The influence of pretreatment and enzyme loading on the effectiveness of batch and fed‐batch hydrolysis of corn stover
Bibliographic record
Abstract
To try to improve hydrolysis yields at elevated solids loadings, a comparison was made between batch and fed-batch addition of fresh substrate at the initial and later phases of hydrolysis. Both ethanol (EPCS) and steam-pretreated corn stover (SPCS) substrates were tested at low (5 FPU) and high (60 FPU) loadings of cellulase per gram of cellulose. The fed-batch addition of fresh substrate resulted in a slight decrease in hydrolysis yields when compared with the corresponding batch reactions. A 72-h hydrolysis of the SPCS substrate resulted in a hydrolysis yield of 66% compared with 51% for the EPCS substrate. When the enzyme adsorption and substrate characteristics were assessed during batch and fed-batch hydrolysis, it appeared that the irreversible binding of cellulases to the more recalcitrant original substrate limited their access to the freshly added substrate. After 72-h hydrolysis of the SPCS substrate at low enzyme loadings, ∼40-50% of the added cellulases were desorbed into solution, whereas only 20% of the added enzyme was released from the EPCS substrate. Both simultaneous and sequential treatments with xylanases and cellulases resulted in an up to a 20% increase in hydrolysis yields for both substrates at low enzyme loading. Simons' stain measurements indicated that xylanase treatment increased cellulose access, thus facilitating cellulose 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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".