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Record W4416432163 · doi:10.1021/acs.biomac.5c01420

Hydrophilicity Regulation of Styrene-Based Copolymers to Promote the Enzymatic Saccharification Efficiency by Dually Protecting Cellulases and Blocking Residual Lignin

2025· article· en· W4416432163 on OpenAlexaff
Tian Liu, Peipei Wang, Usama Shakeel, Jiaqi Guo, Wenyuan Zhu, Mohammad Rizwan Khan, Huining Xiao, Junlong Song

Bibliographic record

VenueBiomacromolecules · 2025
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of New Brunswick
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsKing Saud UniversityNational Natural Science Foundation of China
KeywordsCellulaseBioconversionLigninAdsorptionEnzymatic hydrolysisHydrolysisLignocellulosic biomassCellulose

Abstract

fetched live from OpenAlex

Nonproductive adsorption behavior of residual lignin on cellulase has long hindered lignocellulosic biomass enzymatic saccharification. In this study, a strategy of hydrophilicity regulation of styrene-based copolymers was developed to improve the enzymatic saccharification efficiency by specifically acting on cellulases and blocking residual lignin. Styrene-based copolymers with 2-acrylamido-2-methylpropanesulfonate (ST- co -AMPS or SA), varying in molecular weight and hydrophilicity, were synthesized. At pH 4.8 and 10 FPU/glucan cellulase dosage, adding 0.1 g/g-substrate SA8–32 (initiator dosage = 8%, ST% = 32%) boosted the sugar yield from 45.6 to 83.4%. When the pH increased to 5.5, SA4–16 addition further raised the yield to 94%. Quartz crystal microbalance and atomic force microscopy analyses revealed that ST- co -AMPS directly interacted with cellulase to form complexes while also blocking residual lignin. Fluorescence spectrometer analysis showed SA-cellulase complexes reduced nonproductive adsorption via hydrogen bonding and van der Waals forces. Given the demand for efficient lignocellulosic bioconversion in sustainable biorefineries, ST- co -AMPS shows great potential as an additive to enhance lignocellulose enzymatic saccharification.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.205
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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