Directional Structure Modification of Poplar Biomass-Inspired High Efficacy of Enzymatic Hydrolysis by Sequential Dilute Acid–Alkali Treatment
Bibliographic record
Abstract
A major challenge in converting lignocellulose to biofuel is overcoming the resistance of the biomass structure. Herein, sequential dilute acid-alkali/aqueous ammonia treatment was evaluated to enhance enzymatic hydrolysis of poplar biomass by removing hemicellulose first and then removing lignin with acid and base, respectively. The results show that glucose release in sequential dilute acid-alkali treatments (61.4-71.4 mg/g) was 7.3-24.8% higher than sequential dilute acid-aqueous ammonia treatments (57.2-61.8 mg/g) and 283.8-346.3% higher than control (16.0 mg/g), respectively. Dilute acid treatment removed most hemicellulose (84.9%) of the biomass, followed by alkaline treatment with 27.5% removal of lignin. Roughness, surface area, and micropore volume of the biomass were crucial for the enzymatic hydrolysis. Furthermore, the ultrastructure changes observed using crystallinity, Fourier transform infrared spectroscopy, thermogravimetric analysis, and pyrolysis gas chromatography/mass spectrometry support the effects of sequential dilute acid-alkali treatment. The results provide an efficient approach to facilitate a better enzymatic hydrolysis of the poplar samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".