Influence of steam pretreatment severity on post‐treatments used to enhance the enzymatic hydrolysis of pretreated softwoods at low enzyme loadings
Bibliographic record
Abstract
It is recognized that some form of post-treatment will usually be required if reasonable hydrolysis yields (>60%) of steam pretreated softwood are to be achieved when using low enzyme loadings (5 FPU/g cellulose). In the work reported here we modified/removed lignin from steam pretreated softwood while investigating the influence that the severity of pretreatment might have on the effectiveness of subsequent post-treatments. Although treatment at a lower severity could provide better overall hemicellulose recovery, post-treatment was not as effective on the cellulosic component. Pretreatment at medium severity resulted in the best compromise, providing reasonable recovery of the water soluble hemicellulose sugars and the use of post-treatment conditions that significantly increased the enzymatic hydrolysis of the water insoluble cellulosic component. Post-treatment with alkaline hydrogen peroxide or neutral sulfonation resulted in 62% cellulose hydrolysis at an enzyme loading of 5 FPU/g cellulose, which was four times greater than was obtained when the cellulosic fraction was not post-treated. When the enzyme loading was increased to 15 FPU/g cellulose, the post-treated cellulosic fraction was almost completely hydrolyzed to glucose. Despite the higher lignin content (44%) of the sulfonated substrate, similar hydrolysis yields to those achieved after alkaline peroxide post-treatment (14% lignin content) indicated that, in addition to lignin removal, lignin modification also plays an important role in influencing the effectiveness of hydrolysis when low enzyme loadings are used.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".