Além do cloreto de colina insights de dinâmica molecular sobre solventes eutéticos profundos à base de cloreto de tetraetilamônio para a deslignificação de biomassa

Prévia do material em texto

Beyond choline chloride: Molecular dynamics insights into 
tetraethylammonium chloride-based deep eutectic solvents for 
biomass delignification
Sarmad Rizvi a, Hrushikesh M. Gade a,*
a Department of Chemical Engineering, Malaviya National Institute of Technology (MNIT), Jaipur, Rajasthan, India
A R T I C L E I N F O
Keywords:
Deep eutectic solvents
Biomass delignification
Green solvents
Molecular dynamics simulation
Lignin-carbohydrate complex
Tetraethylammonium chloride
A B S T R A C T
Rational design of deep eutectic solvents (DESs) for selective biomass fractionation requires molecular-level 
insights into how composition governs lignin-cellulose disruption. While choline chloride-based DESs domi-
nate research, the choline hydroxyl group strongly sequesters chloride ions, limiting anion-mediated delignifi-
cation. We present a systematic molecular dynamics investigation of tetraethylammonium chloride (TEACl)- 
based DESs, where the absence of hydroxyl functionality enhances chloride mobility, paired with urea (URE) or 
lactic acid (LAC) as hydrogen bond donors in binary systems, and 1,4-butanediol (BDO) in ternary formulations. 
All-atom simulations over 300 ns reveal distinct solvation mechanisms: urea-based DESs form stable solvation 
shells that moderately weaken cohesion, whereas lactic acid-based systems exhibit dynamic hydrogen-bond 
networks with high chloride recruitment, driving delignification through synergistic anion-HBD interactions. 
Ternary BDO incorporation further enhances chloride mobility and accelerates hydrogen-bond turnover. Criti-
cally, the TEACl:LAC:BDO system achieves the most pronounced reduction in lignin-lignin and cellulose-lignin 
interfacial bonds, significantly outperforming other formulations. Analyses establish that delignification effi-
ciency correlates more strongly with dynamic hydrogen-bond exchange and chloride accessibility than with bulk 
viscosity or static solvation strength. These findings provide a comprehensive computational framework 
demonstrating that non-choline quaternary ammonium-based DESs with acidic, polyol-modified formulations 
enable superior biomass fractionation through enhanced ionic participation. These structure-performance re-
lationships offer rational design principles for optimized green solvents in sustainable biorefinery applications.
1. Introduction
As fossil fuel reserves decline and global energy demand increases, 
increasing attention is directed towards renewable energy sources. 
Lignocellulosic biomass has emerged as a sustainable feedstock for 
bioenergy and bio-based chemicals [1], driving the need for efficient, 
cost-effective, and environmentally benign biomass conversion strate-
gies [2]. However, biomass utilization is impeded by the complex plant 
cell wall architecture, which consists of a dense network of interlaced 
polymers with distinct physicochemical properties and strong resistance 
to deconstruction [3]. Lignocellulosic biomass is primarily composed of 
cellulose, hemicellulose, and lignin, each offering high value utilization 
upon isolation. Among these, lignin poses the greatest challenge due to 
its cross-linked polyaromatic, amorphous, and hydrophobic nature. 
Lignin is laminated onto cell wall polysaccharides, forming lignin- 
carbohydrate complexes (LCCs) that further hinder biomass decon-
struction and valorization [4,5].
Industrial biofuel production typically begins with a pretreatment 
step to deconstruct LCCs, improving cellulose accessibility and reducing 
processing costs [6,7]. This is followed by enzymatic hydrolysis, which 
converts exposed polysaccharides into fermentable oligosaccharides for 
ethanol production [8]. Consequently, pretreatment has become a pri-
mary research focus for improving overall bioconversion efficiency.
Numerous pretreatment strategies have been developed to improve 
biomass digestibility and product yields. Conventional chemical 
methods, including dilute acid and alkaline pretreatments, target 
hemicellulose depolymerization and lignin removal, respectively, while 
organosolv and oxidative treatments promote lignin solubilization and 
depolymerization [9–12]. Physical approaches such as mechanical 
milling and steam explosion disrupt LCC integrity and reduce particle 
* Corresponding author.
E-mail address: hrushikesh.chem@mnit.ac.in (H.M. Gade). 
Contents lists available at ScienceDirect
International Journal of Biological Macromolecules
journal homepage: www.elsevier.com/locate/ijbiomac
https://doi.org/10.1016/j.ijbiomac.2026.152327
Received 25 January 2026; Received in revised form 28 April 2026; Accepted 29 April 2026 
International Journal of Biological Macromolecules 364 (2026) 152327 
Available online 30 April 2026 
0141-8130/© 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. 
size [13,14]. Physicochemical methods, including liquid hot water and 
AFEX, combine thermal and chemical effects to modify biomass struc-
ture [15,16], while biological pretreatment employs lignin-degrading 
microorganisms under mild conditions [17]. However, these methods 
often require high temperatures, involve corrosive or toxic reagents, and 
can induce lignin condensation, limiting downstream valorization. In 
contrast, green solvent-based approaches using deep eutectic solvents 
(DESs) and ionic liquids (ILs) provide environmentally friendly alter-
natives for lignocellulosic biomass fractionation [18]. Among them, 
DESs have gained increasing attention due to their simple preparation, 
low toxicity, biodegradability, and recyclability, offering advantages 
over often costly and potentially toxic ILs [19,20].
DESs are formed by combining a hydrogen bond donor (HBD) and a 
hydrogen bond acceptor (HBA) in a specific molar ratio, resulting in a 
eutectic mixture with reduced melting point due to extensive hydrogen 
bonding. First applied in biomass delignification in 2012, DESs 
demonstrated promising pretreatment performance [21]. Their primary 
objective in biomass processing is the selective extraction of lignin while 
preserving cellulose integrity [22,23]. A wide range of compounds serve 
as HBAs, with quaternary salts such as choline chloride (ChCl), tet-
raalkylammonium chloride, and betaine being most common, while 
HBDs include urea, amino acids, sugars, alcohols, amides, amines, and 
carboxylic acids [24]. Most studies have focused on ChCl-based DESs 
due to their low cost, biodegradability, and ease of preparation 
[22,23,25–27].
Although ChCl is the most widely used HBA in DESs formulation for 
biomass pretreatment, its cholinium cation bears a hydroxyl group that 
can participate in the DESs hydrogen-bond network. Mechanistic studies 
of DESs delignification highlight a key role of halide anions (chloride) in 
interacting with lignin functionalities and promoting bond cleavage/ 
structural disruption, thereby affecting delignification performance 
[22,28]. Structural studies of reline (ChCl-based) show that chloride is 
strongly associated with choline predominantly via hydrogen bonding 
with the choline hydroxyls, alongside extensive chloride-HBD in-
teractions, highlighting that the cation functionality can influence DESs 
structuring and dynamics [29]. In contrast, tetraalkylammonium salts 
such as tetraethylammonium chloride (TEACl) lack hydrogen bond 
donating functionality on the cation, accordingly, while electrostatic ion 
pairing remains present, the cation does not provide a hydroxyl- 
mediated hydrogen-bonding site to “lock” chloride into a persistent 
cation-anion hydrogen bond. Comparative analyses of cholinium- versus 
non-hydroxylated quaternary-ammonium-based DESs indicate that 
changing the cation can significantly alter the hydrogen-bond network 
and the resulting three-dimensional arrangement of constituents 
[30,31]. Despite emerging experimental reports of TEACl-based DESs 
pretreatment systems for lignocellulosic fractionation, these formula-improving enzymolysis efficiency via pretreatment 
using deep eutectic solvents, Bioresour. Technol. 376 (2023) 128937, https://doi. 
org/10.1016/j.biortech.2023.128937.
[62] Z. Wang, Y. Liu, K. Barta, P.J. Deuss, The effect of acidic ternary deep eutectic 
solvent treatment on native lignin, ACS Sustain. Chem. Eng. 10 (2022) 
12569–12579, https://doi.org/10.1021/acssuschemeng.2c02954.
[63] R. Schulz, B. Lindner, L. Petridis, J.C. Smith, Scaling of multimillion-atom 
biological molecular dynamics simulation on a Petascale supercomputer, J. Chem. 
Theory Comput. 5 (2009) 2798–2808, https://doi.org/10.1021/ct900292r.
[64] Z. Liu, Y. Hou, S. Hu, Y. Li, Possible dissolution mechanism of alkali lignin in lactic 
acid-choline chloride under mild conditions, RSC Adv. 10 (2020) 40649–40657, 
https://doi.org/10.1039/D0RA07808E.
[65] A.M. Da Costa Lopes, J.R.B. Gomes, J.A.P. Coutinho, A.J.D. Silvestre, Novel 
insights into biomass delignification with acidic deep eutectic solvents: a 
mechanistic study of β-O-4 ether bond cleavage and the role of the halide 
counterion in the catalytic performance, Green Chem. 22 (2020) 2474–2487, 
https://doi.org/10.1039/C9GC02569C.
[66] D. Smink, A. Juan, B. Schuur, S.R.A. Kersten, Understanding the role of choline 
chloride in deep eutectic solvents used for biomass delignification, Ind. Eng. Chem. 
Res. 58 (2019) 16348–16357, https://doi.org/10.1021/acs.iecr.9b03588.
[67] Y. Chen, Z. Tang, W. Tang, C. Ma, Y.-C. He, Exploration of biomass fractionation 
and lignin removal for enhancing enzymatic digestion of wheat-stalk through deep 
eutectic solvent Cetyl trimethyl ammonium chloride:Lactic acid treatment, Int. J. 
Biol. Macromol. 306 (2025) 141460, https://doi.org/10.1016/j. 
ijbiomac.2025.141460.
[68] A. Luzar, D. Chandler, Hydrogen-bond kinetics in liquid water, Nature 379 (1996) 
55–57, https://doi.org/10.1038/379055a0.
[69] M. Mohan, K.L. Sale, R.S. Kalb, B.A. Simmons, J.M. Gladden, S. Singh, Multiscale 
molecular simulation strategies for understanding the delignification mechanism of 
biomass in Cyrene, ACS Sustain. Chem. Eng. 10 (2022) 11016–11029, https://doi. 
org/10.1021/acssuschemeng.2c03373.
[70] R. Kandanelli, C. Thulluri, R. Mangala, P.V.C. Rao, S. Gandham, H.R. Velankar, 
A novel ternary combination of deep eutectic solvent-alcohol (DES-OL) system for 
synergistic and efficient delignification of biomass, Bioresour. Technol. 265 (2018) 
573–576, https://doi.org/10.1016/j.biortech.2018.06.002.
[71] S. Miao, A. Sardharwalla, S. Perkin, Ion diffusion reveals heterogeneous viscosity in 
nanostructured ionic liquids, J. Phys. Chem. Lett. 15 (2024) 11855–11861, https:// 
doi.org/10.1021/acs.jpclett.4c02996.
[72] A.V. Bayles, Anomalous solute diffusivity in ionic liquids: label-free visualization 
and physical origins, Phys. Rev. X 9 (2019), https://doi.org/10.1103/ 
PhysRevX.9.011048.
[73] S. Chatterjee, S.H. Deshmukh, S. Bagchi, Does viscosity drive the dynamics in an 
alcohol-based deep eutectic solvent? J. Phys. Chem. B 126 (2022) 8331–8337, 
https://doi.org/10.1021/acs.jpcb.2c06521.
[74] R. T., H. Srinivasan, V.K. Sharma, V.G. Sakai, S. Mitra, Role of molecular structure 
in defining the dynamical landscape of deep eutectic solvents, J. Chem. Phys. 162 
(2025) 244503, https://doi.org/10.1063/5.0266841.
[75] J.L. Wong, S.N.B.A. Khadaroo, J.L.Y. Cheng, J.J. Chew, D.S. Khaerudini, 
J. Sunarso, Green solvent for lignocellulosic biomass pretreatment: an overview of 
the performance of low transition temperature mixtures for enhanced bio- 
conversion, Next Mater. 1 (2023) 100012, https://doi.org/10.1016/j. 
nxmate.2023.100012.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
12tions remain less explored than cholinium-based systems, especially at 
the molecular-mechanistic level [32,33]. Recent efforts to maximize 
delignification efficiency have shifted from binary to ternary DESs for-
mulations [34,35], particularly those incorporating polyols. As multi-
functional alcohols, polyols leverage their dense hydroxyl networks to 
facilitate extensive hydrogen bonding, which has been shown to 
improve both biomass dissolution and subsequent enzymatic perfor-
mance [33,36]. However, the fundamental molecular pathways by 
which these ternary systems drive lignin-cellulose dissociation remain 
largely undefined. Bridging this mechanistic gap is critical for the 
rational development of next-generation solvents capable of superior 
lignin separation.
In this work, liquid-phase all-atom molecular dynamics (MD) simu-
lations were employed to investigate the physicochemical and interfa-
cial mechanisms governing delignification in TEACl-based DESs. TEACl 
was used as the HBA, with urea (URE) and lactic acid (LAC) as HBDs in 
binary DESs, and 1,4-butanediol (BDO) introduced as a secondary HBD 
in ternary systems; the chemical structures of all DES components are 
shown in Fig. 1(a). Bulk DES density was calculated and viscosity was 
evaluated using a time-decomposition Green-Kubo framework to vali-
date force-field parametrization and solvent transport properties. Sim-
ulations were then performed under experimentally relevant conditions 
to systematically compare binary and ternary DESs formulations under 
identical conditions and to assess how HBD chemistry and secondary 
HBD incorporation influence delignification. By correlating chloride 
recruitment, hydrogen-bond dynamics, and interfacial disruption with 
structural descriptors of lignin destabilization, this study delivers a 
mechanistic ranking of TEACl-based DESs in terms of delignification 
efficiency, providing molecular-level insights to interpret experimental 
observations and to guide the rational design of efficient DES-based 
biomass pretreatment systems.
2. Computational details
All MD simulations were performed using GROMACS version 2023.3 
[37]. All molecules were parametrized with the CHARMM36 force field 
[38]. Forcefield parameters for cellulose and DES components were 
generated using the CHARMM-GUI tool [39] and lignin parameters were 
obtained using SPRIG tool in combination with Lignin Builder [40,41].
2.1. Bulk deep eutectic solvent model development and property 
evaluation
Bulk DES systems based on TEACl were constructed to represent both 
binary and ternary formulations. The binary DES systems comprised 
TEACl paired with either LAC or URE as HBDs in the ratio 1:2. For 
ternary DES systems, the number of BDO molecules was calculated to 
achieve a 20 vol% composition relative to the total solvent volume. The 
1:2 HBA:HBD molar ratio was selected based on well-established 
eutectic compositions reported for quaternary ammonium chloride- 
Fig. 1. (a) Molecular structures of the HBA; tetraethylammonium chloride and HBDs; urea, lactic acid, and 1,4-butanediol, (b) Initial configuration (side and top 
views) of the lignin-cellulose composite model employed for all DES systems, showing lignin chains (blue, in colour) aggregated on the 36-chain cellulose fibril.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
2 
based DESs [42,43]. The selection of BDO and its 20 vol% composition 
was adopted from experimental diol-based DES systems [36] and 
ternary DES studies on bamboo fractionation [33] as a representative 
ternary system, in which the ternary modifier content was high enough 
to alter hydrogen-bond organization and chloride accessibility without 
overwhelming the primary DES network.
To validate the physicochemical properties of DES models, bulk 
solvent boxes were equilibrated independently prior to introducing 
biopolymer components. Energy minimization was followed by an NVT 
equilibration for 2 ns, and an NPT equilibration until density conver-
gence was achieved, at 300 K and 1 bar using PBC in all three directions. 
Temperature coupling was controlled via the V-rescale thermostat [44], 
and pressure was maintained using the C-rescale barostat [45]. Equili-
brated densities were obtained by averaging over the final portion of 
each trajectory after confirming the absence of systematic drift. These 
equilibrated densities served both as validation of the DES models and as 
the basis for subsequent viscosity calculations.
Shear viscosity of each DES system was computed using the Green-
–Kubo formalism combined with a time-decomposition strategy, 
following procedure proposed by Zhang et al. [46]. For each DES 
composition, N = 30 statistically independent NVT trajectories were 
generated at 300 K, each with a duration of 5 ns. Independent trajec-
tories were initiated using distinct initial velocity seeds to ensure un-
correlated sampling. The first 1 ns of each trajectory was discarded to 
eliminate transient effects, and the remaining segments were used for 
analysis.
For each trajectory, the running integral of viscosity was evaluated 
from the off-diagonal components of the pressure tensor. Ensemble- 
averaged viscosity 〈η(t)〉 and the corresponding time-dependent stan-
dard deviation σ(t) were then computed across all replicas. The growth 
of σ(t) was fitted to a power-law function of the form σ(t) = A tᵇ. The 
cutoff time (tcut) was defined as the time at which σ(t) reached 
approximately 40% of 〈η(t)〉, a criterion shown to yield reliable viscosity 
estimates for viscous liquids. The averaged running integral 〈η(t)〉 was 
subsequently fitted up to tcut using a weighted double-exponential 
function, and the long-time limit of this fit was taken as the final vis-
cosity value.
2.2. Molecular dynamics simulations of lignin-cellulose complex in DESs
The cellulose Iβ model system was constructed using the Cellulose- 
Builder tool [47], based on the experimental crystallographic data. As 
cellulose Iβ is the dominant structural form in plant cell walls, only this 
allomorph was considered. The crystalline cellulose fibril comprised 36 
chains, each containing 16 glucose monomers. Lignin models were 
generated with the Lignin Builder utility, supported by the SPRIG plat-
form [40,41]. Two predominant structural units, guaiacyl (G) and 
syringyl (S), derived from coniferyl and sinapyl alcohol precursors and 
commonly abundant in hardwood lignin [48], were incorporated. Lignin 
clusters consisted of eight chains, each averaging four residues, with G- 
and S- units covalently linked via β–O–4 bonds, the most prevalent 
linkage in natural lignin [49].
The lignin-cellulose complex (Fig. 1(b)) was generated by placing 
five lignin clusters around a central cellulose fibril in a simulation box 
measuring 10.8 × 10.8 × 10.8 nm3. A 20 ns NPT simulation was per-
formed to facilitate noncovalent interactions, allowing lignin adsorption 
onto the cellulose surface and form a representative complex for sub-
sequent DES simulations. The cellulose-to-lignin mass ratio (1:0.52) was 
chosen to approximate the experimentally observed ratios in natural 
plant biomass [50].
The lignin-cellulose complex was then solvated using equilibrated 
DES boxes from Section 2.1. Solvent molecules were inserted while 
minimizing atomic overlaps, followed by removal of overlapping mol-
ecules using the standard GROMACS solvation procedure. Solvated 
systems were neutralized, and periodic boundary conditions were 
applied. Energy minimization was performed using the steepest descent 
algorithm until the maximum force was below 500 kJ/mol/nm. Equil-
ibration was carried out under NVT (5 ns) and NPT (10 ns) ensembles at 
373.15 K and 1 bar, with position restraintsapplied to the heavy atoms 
of cellulose and lignin. Production simulations were then performed in 
the NPT ensemble for 300 ns at 373.15 K and 1 bar, with all restraints 
removed. Temperature and pressure were controlled via the V-rescale 
thermostat [44] and the C-rescale barostat [45]. Long-range electro-
static interactions were treated using the PME method [51], and van der 
Waals and coulombic interactions truncated at 1 nm. A 2-fs integration 
step was used, with all covalent bonds involving hydrogen constrained 
using LINCS [52]. Trajectory analysis was performed using VMD, 
GROMACS in-built commands and the MDAnalysis package [53,54].
3. Results and discussion
3.1. Physicochemical validation of TEACl-based DES systems
Prior to investigating complex lignin-cellulose interactions, the bulk 
density and shear viscosity of the TEACl-based DESs were evaluated to 
ensure the simulated solvent environments were structurally and 
dynamically stable. As shown in Fig. S1, bulk densities for all four sys-
tems converged during NPT equilibration at 300 K, confirming that the 
force-field parameters effectively represent well-equilibrated structures. 
The resulting density values (Table 1) fall within the expected ranges for 
quaternary ammonium chloride-based DESs and serve as a reliable 
baseline for transport property calculations.
The shear viscosity was computed using the time-decomposition 
Green-Kubo framework detailed in Section 2.1. Fig. 2(a) illustrates the 
ensemble-averaged running integral of viscosity, 〈η(t)〉, which ap-
proaches the characteristic plateau required for reliable estimation in 
viscous liquids. The statistical reliability of these values is supported by 
the time-dependent standard deviation, σ(t), which exhibits systematic 
power-law growth (Fig. 2(b)), enabling the objective determination of 
the integration cutoff time (tcut).
The computed viscosities, summarized in Table 1, reveal distinct 
transport behaviors influenced by HBD chemistry and polyol incorpo-
ration. While the addition of BDO as a secondary HBD consistently 
decreased the density of both binary systems, it had diverging effects on 
their viscosity. Specifically, the viscosity of the TEACl:LAC system 
decreased upon BDO addition, a trend typical of co-solvent incorpora-
tion which reduces viscosity of DES system [56]. Conversely, the TEACl: 
URE system exhibited a marked increase in viscosity upon the addition 
of BDO. The viscosity increase in the URE-based ternary system can be 
attributed to the high cohesive energies and the formation of an exten-
sive intermolecular hydrogen-bond network that arises when polyols 
interact with amide-based HBDs [57,58]. Such findings highlight that 
the internal structuring of the DES is highly sensitive to the HBD's ability 
to participate in the local hydrogen-bond matrix. Overall, the density 
and viscosity results demonstrate that the DES systems are physically 
well-behaved and exhibit transport properties characteristic of strongly 
hydrogen-bonded eutectic systems. This independent validation pro-
vides a reliable foundation for interpreting the molecular-scale behavior 
of lignin-cellulose complexes in these solvent environments. Notably, 
the TEACl:URE:BDO system, despite its higher viscosity relative to 
TEACl:URE, achieves better delignification performance in the subse-
quent simulations, suggesting that bulk viscosity alone does not deter-
mine delignification efficacy, a point discussed mechanistically in 
Section 3.3.5.
3.2. Structural dynamics of lignin-cellulose complex in DES environments
The dynamic evolution of the lignin-cellulose complex across all DES 
systems is illustrated in Fig. 3, showing top and side views at 10, 150, 
and 300 ns. The sequence highlights the progressive disruption of lignin- 
cellulose contacts and increased lignin dispersion over time, consistent 
with the subsequent structural dynamics trends. In TEACl:URE (Fig. 3
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
3 
(a)), lignin remains largely adhered to the cellulose surface, indicating 
limited delignification. TEACl:LAC (Fig. 3(b)) exhibits partial swelling 
of lignin chains. The introduction of 1,4-butanediol in TEACl:URE:BDO 
(Fig. 3(c)) leads to noticeable lignin expansion and partial detachment 
from cellulose. The most pronounced separation occurs in TEACl:LAC: 
BDO (Fig. 3(d)), where lignin fragments are visibly displaced from the 
cellulose fibril by 300 ns. These observations show that lactic acid-based 
DESs, particularly with polyol additives, promote sustained disruption 
of the lignin-cellulose interface in comparison to other DES systems. 
While these visual snapshots provide a compelling qualitative overview 
of the progressive lignin-cellulose interface disruption, they represent 
the cumulative result of complex, underlying molecular forces. To un-
cover the mechanistic ‘why’ behind these distinct performance profiles, 
specifically why the lactic acid-based ternary system so significantly 
facilitates lignin detachment while urea-based systems remain largely 
adhesive, it is necessary to move beyond structural observations. In the 
following sections, we dissect the structural as well as intermolecular 
interactions to isolate the specific molecular determinants that drive this 
observed delignification.
3.2.1. Root mean square deviation analysis
The structural stability of the LCC across different DES environments 
was evaluated using the root mean square deviation (RMSD) of cellulose 
fibrils and lignin chains over 300 ns production trajectories. Distinct 
trends depending on the type of HBD were observed, shown in Fig. 4 and 
Table 2.
3.2.1.1. Cellulose. In the Urea based binary DES, cellulose RMSD sta-
bilized rapidly within the first ~50 ns and remained confined to a nar-
row range ~ (0.09–0.11 nm) for the remainder of the trajectory, 
indicating minimal perturbation on the cellulose structure. In contrast, 
TEACl:LAC displayed a gradual RMSD increase up to ~0.21 nm at ~230 
ns, followed by slight relaxation, suggesting stronger solvent–surface 
interactions. The addition of BDO accentuated these effects: in TEACl: 
URE:BDO, RMSD increased more gradually to ~0.21 nm by ~260 ns, 
whereas in TEACl:LAC:BDO, RMSD rose sharply within ~70 ns and then 
relaxed to a broader range (~0.15–0.17 nm). Despite solvent-dependent 
variations, overall deviations remained small and did not significantly 
alter cellulose structure, consistent with previous reports [3,59].
3.2.1.2. Lignin. Lignin exhibited consistently higher RMSD values than 
cellulose, reflecting its greater susceptibility to DES solvation [60,61]. 
Quantitatively, these RMSD trends confirm the rank order visually 
established in Fig. 3, with TEACl:LAC:BDO showing the most pro-
nounced structural deviation. In all systems, RMSD increased gradually 
over time, indicating persistent conformational dynamics without full 
convergence. In TEACl:URE, lignin RMSD increased modestly from 
~0.34 to ~0.38 nm after ~75 ns, suggesting moderate flexibility. 
TEACl:LAC induced stronger rearrangements, with RMSD rising steadily 
to ~0.70 nm by 300 ns. BDO further enhanced these effects: in TEACl: 
URE:BDO, RMSD equilibrated near ~0.50–0.51 nm after ~250 ns, while 
TEACl:LAC:BDO showed the largest deviations, reaching ~1.1 nm 
without convergence. The significant rise in lignin RMSD within acidic 
and polyol-modified environments aligns with established findings that 
these solvent classes effectively broaden lignin conformational ensem-
bles [33,62].
The rising RMSD of lignin in LAC-based DES systems, specifically 
TEACl:LAC:BDO system warrants explicit discussion. This behavior re-
flects the timescale challenge inherent to simulating thedissolution of 
large, flexible biopolymers: lignin is an amorphous, cross-linked 
macromolecule whose conformational reorganization under strong sol-
vation forces can extend well beyond sub-microsecond simulation 
windows, a recognized limitation of all-atom MD studies of polymer 
dissolution [63]. Crucially, the non-convergence does not undermine 
the mechanistic conclusions of this study for two reasons. First, the 
relative ranking of all four DES formulations is established clearly and 
consistently across all the computed structural and energetic de-
scriptors, RMSD, SASA, hydrogen-bond populations, RDFs, and inter-
action energies, which show mutually consistent trends regardless of the 
absolute equilibrium state of the TEACl:LAC:BDO trajectory. Second, the 
Table 1 
Physicochemical properties of TEACl-based binary and ternary DESs at 300 K, including computed density; power-law exponent (b), cutoff time (tcut), and shear 
viscosity obtained using the Green-Kubo time-decomposition approach.
DES system Computed density (g. 
cm− 3)
Power-law exponent 
(b)
Cutoff time (tcut, 
ps)
Computed shear viscosity 
(mPa.s)
Literature benchmark (near 300 
K)
TEACl:URE (1:2) 1.151 ± 0.007 0.4714 747.01 80.87 *
TEACl:LAC (1:2) 1.083 ± 0.006 0.5747 978.88 199.64 Ref. [55]
TEACl:URE:BDO (1:2:20 vol 
%)
1.126 ± 0.007 0.4858 605.84 175.68 *
TEACl:LAC:BDO (1:2:20 vol 
%)
1.074 ± 0.007 0.5719 781.69 154.07 *
* Literature benchmarks for these DESs near 300 K are unavailable.
Fig. 2. Calculated results using the time decomposition method for the DESs. All results are based on 30 independent trajectories. (a) the calculated averaged 
running integral; (b) the calculated standard deviation.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
4 
Fig. 3. Time-evolution snapshots of the lignin–cellulose complex in four TEACl-based DES systems: (a) TEACl:URE, (b) TEACl:LAC, (c) TEACl:URE:BDO, and (d) 
TEACl:LAC:BDO, captured at 10 ns, 150 ns, and 300 ns.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
5 
continuously rising RMSD is itself mechanistically meaningful: it is 
corroborated by the simultaneously unsaturated SASA trajectory 
(Section 3.2.2), the highest chloride recruitment (Section 3.3.1), and the 
lowest CEL–LIG interaction energy among all systems (Section 3.3.3). 
Taken together, these descriptors provide convergent evidence that 
TEACl:LAC:BDO achieves the most pronounced lignin destabilization, 
irrespective of whether the RMSD has reached a final plateau within 
300 ns. We therefore interpret the non-convergence as indicative of an 
active, ongoing solvation process, one that is consistent with the supe-
rior delignification performance of this formulation, rather than as a 
failure of the simulation to sample the relevant conformational space.
3.2.2. Solvent accessible surface area evolution
To complement the RMSD analysis, solvent accessible surface area 
(SASA) was calculated for both cellulose and lignin in all DES environ-
ments to assess changes in surface exposure to the solvent (Fig. 5 & 
Table 2).
3.2.2.1. Cellulose. SASA values across all four DES systems remained 
essentially flat, confined to the narrow range of ~190–200 nm2 
throughout the 300-ns trajectories. This invariance reflects the robust-
ness of the crystalline fibril structure in DES environments and is 
consistent with the minimal RMSD fluctuations observed, indicating 
resistance to solvent-induced expansion or collapse regardless of DES 
composition.
3.2.2.2. Lignin. In contrast, lignin exhibited pronounced solvent- 
dependent SASA increases in all systems. In TEACl:URE, SASA 
increased modestly from ~210 to ~225 nm2, indicating limited surface 
expansion. The addition of BDO led to a larger increase to ~250 nm2 
within 150 ns, followed by partial equilibration. TEACl:LAC induced a 
sharper rise, with SASA stabilizing in the 260–270 nm2 range after 
~150 ns, suggesting persistent solvent penetration. The largest effect 
occurred in TEACl:LAC:BDO, where SASA increased continuously 
throughout the simulation, reaching ~325 nm2 by 300 ns without 
saturation.
The continuous SASA expansion observed for lignin in the ternary 
lactic acid system mirrors previous reports of enhanced lignin solubili-
zation in acidic DESs relative to urea-based formulations [27,60,64]. 
These results confirm that the synergy between acidic HBDs and polyol 
co-solvents promotes greater solvent penetration and surface exposure 
of the lignin polymer. The molecular basis for this synergy, specifically 
the combined role of lactic acid's dynamic H-bond exchange and BDO- 
enhanced chloride recruitment, is examined in Sections 3.3.1 and 3.3.2.
3.2.3. Quantitative comparison and convergence assessment of RMSD and 
SASA
To provide a quantitative basis for comparing structural dynamics 
across systems, mean RMSD and SASA values were computed over the 
final 50 ns of each production trajectory, with standard deviations re-
ported as measures of conformational fluctuation (Table 2).
For cellulose, mean RMSD values remained low across all systems: 
0.106 ± 0.003 nm (TEACl:URE), 0.172 ± 0.009 nm (TEACl:LAC), 0.189 
± 0.007 nm (TEACl:URE:BDO), and 0.171 ± 0.008 nm (TEACl:LAC: 
BDO), confirming that the crystalline fibril structure is preserved irre-
spective of DES composition, consistent with the narrow cellulose SASA 
range of 194.8–196.6 nm2 observed across all systems. For lignin, mean 
RMSD values follow the delignification ranking directly: 0.376 ± 0.002 
nm (TEACl:URE), 0.651 ± 0.020 nm (TEACl:LAC), 0.513 ± 0.005 nm 
(TEACl:URE:BDO), and 1.078 ± 0.055 nm (TEACl:LAC:BDO), repre-
senting a 187% increase from the least to most active formulation. The 
narrow standard deviations for TEACl:URE (±0.002 nm) and TEACl: 
URE:BDO (±0.005 nm) confirm plateau convergence in urea-based 
systems, while the larger deviation in TEACl:LAC:BDO (±0.055 nm) 
reflects the ongoing delignification dynamics discussed earlier. Lignin 
SASA values in the final 50 ns similarly follow the same rank order: 
226.0 ± 0.8 nm2 (TEACl:URE), 271.9 ± 2.0 nm2 (TEACl:LAC), 251.1 ±
2.6 nm2 (TEACl:URE:BDO), and 311.4 ± 6.8 nm2 (TEACl:LAC:BDO), 
Fig. 4. RMSD profiles of cellulose and lignin in binary and ternary TEACl-based 
DESs. (CEL_URE represents the RMSD of cellulose in the TEACl:URE DES system. 
Additional legend labels follow the same naming convention, where CEL and LIG 
refer to cellulose and lignin, respectively, and URE, LAC, and BDO denote HBDs 
present in the DESs.)
Table 2 
Mean RMSD and SASA values of cellulose and lignin in DES systems, averaged 
over the final 50 ns of production trajectories, with standard deviations.
System RMSD mean (nm) SASA mean (nm2)
CEL_URE 0.106 ± 0.003 195.6 ± 0.4
CEL_LAC 0.172 ± 0.009 196.6 ± 0.9
CEL_URE_BDO 0.189 ± 0.007 194.8 ± 0.6
CEL_LAC_BDO 0.171 ± 0.008 195.8 ± 0.8
LIG_URE 0.376 ± 0.002 226.0 ± 0.8
LIG_LAC 0.651 ± 0.020 271.9 ± 2.0
LIG_URE_BDO 0.513 ± 0.005 251.1 ± 2.6
LIG_LAC_BDO 1.078 ± 0.055 311.4 ± 6.8
Fig. 5. SASA evolution of cellulose and lignin in binary and ternary TEACl- 
based DESs. (Legend labels follow the same convention as in Fig. 4, where CEL 
and LIG denote cellulose and lignin, respectively, and URE, LAC, and BDO corre-
spond to HBDs in DESs.)
S. Rizvi and H.M. Gade InternationalJournal of Biological Macromolecules 364 (2026) 152327 
6 
representing a 38% increase from TEACl:URE to TEACl:LAC:BDO. The 
consistency between RMSD and SASA trends across all four systems 
provides strong statistical support for the mechanistic ranking 
confirmed by H-bond population dynamics (Section 3.3.1), H-bond 
lifetime analysis (Section 3.3.2), and interaction energetics (Section 
3.3.3).
3.3. Intermolecular interactions and mechanism
3.3.1. Hydrogen bonding population dynamics
Hydrogen bonding is the fundamental force governing how DESs 
interact with and dismantle lignin-cellulose assemblies. The RMSD and 
SASA trends established in Section 3.2, notably the 187% greater lignin 
RMSD and 38% greater SASA in TEACl:LAC:BDO relative to TEACl:URE, 
reflect the cumulative outcome of distinct hydrogen-bond interaction 
patterns described below. Notably, the TEA+ cation demonstrated no 
hydrogen bonds with either lignin or cellulose, confirming that its pri-
mary role is to facilitate halide ion mobility rather than participating 
directly in the hydrogen-bond network.
3.3.1.1. Binary DES systems. In the TEACl:URE (Fig. 6(a)), lignin 
rapidly forms a solvation shell characterized by high urea interaction 
counts (~150–200) and chloride-lignin bonds that increase gradually 
from ~25 → 90. This leads to moderate interface disruption, with 
cellulose-lignin (CEL–LIG) bonds decreasing from ~35 to ~27, and 
LIG–LIG self-association declined rapidly from 250 → 200, before sta-
bilizing around 180–200. In contrast, the TEACl:LAC system exhibits a 
more dynamic binding environment (Fig. 6(b)). Although it maintains 
fewer LIG–HBD bonds than URE-based binary system (~60–120), it 
facilitates significantly higher chloride infiltration, with populations 
reaching 150 bonds. This enhanced anion participation drives sustained 
lignin destabilization, evidenced by a continuous decline in LIG-LIG self- 
association (260 → 170), and CEL-LIG interpolymer cohesion (35 → 25).
3.3.1.2. Ternary DES systems. The addition of 1,4-butanediol (BDO) as a 
secondary HBD creates a cooperative environment that amplifies 
delignification. In TEACl:URE:BDO (Fig. 6(c)), lignin formed stable 
LIG–BDO interactions (45–60), while LIG–URE interactions decreased 
slightly relative to the binary system due to partial competition. Com-
bined urea and BDO interactions (170–210) marginally exceeded the 
binary case. LIG–Cl− interactions increased to ~90, LIG–LIG declined 
from 260 → 175, and CEL–LIG bonds decreased from 33 → 22, indi-
cating enhanced but ultimately moderate delignification. The most 
significant impact is observed in the TEACl:LAC:BDO system (Fig. 6(d)), 
where chloride-lignin (LIG–Cl) interactions reach their maximum at 
160 bonds, the highest among all studied formulations. LIG–BDO in-
teractions ranged from 25 to 50, while LIG–LAC interactions fluctuated 
between 75 and 100. As a result, LIG–LIG bonds declined from 260 → 
140, and CEL–LIG bonds decreased from 32 → 16, representing the 
greatest interface disruption among all formulations.
3.3.1.3. Comparative insights. Although urea-based systems exhibit 
higher HBD–lignin bond counts than lactic acid systems, delignification 
efficiency follows the order: TEACl:UREslowly-exchanging solvation 
shells around lignin hydroxyl groups. This “static solvation” is consistent 
with the high but persistent LIG–URE bond populations observed in 
Fig. 6(a) and explains why urea-based systems achieve only moderate 
delignification despite high HBD–lignin bond counts. In sharp contrast, 
LIG–LAC bonds in TEACl:LAC display τ₂ = 43.1 ps, a 6-fold reduction 
relative to urea, confirming that lactic acid engages in rapid, high- 
turnover interactions with lignin. This dynamic exchange continuously 
frees lignin hydroxyl groups for subsequent interaction with chloride 
ions, driving sustained interfacial disruption.
The role of chloride ion dynamics further reinforces this picture. 
LIG–Cl− bond lifetimes follow the delignification ranking directly: τ₂ =
150 ps (TEACl:URE) > 121.8 ps (TEACl:LAC) > 107.8 ps (TEACl:URE: 
BDO) > 82.5 ps (TEACl:LAC:BDO). The progressive reduction in chlo-
ride bond lifetime across this series indicates that chloride participates 
more dynamically in the best-performing systems, penetrating and 
withdrawing from lignin-rich regions with higher frequency rather than 
forming stable, sequestered complexes. This is consistent with the 
chloride RDF peak heights (Fig. 8) and LIG–Cl− population trends 
(Fig. 6), establishing a coherent mechanistic picture.
The incorporation of 1,4-butanediol as a secondary HBD accelerates 
bond exchange across both solvent families. In the URE-based ternary 
system, LIG–URE τ₂ decreases from 260 to 229 ps and LIG–Cl− τ₂ from 
150 to 107.8 ps, with BDO itself forming moderate-lifetime interactions 
(τ₂ = 138.1 ps). In the LAC-based ternary system, LIG–LAC τ₂ decreases 
further to 38.5 ps and LIG–Cl− τ₂ to 82.5 ps, while LIG–BDO bonds 
exhibit τ₂ = 64.5 ps, shorter than BDO bonds in the URE system, 
consistent with the more dynamic overall hydrogen-bond environment. 
Taken together, these lifetime data provide direct kinetic evidence that 
the TEACl:LAC:BDO system achieves the highest hydrogen-bond turn-
over across all interaction types, corroborating the delignification 
ranking established by structural and energetic analyses.
3.3.3. Interaction energetics
To further dissect the molecular basis of lignin-cellulose stability 
across DES environments, we analyzed non-bonded interaction energies 
(Lennard-Jones and Coulombic) between cellulose, lignin, and individ-
ual solvent components Table 4 and Fig. S3 in SI). The resulting data 
reveals a clear energetic hierarchy that complements the structural and 
interaction dynamics discussed previously.
3.3.3.1. Cellulose. Across all systems, cellulose-Cl− electrostatic in-
teractions dominated, ranging from − 9996 kJ mol− 1 in TEACl:URE to 
− 11,686 kJ mol− 1 in TEACl:LAC. The stronger stabilization in lactic acid 
DESs indicates enhanced ionic coordination near cellulose hydroxyls, 
consistent with the pronounced first Cl− solvation shell at ~0.2 nm 
observed in RDFs (Fig. S4). Addition of BDO slightly weakened this 
interaction (− 9293 kJ mol− 1), suggesting partial anion shielding 
through secondary hydrogen bonding.
3.3.3.2. Lignin. The lignin-HBD and lignin-Cl− interactions followed a 
similar but more intensified trend, with lactic acid systems displaying 
the strongest cumulative attraction. While lignin-urea interactions 
Table 3 
Slow relaxation times (τ₂) from bi-exponential fits to LIG–DES component 
hydrogen-bond autocorrelation functions. Smaller τ₂ indicates faster bond ex-
change and higher dynamic turnover.
Interaction TEACl: 
URE
TEACl: 
LAC
TEACl:URE: 
BDO
TEACl:LAC: 
BDO
LIG–URE τ₂ (ps) 260 – 229 –
LIG–LAC τ₂ (ps) – 43.1 – 38.5
LIG–Cl− τ₂ (ps) 150 121.8 107.8 82.5
LIG–BDO τ₂ (ps) – – 138.1 64.5
Table 4 
Non-bonded interaction energies (Lennard-Jones/Coulombic) between lignin, 
cellulose, and DES components. Negative values indicate stabilizing in-
teractions, confirming hierarchical lignin–solvent affinities.
Interaction 
(kJ mol− 1)
TEACl: 
URE (1:2)
TEACl:URE: 
BDO (1:2 +
20% BDO)
TEACl:LAC 
(1:2)
TEACl:LAC: 
BDO (1:2 +
20% BDO)
CEL–LIG − 1279 / 
− 869
− 1135 / − 806 − 1096 / 
− 840
− 1009 / − 785
CEL–TEA − 3373 / 
− 4400
− 2694 / 
− 2312
− 3136 / 
− 4998
− 2852 / 
− 7409
CEL–Cl− +515 / 
− 9996
+506 / 
− 11,601
+767 / 
− 11,686
+795 / − 9293
CEL–HBD URE: 
− 2620 / 
− 5557
URE: − 2090 / 
− 4593 
BDO: − 1561 / 
− 1420
LAC: 
− 2973 / 
− 2763
LAC: − 2396 / 
− 2183 
BDO: − 1007 / 
− 918
LIG–TEA − 3739 / 
− 23
− 2981 / 
+1734
− 3458 / 
+526
− 3112 / +873
LIG–Cl− +303 / 
− 9167
+304 / − 9667 +632 / 
− 11,207
+577 / 
− 10,832
LIG–HBD URE: 
− 2790 / 
− 3236
URE: − 2177 / 
− 2901 
BDO: − 2469 / 
− 1564
LAC: 
− 3988 / 
− 1891
LAC: − 3698 / 
− 2020 
BDO: − 1611 / 
− 1008
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
8 
declined upon the introduction of BDO, the Coulombic contributions to 
lignin-lactic acid interactions actually increased in the ternary system, 
demonstrating superior acid-lignin stabilization. Furthermore, lignin- 
Cl− electrostatics were significantly more robust in lactic acid DESs 
(− 11,207 kJ mol− 1) compared to urea-based formulations (− 9167 kJ 
mol− 1). This difference is consistent with the experimentally observed 
superior affinity of acidic DES constituents for lignin reported by Guo 
et al. [27]. Such trends are consistent with established theories that 
enhanced LJ interaction strength between a solvent and lignin facilitates 
polymer swelling and chain exposure, thereby increasing the SASA [69].
Comparison of cellulose-lignin and lignin-DES interaction energies 
shows that delignification efficacy is governed by the balance between 
interpolymer cohesion (CEL_LIG) and solvent stabilization (LIG_DES) 
(Fig. 7). In urea-based systems, relatively strong cellulose-lignin in-
teractions (− 2148.19 kJ mol− 1) and moderate lignin-DES stabilization 
limited lignin disruption. Addition of BDO weakened cellulose-lignin 
binding (− 1941.69 kJ mol− 1) by enhancing lignin-DES interactions, 
particularly lignin-BDO contacts, explaining the intermediate increases 
in RMSD and SASA observed in Section 3.2. In contrast, LAC-based bi-
nary system exhibited stronger lignin-DES stabilization and weaker 
cellulose-lignin interactions (− 1936.14 kJ mol− 1), driving extensive 
lignin restructuring. The TEACl:LAC:BDO system emerged as the most 
effective formulation because it achieved the strongest lignin-DES sta-
bilizing contacts while simultaneously reducing cellulose-lignin binding 
(− 1793.61 kJ mol− 1) to the lowest values recorded across all systems. 
This progressive weakening of CEL–LIG binding energy (− 2148 → 
− 1793 kJ mol− 1) is the thermodynamic underpinning of the declining 
CEL–LIG hydrogen-bond populations documented in Section 3.3.1, 
where bond counts fell by 23–50% across the four systems, and provides 
the fundamental “why” behind the system's superior performance in 
facilitating extensive lignin restructuring and detachment.
3.3.4. Radial distribution function (RDF) analysis
To examine how DES composition influences local solvation, RDFs 
were calculated between cellulose hydroxyl hydrogens (HO2, HO3, 
HO6; Fig. S4 in SI) and lignin hydroxyl hydrogens (Fig. 8) with key DES 
constituents: chloride anion (CLA), HBDs (urea N/O: URE_NO; lactic 
acid O: LAC_O; 1,4-butanediol O: BDO_O), and the TEA cationic center 
(TEA_N).
3.3.4.1. RDF around lignin hydroxyl hydrogens. Lignin RDFs exhibit 
more pronounced first solvation peaks for Cl− than cellulose, indicating 
stronger anion–phenolic hydroxyl interactions. The Cl− peak is highest 
in TEACl:LAC:BDO (~9), followed by TEACl:LAC (~8), and decreases to 
~5 in TEACl:URE:BDO and ~ 4 in TEACl:URE, showing that acidic 
DESs, particularly with polyol, promote tighter ionic coordinationwith 
lignin hydroxyls. Secondary peaks for LAC_O (~0.23 nm) and URE_NO 
(~0.22 nm) sharpen in ternary DESs, suggesting that BDO enables closer 
approach of HBD species to lignin hydroxyl groups. A modest BDO_O 
peak (~0.20–0.22 nm) appears in both ternary systems, indicating that 
BDO can act as a hydrogen-bond donor to lignin. TEA_N remains weakly 
structured at larger distances, consistent with its limited direct role in 
lignin solvation.
3.3.4.2. Comparative mechanistic insights. The RDFs highlight an inter-
action hierarchy:
Cl− ≫ HBD (LAC_O / URE_NO / BDO_O) ≫ TEA_N.
TEACl:LAC show stronger ionic structuring around cellulose and 
lignin than TEACl:URE. Previous studies report that adding a third DES 
component aids lignin solubility [61,70]. Accordingly, BDO addition 
further strengthens these interactions and allows for closer packing of 
ions and HBDs near biomass hydroxyls. These structural observations 
are consistent across all levels of analysis: the tighter Cl− coordination 
seen in RDFs directly explains the higher chloride H-bond populations 
(Fig. 6, Section 3.3.1), the shorter LIG–Cl− lifetimes (Table 3, Section 
3.3.2), and the more negative LIG–Cl− Coulombic energies in lactic acid 
systems (Table 4, Section 3.3.3).
3.3.5. Distinguishing bulk viscosity from molecular delignification 
mechanisms
Bulk viscosity is expected to influence DESs pretreatment primarily 
by controlling mass-transport timescales such as solvent penetration and 
diffusion, but it does not uniquely define local molecular interactions. 
Specifically, TEACl:URE:BDO (175.68 mPa⋅s, Table 1) outperforms 
TEACl:URE (80.87 mPa⋅s) in delignification despite being more than 
twice as viscous, and TEACl:LAC:BDO (154.07 mPa⋅s) outperforms 
TEACl:URE:BDO despite lower viscosity, suggesting that the relation-
ship between viscosity and delignification is non-monotonic and gov-
erned by molecular-level interactions rather than bulk transport 
properties. Previous studies have shown that ion diffusion in nano-
structured ionic liquids can deviate from the Stokes-Einstein relation, 
illustrating that viscosity and microscopic transport can decouple in 
complex liquids like ILs and DESs [71,72]. In DESs, viscosity reflects the 
collective resistance to flow of an extended hydrogen-bond network and 
arises from heterogeneous local environments rather than homogeneous 
solvation structures. This interpretation is supported by studies high-
lighting nanoscale dynamic heterogeneity in DESs that modulate 
transport properties independently of bulk viscosity [73,74].
At the molecular level, delignification is governed more directly by 
local solvation structure, hydrogen-bond dynamics, and the accessibility 
of reactive species, particularly halide anions and HBDs, at lignin-lignin 
and lignin-cellulose interfaces. Mechanistic studies of DES- and IL- 
mediated biomass fractionation consistently show that effective lignin 
disruption correlates with anion penetration and dynamic hydrogen- 
bond rearrangement rather than with viscosity alone [65,75]. Accord-
ingly, when interpreting DESs performance for biomass delignification, 
viscosity should be treated as a factor that modulates kinetics and 
transport, whereas local solvation structure, hydrogen-bond rearrange-
ment, and ion accessibility at lignin-cellulose interface provides the 
more direct molecular-level determinants of delignification efficiency, 
as evidenced by the enhanced delignification observed in the more 
viscous DES systems relative to TEACl:URE in this study.
4. Conclusions
This work provides a comprehensive molecular-level understanding 
of lignin-cellulose disruption in TEACl-based DES systems. Across four 
Fig. 7. Interaction energies for cellulose–lignin and lignin–DES pairs across 
four DES systems. Interaction energy dominates between lignin-DES, particu-
larly in lactic acid and BDO-containing systems.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
9 
TEACl formulations, the simulations show that delignification efficiency 
is governed not by hydrogen-bond abundance alone, but by the coupled 
effects of (i) dynamic hydrogen-bond turnover and (ii) chloride-ion 
recruitment and penetration into lignin-rich regions. Although urea- 
based DESs form a larger number of persistent HBD-lignin hydrogen 
bonds, this “static solvation” produces only moderate weakening of 
lignin-lignin cohesion and limited disruption of the cellulose-lignin 
interface. In contrast, lactic acid-based DESs promote more dynamic 
interfacial rearrangement and substantially stronger lignin-Cl− coordi-
nation, resulting in sustained lignin disaggregation and progressive loss 
of cellulose-lignin contacts. The inclusion of 1,4-butanediol as a sec-
ondary HBD further enhances chloride accessibility and hydrogen bond 
reorganization, resulting in superior delignification capacity. The 
TEACl:LAC:BDO system, in particular, achieves the most pronounced 
lignin-lignin dissociation and cellulose-lignin interface disruption, 
correlating with its highest ionic participation and hydrogen bond 
turnover.
These mechanistic trends revealed herein provide practical solvent- 
design rules for TEACl-based delignification systems: (1) acidic HBDs 
are favored to maximize chloride recruitment and interfacial disruption 
of lignin cohesion; (2) polyol co-HBD incorporation (e.g., ~20 vol% 
BDO) promotes hydrogen-bond turnover and improves anion access, 
strengthening delignification propensity; and (3) bulk viscosity should 
be treated primarily as a kinetic/transport descriptor rather than a direct 
predictor of delignification effectiveness. Collectively, these insights 
underline the critical importance of HBD type and cooperative HBD- 
anion interactions in tailoring DESs formulations for biomass 
fractionation.
We acknowledge that the computational findings presented here 
would benefit from direct experimental validation through time- 
resolved spectroscopy, calorimetric characterization of DES-lignin in-
teractions, or quantitative lignin removal assays for the specific TEACl: 
LAC:BDO formulation. The lignin model used here, while simplified 
relative to native polydisperse lignin, captures the dominant β-O-4 
linkages and G/S monomer distribution characteristic of hardwood 
biomass. Future work should extend these simulations to larger, poly-
disperse lignin models and validate the predicted chloride-recruitment 
hierarchy against experimental ion activity measurements.
CRediT authorship contribution statement
Sarmad Rizvi: Writing – review & editing, Writing – original draft, 
Visualization, Software, Methodology, Investigation, Formal analysis, 
Data curation, Conceptualization. Hrushikesh M. Gade: Writing – re-
view & editing, Supervision, Resources, Project administration.
Declaration of Generative AI and AI-assisted technologies in the 
writing process
During the preparation of this work the authors used ChatGPT in 
order to improve language and readability. After using this tool/service, 
the authors reviewed and edited the content as needed and take full 
responsibility for the content of the published article.
Funding sources
This research did not receive any specific grant from funding 
agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper.
Appendix A. Supplementary data
DES box density convergence; cellulose-DES hydrogen-bonding 
analysis; LJ and coulombic interaction energy components; RDFs of 
cellulose with HBDs and chloride anions. Supplementary data to this 
Fig. 8. RDFs of lignin hydroxylhydrogens versus DES components in (a) TEACl:URE, (b) TEACl:LAC, (c) TEACl:URE:BDO, and (d) TEACl:LAC:BDO.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
10 
article can be found online at https://doi.org/10.1016/j.ijbiomac.20 
26.152327.
Data availability
Data will be made available on request.
References
[1] A.K. Chandel, V.K. Garlapati, A.K. Singh, F.A.F. Antunes, S.S. Da Silva, The path 
forward for lignocellulose biorefineries: bottlenecks, solutions, and perspective on 
commercialization, Bioresour. Technol. 264 (2018) 370–381, https://doi.org/ 
10.1016/j.biortech.2018.06.004.
[2] C. Huang, W. Lin, C. Lai, X. Li, Y. Jin, Q. Yong, Coupling the post-extraction process 
to remove residual lignin and alter the recalcitrant structures for improving the 
enzymatic digestibility of acid-pretreated bamboo residues, Bioresour. Technol. 
285 (2019) 121355, https://doi.org/10.1016/j.biortech.2019.121355.
[3] D. Haldar, M.K. Purkait, A review on the environment-friendly emerging 
techniques for pretreatment of lignocellulosic biomass: mechanistic insight and 
advancements, Chemosphere 264 (2021) 128523, https://doi.org/10.1016/j. 
chemosphere.2020.128523.
[4] N. Giummarella, Y. Pu, A.J. Ragauskas, M. Lawoko, A critical review on the 
analysis of lignin carbohydrate bonds, Green Chem. 21 (2019) 1573–1595, https:// 
doi.org/10.1039/C8GC03606C.
[5] N. Feng, S. She, F. Tang, X. Zhao, J. Chen, P. Wang, Q. Wu, O.J. Rojas, Formation 
and identification of lignin–carbohydrate complexes in pre-hydrolysis liquors, 
Biomacromolecules 24 (2023) 2541–2548, https://doi.org/10.1021/acs. 
biomac.3c00053.
[6] P. Alvira, E. Tomás-Pejó, M. Ballesteros, M.J. Negro, Pretreatment technologies for 
an efficient bioethanol production process based on enzymatic hydrolysis: a 
review, Bioresour. Technol. 101 (2010) 4851–4861, https://doi.org/10.1016/j. 
biortech.2009.11.093.
[7] S. Rizvi, H.M. Gade, Imidazolium-based ionic liquids as cellulose solvents: 
mechanism and molecular insights, Biomass Bioenergy 196 (2025) 107758, 
https://doi.org/10.1016/j.biombioe.2025.107758.
[8] H. Jørgensen, J.B. Kristensen, C. Felby, Enzymatic conversion of lignocellulose into 
fermentable sugars: challenges and opportunities, Biofuels Bioprod. Biorefin. 1 
(2007) 119–134, https://doi.org/10.1002/bbb.4.
[9] J.C. Solarte-Toro, J.M. Romero-García, J.C. Martínez-Patiño, E. Ruiz-Ramos, 
E. Castro-Galiano, C.A. Cardona-Alzate, Acid pretreatment of lignocellulosic 
biomass for energy vectors production: a review focused on operational conditions 
and techno-economic assessment for bioethanol production, Renew. Sustain. 
Energy Rev. 107 (2019) 587–601, https://doi.org/10.1016/j.rser.2019.02.024.
[10] U. Shakeel, Y. Zhang, C. Liang, W. Wang, W. Qi, Unrevealing the influence of 
reagent properties on disruption and digestibility of lignocellulosic biomass during 
alkaline pretreatment, Int. J. Biol. Macromol. 266 (2024) 131193, https://doi.org/ 
10.1016/j.ijbiomac.2024.131193.
[11] L.G. Nair, K. Agrawal, P. Verma, Organosolv pretreatment: an in-depth purview of 
mechanics of the system, Bioresour. Bioprocess. 10 (2023) 50, https://doi.org/ 
10.1186/s40643-023-00673-0.
[12] Z. Zhou, D. Ouyang, D. Liu, X. Zhao, Oxidative pretreatment of lignocellulosic 
biomass for enzymatic hydrolysis: Progress and challenges, Bioresour. Technol. 
367 (2023) 128208, https://doi.org/10.1016/j.biortech.2022.128208.
[13] Y.W. Sitotaw, N.G. Habtu, A.Y. Gebreyohannes, S.P. Nunes, T. Van Gerven, Ball 
milling as an important pretreatment technique in lignocellulose biorefineries: a 
review, Biomass Convers. Biorefinery 13 (2023) 15593–15616, https://doi.org/ 
10.1007/s13399-021-01800-7.
[14] N. Jacquet, G. Maniet, C. Vanderghem, F. Delvigne, A. Richel, Application of steam 
explosion as pretreatment on lignocellulosic material: a review, Ind. Eng. Chem. 
Res. 54 (2015) 2593–2598, https://doi.org/10.1021/ie503151g.
[15] Z. Zhang, X. Zhao, C. Huang, C. Lai, Q. Yong, Liquid hot water pretreatment 
technology: opening a new chapter in the green transformation and high-value 
utilization of biomass resources, Biomass Bioenergy 203 (2025) 108278, https:// 
doi.org/10.1016/j.biombioe.2025.108278.
[16] V. Balan, M. Mohammadi, B.E. Dale, Advancements in ammonia-based 
pretreatment: key benefits and industry applications, RSC Sustain. 3 (2025) 
4228–4249, https://doi.org/10.1039/D5SU00070J.
[17] M. Saritha, A. Arora, Lata, Biological pretreatment of lignocellulosic substrates for 
enhanced delignification and enzymatic digestibility, Indian J. Microbiol. 52 
(2012) 122–130, https://doi.org/10.1007/s12088-011-0199-x.
[18] P. Verdía Barbará, H. Choudhary, P.S. Nakasu, A. Al-Ghatta, Y. Han, C. Hopson, R. 
I. Aravena, D.K. Mishra, A. Ovejero-Pérez, B.A. Simmons, J.P. Hallett, Recent 
advances in the use of ionic liquids and deep eutectic solvents for lignocellulosic 
biorefineries and biobased chemical and material production, Chem. Rev. 125 
(2025) 5461–5583, https://doi.org/10.1021/acs.chemrev.4c00754.
[19] N. Mqoni, I. Bahadur, S. Singh, X. Meng, A. Ragauskas, Deep eutectic solvents for 
pretreatment of lignocellulose biomass: physical properties, solubility mechanisms, 
and their interactions, Chem. Rev. 126 (2026) 1206–1257, https://doi.org/ 
10.1021/acs.chemrev.5c00597.
[20] M. Hu, Y. Yu, X. Li, X. Wang, Y. Liu, The dawn of aqueous deep eutectic solvents for 
lignin extraction, Green Chem. 25 (2023) 10235–10262, https://doi.org/10.1039/ 
D3GC03563H.
[21] M. Francisco, A. van den Bruinhorst, M.C. Kroon, New natural and renewable low 
transition temperature mixtures (LTTMs): screening as solvents for lignocellulosic 
biomass processing, Green Chem. 14 (2012) 2153–2157, https://doi.org/10.1039/ 
C2GC35660K.
[22] H. Xu, Y. Kong, J. Peng, W. Wang, B. Li, Mechanism of deep eutectic solvent 
delignification: insights from molecular dynamics simulations, ACS Sustain. Chem. 
Eng. 9 (2021) 7101–7111, https://doi.org/10.1021/acssuschemeng.1c01260.
[23] M. Zhang, C. Ren, X. Song, R. Zhang, X. Shi, H. Zhang, D. Ji, L. Zang, Efficient 
dissolution of lignin during lignocellulosic biomass fractionation using deep 
eutectic solvents, Biomass Bioenergy 197 (2025) 107836, https://doi.org/ 
10.1016/j.biombioe.2025.107836.
[24] Á. Lobato-Rodríguez, B. Gullón, A. Romaní, P. Ferreira-Santos, G. Garrote, P. 
G. Del-Río, Recent advances in biorefineries based on lignin extraction using deep 
eutectic solvents: a review, Bioresour. Technol. 388 (2023) 129744, https://doi. 
org/10.1016/j.biortech.2023.129744.
[25] S. Zhou, X. Zhang, Y. Xu, Molecular dynamics interpretation of citric acid-water 
assisting mechanism in deep eutectic solvent to destruct agricultural residue and 
improve enzymatic hydrolyzability of cellulose, Ind. Crop. Prod. 227 (2025) 
120805, https://doi.org/10.1016/j.indcrop.2025.120805.
[26] C. Ji, R. Weng, C. Yu, Y. Fan, J. Ouyang, Insights into acidic deep eutectic solvent- 
mediated softwood pretreatment for high fermentable sugar yield and efficient 
lignin recovery, Ind. Crop. Prod. 227 (2025) 120779, https://doi.org/10.1016/j. 
indcrop.2025.120779.
[27] Z. Guo, J. Wu, L. Liu, Y. Jiao, H. Kang, R. Liu, Sustainable and energy-efficient 
fractionation of lignocellulosic biomass with choline-based deep eutectic solvents, 
Chem Sus Chem 19 (2026) e202502177, https://doi.org/10.1002/ 
cssc.202502177.
[28] A.M. da C. Lopes, J.R.B. Gomes, J.A.P. Coutinho, A.J.D. Silvestre, Novel insights 
into biomass delignification with acidic deep eutectic solvents: a mechanistic study 
of β-O-4 ether bond cleavage and the role of the halide counterion in the catalytic 
performance, 2020, https://doi.org/10.1039/C9GC02569C.
[29] O.S. Hammond, D.T. Bowron, K.J. Edler, Liquid structure of the choline chloride-urea deep eutectic solvent (reline) from neutron diffraction and atomistic 
modelling, Green Chem. 18 (2016) 2736–2744, https://doi.org/10.1039/ 
C5GC02914G.
[30] V. Migliorati, F. Sessa, P. D’Angelo, Deep eutectic solvents: a structural point of 
view on the role of the cation, Chem. Phys. Lett. 737 (2019) 100001, https://doi. 
org/10.1016/j.cpletx.2018.100001.
[31] H.S. Salehi, A.T. Celebi, T.J.H. Vlugt, O.A. Moultos, Thermodynamic, transport, 
and structural properties of hydrophobic deep eutectic solvents composed of 
tetraalkylammonium chloride and decanoic acid, J. Chem. Phys. 154 (2021) 
144502, https://doi.org/10.1063/5.0047369.
[32] L. Yang, L. Xu, H. Yang, Z. Shi, P. Zhao, J. Yang, Effect of different washing 
methods on reducing the inhibition of surface lignin in the tetraethylammonium 
chloride/oxalic acid-based deep eutectic solvent pretreatment, Ind. Crop. Prod. 
188 (2022) 115728, https://doi.org/10.1016/j.indcrop.2022.115728.
[33] R. Zhao, Y. Sheng, H. Yang, Z. Shi, D. Wang, J. Yang, Tailored bamboo 
fractionation with ternary deep eutectic solvent system to maximize biorefinery 
toward enzymatic saccharification and lignin recovery, Int. J. Biol. Macromol. 308 
(2025) 142361, https://doi.org/10.1016/j.ijbiomac.2025.142361.
[34] J. Ma, N.L. Ma, W. Zhang, Y. Wu, Y. Ma, G. Chen, Y. Sun, J. Wang, C. Sun, Highly 
efficient conversion of cellulose and hemicellulose using an innovative ternary 
deep eutectic solvent: achieving the green and complete use of lignocellulose, Ind. 
Crop. Prod. 230 (2025) 121091, https://doi.org/10.1016/j.indcrop.2025.121091.
[35] S. Zhou, J. Xu, Z. Zhang, A. Li, J. Li, W. Zhang, K. Chen, Efficient and sustainable 
fractionation of eucalyptus biomass into high quality cellulose and lignin using a 
novel ternary deep eutectic solvent, Chem. Eng. J. 518 (2025) 164829, https://doi. 
org/10.1016/j.cej.2025.164829.
[36] J. Cheng, C. Huang, Y. Zhan, S. Han, J. Wang, X. Meng, C.G. Yoo, G. Fang, A. 
J. Ragauskas, Effective biomass fractionation and lignin stabilization using a diol 
DES system, Chem. Eng. J. 443 (2022) 136395, https://doi.org/10.1016/j. 
cej.2022.136395.
[37] M.J. Abraham, T. Murtola, R. Schulz, S. Páll, J.C. Smith, B. Hess, E. Lindahl, 
GROMACS: high performance molecular simulations through multi-level 
parallelism from laptops to supercomputers, SoftwareX 1–2 (2015) 19–25, https:// 
doi.org/10.1016/j.softx.2015.06.001.
[38] Y. Yu, A. Krämer, R.M. Venable, B.R. Brooks, J.B. Klauda, R.W. Pastor, 
CHARMM36 lipid force field with explicit treatment of long-range dispersion: 
parametrization and validation for phosphatidylethanolamine, 
Phosphatidylglycerol, and ether lipids, J. Chem. Theory Comput. 17 (2021) 
1581–1595, https://doi.org/10.1021/acs.jctc.0c01327.
[39] J. Lee, X. Cheng, J.M. Swails, M.S. Yeom, P.K. Eastman, J.A. Lemkul, S. Wei, 
J. Buckner, J.C. Jeong, Y. Qi, S. Jo, V.S. Pande, D.A. Case, C.L.I. Brooks, A.D. 
Jr. MacKerell, J.B. Klauda, W. Im, CHARMM-GUI input generator for NAMD, 
GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the 
CHARMM36 additive force field, J. Chem. Theory Comput. 12 (2016) 405–413, 
https://doi.org/10.1021/acs.jctc.5b00935.
[40] V. Sethuraman, J.V. Vermaas, L. Liang, A.J. Ragauskas, J.C. Smith, L. Petridis, 
Atomistic simulations of Polydisperse lignin melts using simple Polydisperse 
residue input generator, Biomacromolecules 25 (2024) 767–777, https://doi.org/ 
10.1021/acs.biomac.3c00951.
[41] J.V. Vermaas, L.D. Dellon, L.J. Broadbelt, G.T. Beckham, M.F. Crowley, Automated 
transformation of lignin topologies into atomic structures with LigninBuilder, ACS 
Sustain. Chem. Eng. 7 (2019) 3443–3453, https://doi.org/10.1021/ 
acssuschemeng.8b05665.
S. Rizvi and H.M. Gade International Journal of Biological Macromolecules 364 (2026) 152327 
11 
[42] Q. Zhang, K.D.O. Vigier, S. Royer, F. Jérôme, Deep eutectic solvents: syntheses, 
properties and applications, Chem. Soc. Rev. 41 (2012) 7108–7146, https://doi. 
org/10.1039/C2CS35178A.
[43] G. Cui, D. Yang, H. Qi, Efficient SO2 absorption by anion-functionalized deep 
eutectic solvents, Ind. Eng. Chem. Res. 60 (2021) 4536–4541, https://doi.org/ 
10.1021/acs.iecr.0c04981.
[44] G. Bussi, D. Donadio, M. Parrinello, Canonical sampling through velocity rescaling, 
J. Chem. Phys. 126 (2007) 014101, https://doi.org/10.1063/1.2408420.
[45] M. Bernetti, G. Bussi, Pressure control using stochastic cell rescaling, J. Chem. 
Phys. 153 (2020) 114107, https://doi.org/10.1063/5.0020514.
[46] Y. Zhang, A. Otani, E.J. Maginn, Reliable viscosity calculation from equilibrium 
molecular dynamics simulations: a time decomposition method, J. Chem. Theory 
Comput. 11 (2015) 3537–3546, https://doi.org/10.1021/acs.jctc.5b00351.
[47] T.C.F. Gomes, M.S. Skaf, Cellulose-builder: a toolkit for building crystalline 
structures of cellulose, J. Comput. Chem. 33 (2012) 1338–1346, https://doi.org/ 
10.1002/jcc.22959.
[48] K. Soongprasit, V. Sricharoenchaikul, D. Atong, Phenol-derived products from fast 
pyrolysis of organosolv lignin, Energy Rep. 6 (2020) 151–167, https://doi.org/ 
10.1016/j.egyr.2020.08.040.
[49] R. Chaudhary, P.L. Dhepe, Solid base catalyzed depolymerization of lignin into low 
molecular weight products, Green Chem. 19 (2017) 778–788, https://doi.org/ 
10.1039/C6GC02701F.
[50] Y. Pu, D. Zhang, P.M. Singh, A.J. Ragauskas, The new forestry biofuels sector, 
Biofuels Bioprod. Biorefin. 2 (2008) 58–73, https://doi.org/10.1002/bbb.48.
[51] T. Darden, D. York, L. Pedersen, Particle mesh Ewald: an N ⋅log(N) method for 
Ewald sums in large systems, J. Chem. Phys. 98 (1993) 10089–10092, https://doi. 
org/10.1063/1.464397.
[52] B. Hess, H. Bekker, H.J.C. Berendsen, J.G.E.M. Fraaije, LINCS: a linear constraint 
solver for molecular simulations, J. Comput. Chem. 18 (1997) 1463–1472, https:// 
doi.org/10.1002/(SICI)1096-987X(199709)18:12%253C1463::AID-JCC4% 
253E3.0.CO;2-H.
[53] W. Humphrey, A. Dalke, K. Schulten, VMD: visual molecular dynamics, J. Mol. 
Graph. 14 (1996) 33–38, https://doi.org/10.1016/0263-7855(96)00018-5.
[54] R.J. Gowers, M. Linke, J. Barnoud, T.J.E. Reddy, M.N. Melo, S.L. Seyler, 
J. Domański, D.L. Dotson, S. Buchoux, I.M. Kenney, O. Beckstein, MDAnalysis: A 
Python Package for the Rapid Analysis of Molecular Dynamics Simulations, in: 
SciPy 2016, 2016, https://doi.org/10.25080/Majora-629e541a-00e.
[55] L.F. Zubeir, M.H.M. Lacroix, M.C. Kroon, Low transition temperature mixtures as 
innovative and sustainable CO2 capture solvents, J. Phys. Chem. B 118 (2014) 
14429–14441, https://doi.org/10.1021/jp5089004.
[56] F.S. Mjalli, G. Murshid, S. Al-Zakwani, A. Hayyan, Monoethanolamine-based deep 
eutectic solvents, their synthesis and characterization, Fluid Phase Equilib. 448 
(2017) 30–40, https://doi.org/10.1016/j.fluid.2017.03.008.
[57] A.P. Abbott, R.C. Harris, K.S. Ryder, C. D’Agostino, L.F. Gladden, M.D. Mantle, 
Glycerol eutectics as sustainable solvent systems, 2011, https://doi.org/10.1039/ 
C0GC00395F.
[58] I. Cichowska-Kopczyńska, B. Nowosielski, D. Warmińska, I. Cichowska- 
Kopczyńska, B. Nowosielski, D. Warmińska, Deep eutectic solvents: properties and 
applications in CO2 separation, Molecules 28 (2023), https://doi.org/10.3390/ 
molecules28145293.
[59] Y. Feng, G. Liu, H. Sun, C. Xu, B. Wu, C. Huang, B. Lei, A novel strategy to intensify 
the dissolution of cellulose in deep eutectic solvents by partial chemical bonding, 
BioResources 17 (2022) 4167–4185, https://doi.org/10.15376/biores.17.3.4167- 
4185.
[60] Z. Chen, A. Ragauskas, C. Wan, Lignin extraction and upgrading using deep 
eutectic solvents, Ind. Crop. Prod. 147 (2020) 112241, https://doi.org/10.1016/j. 
indcrop.2020.112241.
[61] D. Huo, Y. Sun, Q. Yang, F. Zhang, G. Fang, H. Zhu, Y. Liu, Selective degradation of 
hemicellulose and lignin for