.2022.120104

Falourd, X., Lahaye, M., & Rondeau-Mouro, C. (2022b). Optimization of acquisition and processing times for the measurement of 1H to 13C polarization transfer kinetics. *MethodsX*, 9, Article 101914. https://doi.org/10.1016/j.mex.2022.101914

Falourd, X., Lahaye, M., & Rondeau-Mouro, C. (2024). Probing structural features in potato starch granules at moderate hydration through the modelling of 1H->13C polarization transfer kinetics. *International Journal of Biological Macromolecules*, 272, Article 132806. https://doi.org/10.1016/j.ijbiomac.2024.132806

Felby, C., Thygesen, L. G., Kristensen, J. B., Jørgensen, H., & Elder, T. (2008). Cellulose-water interactions during enzymatic hydrolysis as studied by time domain NMR. *Cellulose*, 15(5), 703–710. https://doi.org/10.1007/s10570-008-9222-8

Foston, M., & Ragauskas, A. J. (2010). Changes in lignocellulosic supramolecular and ultrastructure during dilute acid pretreatment of Populus and switchgrass. *Biomass and Bioenergy*, 34(12), 1885–1895. https://doi.org/10.1016/j.biombioe.2010.07.023

Foston, M., Samuel, R., He, J., & Ragauskas, A. J. (2016). A review of whole cell wall NMR by the direct-dissolution of biomass. *Green Chemistry*, 18(3), 608–621. https://doi.org/10.1039/C5GC02828K

Ghassemi, N., Poulhazan, A., Deligey, F., Mentink-Vigier, F., Marcotte, I., & Wang, T. (2022). Solid-State NMR Investigations of Extracellular Matrixes and Cell Walls of Algae, Bacteria, Fungi, and Plants. *Chemical Reviews*, 122(10), 10036–10086. https://doi.org/10.1021/acs.chemrev.1c00669

Haddad, K., Jeguirim, M., Jellali, S., Guizani, C., Delmotte, L., Bennici, S., & Limousy, L. (2017). Combined NMR structural characterization and thermogravimetric analyses for the assessment of the AAEM effect during lignocellulosic biomass pyrolysis. *Energy*, 134, 10–23. https://doi.org/10.1016/j.energy.2017.06.022

Haddis, D. Z., Chae, M., Asomaning, J., & Bressler, D. C. (2024). Evaluation of steam explosion pretreatment on the cellulose nanocrystals (CNCs) yield from poplar wood. *Carbohydrate Polymers*, 323, Article 121460. https://doi.org/10.1016/j.carbpol.2023.121460

Herbaut, M., Zoghlami, A., Habrant, A., Falourd, X., Foucat, L., Chabbert, B., & Paës, G. (2018). Multimodal analysis of pretreated biomass species highlights generic
markers of lignocellulose recalcitrance. *Biotechnology for Biofuels*, 11(1), 52. https://doi.org/10.1186/s13068-018-1053-8

Herbaut, M., Zoghlami, A., & Paës, G. (2018). Dynamical assessment of fluorescent probes mobility in poplar cell walls reveals nanopores govern saccharification. *Biotechnology for Biofuels*, 11(1), 271. https://doi.org/10.1186/s13068-018-1267-9

Huang, C., Jiang, X., Shen, X., Hu, J., Tang, W., Wu, X., Ragauskas, A., Jameel, H., Meng, X., & Yong, Q. (2022). Lignin-enzyme interaction: A roadblock for efficient enzymatic hydrolysis of lignocellulosics. *Renewable and Sustainable Energy Reviews*, 154, Article 111822. https://doi.org/10.1016/j.rser.2021.111822

Jin, Z., Fu, Y., Chen, Q., & Zeng, Z. (2024). Determination of nuclear magnetic resonance surface relaxivity for the macropore system from wood cell lumen. *Journal of Wood Science*, 70(1), 5. https://doi.org/10.1186/s10086-023-02117-x

Johnson, R. L., & Schmidt-Rohr, K. (2014). Quantitative solid-state 13C NMR with signal enhancement by multiple cross polarization. *Journal of Magnetic Resonance*, 239, 44–49. https://doi.org/10.1016/j.jmr.2013.11.009

Kakutani, H., Okugawa, A., Miyamoto, H., Kono, H., Asami, T., & Yamane, C. (2022). Cellulose/glucomannan blends prepared from aqueous sodium hydroxide solution and their practical use as food materials. *Carbohydrate Polymers*, 298, Article 120101. https://doi.org/10.1016/j.carbpol.2022.120110

Ko, J. K., Ximenes, E., Kim, Y., & Ladisch, M. R. (2015). Adsorption of enzyme onto lignins of liquid hot water pretreated hardwoods: Enzyme Adsorption by Lignin in Hardwood. *Biotechnology and Bioengineering*, 112(3), 447–456. https://doi.org/10.1002/bit.25359

Kono, H., Kondo, N., Isono, T., Ogata, M., & Hirabayashi, K. (2020). Characterization of the secondary structure and order–disorder transition of a β-(1 → 3, 1 → 6)-glucan from Aureobasidium pullulans. *International Journal of Biological Macromolecules*, 154, 1382–1391. https://doi.org/10.1016/j.ijbiomac.2019.11.018

Larsson, P. T., Wickholm, K., & Iversen, T. (1997). A CP/MAS13C NMR investigation of molecular ordering in celluloses. *Carbohydrate Research*, 302(1–2), 19–25. https://doi.org/10.1016/S0008-6215(97)00130-4

Leroy, A., Falourd, X., Foucat, L., Méchin, V., Guillon, F., & Paës, G. (2021). Evaluating polymer interplay after hot water pretreatment to investigate maize stem internode recalcitrance. *Biotechnology for Biofuels*, 14(1), 164. https://doi.org/10.1186/s13068-021-02015-8

Li, M., Cao, S., Meng, X., Studer, M., Wyman, C. E., Ragauskas, A. J., & Pu, Y. (2017). The effect of liquid hot water pretreatment on the chemical–structural alteration and the reduced recalcitrance in poplar. *Biotechnology for Biofuels*, 10(1), 237. https://doi.org/10.1186/s13068-017-0926-6

Liu, P., Jin, C., Zhang, M., Xue, Y., Gao, B., Jia, Y., Yan, Y., Zhang, H., Wang, S., & Tang, Y. (2022). A CP/MAS¹³C NMR investigation of cellulose ultrastructure in traditional Chinese handmade papers. *Holzforschung*, 76(9), 853–862. https://doi.org/10.1515/hf-2022-0048

Liu, Y., Wu, W., Xu, H., Zhou, Q., Zhong, Y., Zhang, L., Xu, Q., Lu, Z., Zhang, J., Zhao, Q., & Mao, Z. (2024). A fast and effective way to measure the inner pore size distributions of wetted cotton fibers and their pretreatment performance using time-domain nuclear magnetic resonance. *International Journal of Biological Macromolecules*, 271, Article 132781. https://doi.org/10.1016/j.ijbiomac.2024.132781

Lorthioir, C., Laupretre, F., Soulestin, J., & Lefebvre, J.-M. (2009). Segmental Dynamics of Poly(ethylene oxide) Chains in a Model Polymer/Clay Intercalated Phase: Solid-State NMR Investigation. *Macromolecules*, 42(1), 218–230. https://doi.org/10.1021/ma801909s

Mankar, A. R., Pandey, A., Modak, A., & Pant, K. K. (2021). Pretreatment of lignocellulosic biomass: A review on recent advances. *Bioresource Technology*, 334, Article 125235. https://doi.org/10.1016/j.biortech.2021.125235

Meng, X., Foston, M., Leisen, J., DeMartini, J., Wyman, C. E., & Ragauskas, A. J. (2013). Determination of porosity of lignocellulosic biomass before and after pretreatment by using Simons' stain and NMR techniques. *Bioresource Technology*, 144, 467–476. https://doi.org/10.1016/j.biortech.2013.06.091

Meng, X., & Ragauskas, A. J. (2014). Recent advances in understanding the role of cellulose accessibility in enzymatic hydrolysis of lignocellulosic substrates. *Current Opinion in Biotechnology*, 27, 150–158. https://doi.org/10.1016/j.copbio.2014.01.014

Moreau, C., Falourd, X., Talantikite, M., Cathala, B., & Villares, A. (2022). Bifunctionalization of Cellulose Fibers by One-Step Williamson's Etherification to Obtain Modified Microfibrillated Cellulose. *ACS Sustainable Chemistry & Engineering*, 10(40), 13415–13423. https://doi.org/10.1021/acssuschemeng.2c03754

Muzamal, M., Jedvert, K., Theliander, H., & Rasmuson, A. (2015). Structural changes in spruce wood during different steps of steam explosion pretreatment. *Holzforschung*, 69(1), 61–66. https://doi.org/10.1515/hf-2013-0234

Newman, R. H. (1992). Nuclear Magnetic Resonance Study of Spatial Relationships Between Chemical Components in Wood Cell Walls. *Holzforschung*, 46(3), 205–210. https://doi.org/10.1515/hfsg.1992.46.3.205

Newman, R. H. (1999). Estimation of the lateral dimensions of cellulose crystallites using NMR signal strengths. *Solid State Nuclear Magnetic Resonance*, 15(1), 21–29. https://doi.org/10.1016/S0926-2040(99)00043-0

Overend, R. P., & Chornet, E. (1987). Fractionation of lignocellulosics by steam-aqueous pretreatments. *Philosophical Transactions of the Royal Society of London, Series A, Mathematical and Physical Sciences*, 321(1561), 523–536. https://doi.org/10.1098/rsta.1987.0029

Pääkkö, M., Ankerfors, M., Kosonen, H., Nykänen, A., Ahola, S., Österberg, M., ... Lindström, T. (2007). Enzymatic Hydrolysis Combined with Mechanical Shearing and High-Pressure Homogenization for Nanoscale Cellulose Fibrils and Strong Gels. *Biomacromolecules*, 8(6), 1934–1941. https://doi.org/10.1021/bm061215p

Park, J., Meng, J., Lim, K. H., Rojas, O. J., & Park, S. (2013). Transformation of lignocellulosic biomass during torrefaction. *Journal of Analytical and Applied Pyrolysis*, 100, 199–206. https://doi.org/10.1016/j.jaap.2012.12.024

Pereira, A., Hoeger, I. C., Ferrer, A., Rencoret, J., Del Rio, J. C., Kruus, K., ... Rojas, O. J. (2017). Lignin Films from Spruce, Eucalyptus, and Wheat Straw Studied with Electroacoustic and Optical Sensors: Effect of Composition and Electrostatic Screening on Enzyme Binding. *Biomacromolecules*, 18(4), 1322–1332. https://doi.org/10.1021/acs.biomac.7b00071

Pérez-Pimienta, J. A., Papa, G., Gladden, J. M., Simmons, B. A., & Sanchez, A. (2020). The effect of continuous tubular reactor technologies on the pretreatment of lignocellulosic biomass at pilot-scale for bioethanol production. *RSC Advances*, 10 (31), 18147–18159. https://doi.org/10.1039/D0RA04031B

Periyasamy, S., Karthik, V., Senthil Kumar, P., Isabel, J. B., Temesgen, T., Hunegnaw, B. M., ... Vo, D.-V. N. (2022). Chemical, physical and biological methods to convert lignocellulosic waste into value-added products. A review. *Environmental Chemistry Letters*, 20(2), 1129–1152. https://doi.org/10.1007/s10311-021-01374-w

Ren, W., Zhang, D., Zhou, Y., Wang, H., Xia, L., Tan, C., ... Yu, Y. (2024). Lignin's complex role in lignocellulosic biomass Recalcitrance: A case study on bamboo. *Chemical Engineering Journal*, 490, Article 151422. https://doi.org/10.1016/j.cej.2024.151422

Rodrigues, R. C. L. B., Green Rodrigues, B., Vieira Canettieri, E., Acosta Martinez, E., Palladino, F., Wisniewski, A., Jr., & Rodrigues, D., Jr. (2022). Comprehensive approach of methods for microstructural analysis and analytical tools in lignocellulosic biomass assessment – A review. *Bioresource Technology*, 348, Article 126627. https://doi.org/10.1016/j.biortech.2021.126627

Salem, K. S., Kasera, N. K., Rahman, M. A., Jameel, H., Habibi, Y., Eichhorn, S. J., ... Lucia, L. A. (2023). Comparison and assessment of methods for cellulose crystallinity determination. *Chemical Society Reviews*. https://doi.org/10.1039/D2CS00569G, 10.1039/D2CS00569G.

Saunders, M. (2017). *PDCO: primal-dual interior method for convex objectives* [Computer software]. https://web.stanford.edu/group/SOL/software/pdcO.

Sievers, C., Marzialetti, T., Hoskins, T. J. C., Valenzuela Olarte, M. B., Agrawal, P. K., & Jones, C. W. (2009). Quantitative solid state NMR analysis of residues from acid hydrolysis of loblolly pine wood. *Bioresource Technology*, 100(20), 4758–4765. https://doi.org/10.1016/j.biortech.2008.11.060

Silveira, R. L., Stoyanov, S. R., Kovalenko, A., & Skaf, M. S. (2016). Cellulose Aggregation under Hydrothermal Pretreatment Conditions. *Biomacromolecules*, 17(8), 2582–2590. https://doi.org/10.1021/acs.biomac.6b00603

Simmons, T. J., Mortimer, J. C., Bernardinelli, O. D., Pöppler, A.-C., Brown, S. P., deAzevedo, E. R., ... Dupree, P. (2016). Folding of xylan onto cellulose fibrils in plant cell walls revealed by solid-state NMR. *Nature Communications*, 7(1), Article 13902. https://doi.org/10.1038/ncomms13902

Smernik, R. J., Baldock, J. A., Oades, J. M., & Whittaker, A. K. (2002). Determination of T1pH Relaxation Rates in Charred and Uncharred Wood and Consequences for NMR Quantitation. *Solid State Nuclear Magnetic Resonance*, 22(1), 50–70. https://doi.org/10.1006/snmr.2002.0064

Sun, C., Chen, H., Madadi, M., Song, G., Meng, X., Zhuang, X., ... Ragauskas, A. J. (2024). Understanding the role of porous particle characteristics and water state distribution at multi-scale variation on cellulase hydrolysis of lignocellulosic biomass. *Energy*, 310, Article 133263. https://doi.org/10.1016/j.energy.2024.133263

Tan, M., Ma, L., Rehman, M. S. U., Ahmed, M. A., Sajid, M., Xu, X., ... Xu, J. (2019). Screening of acidic and alkaline pretreatments for walnut shell and corn stover biorefining using two way heterogeneity evaluation. *Renewable Energy*, 132, 950–958. https://doi.org/10.1016/j.renene.2018.07.131

Thomsen, S. T., Weiss, N. D., Zhang, H., & Felby, C. (2021). Water retention value predicts biomass recalcitrance for pretreated biomass: Biomass water interactions vary based on pretreatment chemistry and reflect composition. *Cellulose*, 28(1), 317–330. https://doi.org/10.1007/s10570-020-03507-w

Wang, T., & Hong, M. (2016). Solid-state NMR investigations of cellulose structure and interactions with matrix polysaccharides in plant primary cell walls. *Journal of Experimental Botany*, 67(2), 503–514. https://doi.org/10.1093/jxb/ery416

Wang, T., Yang, H., Kubicki, J. D., & Hong, M. (2016). Cellulose Structural Polymorphism in Plant Primary Cell Walls Investigated by High-Field 2D Solid-State NMR Spectroscopy and Density Functional Theory Calculations. *Biomacromolecules*, 17(6), 2210–2222. https://doi.org/10.1021/acs.biomac.6b00441

Wang, Z., & Deuss, P. J. (2023). The isolation of lignin with native-like structure. *Biotechnology Advances*, 68, Article 108230. https://doi.org/10.1016/j.biotechadv.2023.108230

Weiss, N. D., Felby, C., & Thygesen, L. G. (2019). Enzymatic hydrolysis is limited by biomass-water interactions at high-solids: Improved performance through substrate modifications. *Biotechnology for Biofuels*, 12(1), 3. https://doi.org/10.1186/s13068-018-1339-x

Weiss, N. D., Thygesen, L. G., Felby, C., Roslander, C., & Gourlay, K. (2017). Biomass-water interactions correlate to recalcitrance and are intensified by pretreatment: An investigation of water constraint and retention in pretreated spruce using low field NMR and water retention value techniques. *Biotechnology Progress*, 33(1), 146–153. https://doi.org/10.1002/bpr.2398

Wen, J.-L., Sun, S.-L., Xue, B.-L., & Sun, R.-C. (2013). Recent Advances in Characterization of Lignin Polymer by Solution-State Nuclear Magnetic Resonance (NMR) Methodology. *Materials*, 6(1), 359–391. https://doi.org/10.3390/ma0610359

Williams, D. L., Crowe, J. D., Ong, R. G., & Hodge, D. B. (2017). Water sorption in pretreated grasses as a predictor of enzymatic hydrolysis yields. *Bioresource Technology*, 245, 242–249. https://doi.org/10.1016/j.biortech.2017.08.200
Xu, F., Yu, J., Tesso, T., Dowell, F., & Wang, D. (2013). Qualitative and quantitative analysis of lignocellulosic biomass using infrared techniques: A mini-review. *Applied Energy, 104*, 801–809. https://doi.org/10.1016/j.apenergy.2012.12.019

Xue, Y., Yu, C., & Kang, X. (2024). Quantitative and Structural Characterization of Native Lignin in Hardwood and Softwood Bark via Solid-State NMR Spectroscopy. *Journal of Agricultural and Food Chemistry, acs.jafc.4c03469*. https://doi.org/10.1021/acs.jafc.4c03469

Yang, T., Luo, D., Wang, L., Hu, C., & Mei, C. (2024). New insights into the influencing mechanisms of hemicellulose removal on dynamic wood-water interactions characterized with low-field NMR. *Industrial Crops and Products, 219*, Article 119139. https://doi.org/10.1016/j.indcrop.2024.119139

Yao, K., Wu, Q., An, R., Meng, W., Ding, M., Li, B., & Yuan, Y. (2018). Hydrothermal pretreatment for deconstruction of plant cell wall: Part II. Effect on cellulose structure and bioconversion. *AICHE Journal, 64*(6), 1954–1964. https://doi.org/10.1002/aic.16106

Yin, J., Yuan, T., Lu, Y., Song, K., Li, H., Zhao, G., & Yin, Y. (2017). Effect of compression combined with steam treatment on the porosity, chemical composition and cellulose

crystalline structure of wood cell walls. *Carbohydrate Polymers, 155*, 163–172. https://doi.org/10.1016/j.carbpol.2016.08.013

Yu, Y., Wu, J., Ren, X., Lau, A., Rezaei, H., Takada, M., Bi, X., & Sokhansanj, S. (2022). Steam explosion of lignocellulosic biomass for multiple advanced bioenergy processes: A review. *Renewable and Sustainable Energy Reviews, 154*, Article 111871. https://doi.org/10.1016/j.rser.2021.111871

Zhao, W., Kirui, A., Deligey, F., Mentink-Vigier, F., Zhou, Y., Zhang, B., & Wang, T. (2021). Solid-state NMR of unlabeled plant cell walls: High-resolution structural analysis without isotopic enrichment. *Biotechnology for Biofuels, 14*(1), 14. https://doi.org/10.1186/s13068-020-01858-x

Zheng, Z., & Schmidt-Rohr, K. (2024). Phenolic syringyl end groups in 13C-enriched hardwoods detected and quantified by solid-state NMR. *Solid State Nuclear Magnetic Resonance, 133*, Article 101947. https://doi.org/10.1016/j.ssnmr.2024.101947

Zoghlami, A., & Paës, G. (2019). Lignocellulosic Biomass: Understanding Recalcitrance and Predicting Hydrolysis. *Frontiers in Chemistry, 7*, 874. https://doi.org/10.3389/fchem.2019.000874