Influence of pretreatment conditions on composition of liquid hydrolysate and subsequent enzymatic saccharification of remaining solids
Bibliographic record
Abstract
Abstract In the present work, the effect of dilute acid pretreatment was studied on the composition of liquid hydrolysate obtained after pretreatment by employing different combinations of process variables (temperature, time and acid concentration). The effect of pretreatment was also studied on subsequent enzymatic saccharification of remaining solids to obtain maximum yield of sugars. The efficiency of pretreatment was measured in terms of high‐xylose and low‐glucose yields, which was found most suitable at pretreatment conditions of 120°C, 120 min and 2% (v/v) acid concentration. With increased severity of pretreatment, xylose yield decreased with concomitant increase in glucose yield. The decrease in xylose yield was attributed to conversion into degradation products such as 5‐hydroxylmethyl furfural (HMF) and acetic acid. The percentage of enzymatic saccharification increased with increased pretreatment severity. Saccharification of biomass pretreated at 180°C, 7 min and 0.5% (v/v) acid concentration produced the maximum glucose yield of saccharification of 352 g/kg dry matter, compared to just 97 g glucose/kg dry matter in the case of untreated biomass. The same pretreatment conditions resulted in maximum total sugar yield of pretreatment and saccharification of 459 g/kg dry matter, which was more than 67% of the total potential sugars in biomass. © 2012 Canadian Society for Chemical Engineering
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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".