Protective effects of five surfactants on cellulase in the saccharification of corn stover based on the impeded Michaelis-Menten model
Bibliographic record
Abstract
Protective effects of five surfactants were investigated relative to the saccharification of lignocellulose using the impeded Michaelis-Menten model (IMM). The yield of total reducing sugar (Ytrs) and cellulase activity were indexed as the effect of surfactant. The IMM was used to fit the correlation between Ytrs and reaction time to obtain the index (Kobs,0) reflecting the accessibility between cellulose and lignocellulose and the comprehensive index (Ki) reflecting cellulase inactivation and non-specific site adsorption. Results showed that the strongest protective effect was found from polyoxyethylene (80) sorbitan monooleate, followed by rhamnolipid. The surfactants protected cellulase from inactivation and nonspecific site adsorption of lignocellulose in the saccharification, leading to enhanced cellulase activity, especially with respect to carboxymethyl cellulase (CMCase) and filter paper enzyme (FPase) activities. The maximum Ytrs was obtained when the CMCase activity was 136.2 U/mL, while the FPase and β-glucosidase activities should be as high and low as possible, respectively, under the optimized condition. These findings lay the foundation for improving the saccharification efficiency of cellulase and reducing the cost of saccharification of biomass cellulose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".