Partly Sulfonated Polystyrenes Effectively Enhance the Enzymatic Saccharification of Poplar Wood under Green Liquor Pretreatment
Bibliographic record
Abstract
Lignosulfonate, as a water-soluble lignin, is famous for weakening the nonproductive adsorption between cellulase and substrate lignin. This study investigated the role of hydrophobic and hydrophilic components of partly sulfonated polystyrene (SPS) as lignosulfonate mimics in enhancing the enzymatic hydrolysis of lignocellulose by minimizing nonproductive cellulase–lignin interactions. SPS samples with varying molecular weights and sulfonation degrees were synthesized, the performance in enzymatic hydrolysis was evaluated, and their interactions with cellulase and lignin were analyzed. The results indicated that reducing the SPS molecular weight and degree of sulfonation significantly improved substrate enzymatic digestibility at 72 h (SED@72 h), with the optimal SPS-MW2700-61.5% enhancing SED@72 h by 20.7% and decreasing the cellulase–lignin adsorption by 81%. SPS with low sulfonation degrees spontaneously adsorbed on cellulase, driven by electrostatic interaction forces. In contrast, SPS with high sulfonation degrees adsorbed on cellulase, driven by van der Waals forces and hydrogen bonding forces. SPS formed a 1:1 complex with cellulase, preserving the enzyme activity. This research provides insights into the development of additives to minimize nonproductive adsorption during lignocellulosic enzymatic hydrolysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".