Effects of Washing, Autoclaving, and Surfactants on the Enzymatic Hydrolysis of Negatively Valued Paper Mill Sludge for Sugar Production
Bibliographic record
Abstract
Paper mill sludge (PMS) is a paper industry waste but can be a potential feedstock for cellulosic sugar production. In this study, washing, autoclaving, and surfactants were investigated for PMS pretreatment before enzymatic hydrolysis to produce cellulosic sugars. It was demonstrated that washing and autoclaving had a limited impact on improving the enzymatic hydrolysis of PMS but washing reduced the ash content, resulting in less acid being used in neutralization. Adding nonionic surfactants of Triton X-100, Tween 80, and PEG 8000 improved the conversion of PMS, and the highest rates were 56.3% and 55.4%, achieved by adding 1% Triton X-100 and 5% PEG 8000, respectively. The lowest conversion rates were produced by 1% and 5% Tween 80, probably because it had a hydrophobic alkyl chain. After the optimization of the enzyme and PMS concentrations in hydrolysis via supplementation with PEG 8000, the highest PMS conversion of 74.7% was achieved by 10% PMS and 3% enzymes. With the addition of PEG 8000, the conversion of PMS was reduced at high concentrations of enzyme and PMS compared with that of the non-PEG control, which was more significant at the later stage of hydrolysis. We proposed that the combined negative effects of end products and surfactants were more significant on hydrolysis than the effects of end products alone.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".