The effect of isolated lignins, obtained from a range of pretreated lignocellulosic substrates, on enzymatic hydrolysis
Bibliographic record
Abstract
The influence of the residual lignin remaining in the cellulosic rich component of pretreated lignocellulosic substrates on subsequent enzymatic hydrolysis was assessed. Twelve lignin preparations were isolated by two isolation methods (protease treated lignin (PTL) and cellulolytic enzymatic lignin (CEL)) from three types of biomass (corn stover, poplar, and lodgepole pine) that had been pretreated by two processes (steam and organosolv pretreatments). Comparative analysis of the isolated lignin showed that the CEL contained lower amounts of carbohydrates and protein than did the PTL and that the isolated lignin from corn stover contained more carbohydrates than did the lignin derived from the poplar and lodgepole pine. The lower yields of acid insoluble lignin (AIL) obtained from the corn stover when using the PTL method indicated that the lignin from the corn stover had a higher hydrophilicity than did the lignin from the poplar and lodgepole pine. The isolated lignin preparations were added to the reaction mixture containing crystalline cellulose (Avicel) and their possible effects on enzymatic hydrolysis were assessed. It was apparent that the lignin isolated from lodgepole pine and steam pretreated poplar decreased the hydrolysis yields of Avicel, whereas the other isolated lignins did not appear to decrease the hydrolysis yields significantly. The hydrolysis yields of the pretreated lignocellulose and those of Avicel containing the PTL showed good correlation, indicating that the nature of the residual lignin obtained after pretreatment significantly influenced 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 | 0.001 |
| 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) | 0.001 | 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".