Recent advances in understanding the effects of lignin structural characteristics on enzymatic hydrolysis
Bibliographic record
Abstract
Enzymatic hydrolysis of lignocellulose for bioethanol production shows a great potential to remit the rapid consumption of fossil fuels, given the fact that lignocellulose feedstocks are abundant, cost-efficient, and renewable. Lignin results in low enzymatic saccharification by forming the steric hindrance, non-productive adsorption of cellulase onto lignin, and deactivating the cellulase. In general, the non-productive binding of cellulase on lignin is widely known as the major cause for inhibiting the enzymatic hydrolysis. Pretreatment is an effective way to remove lignin and improve the enzymatic digestibility of lignocellulose. Along with removing lignin, the pretreatment can modify the lignin structure, which significantly affects the non-productive adsorption of cellulase onto lignin. To relieve the inhibitory effect of lignin on enzymatic hydrolysis, enormous efforts have been made to elucidate the correlation of lignin structure with lignin-enzyme interactions but with different views. In addition, contrary to the traditional belief that lignin inhibits enzymatic hydrolysis, in recent years, the addition of water-soluble lignin such as lignosulfonate or low molecular-weight lignin exerts a positive effect on enzymatic hydrolysis, which gives a new insight into the lignin-enzyme interactions. For throwing light on their structure-interaction relationship during enzymatic hydrolysis, the effect of residual lignin in substrate and introduced lignin in hydrolysate on enzymatic hydrolysis are critically reviewed, aiming at realizing the targeted regulation of lignin structure for improving the saccharification of lignocellulose. The review is also focused on exploring the lignin-enzyme interactions to mitigate the negative impact of lignin and reducing the cost of enzymatic hydrolysis of lignocellulose.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".