Enhanced biomass densification pretreatment using binary chemicals for efficient lignocellulosic valorization
Bibliographic record
Abstract
Many effective pretreatment methods (such as dilute acid, dilute alkali, ionic liquids , etc.) have been developed for lignocellulose upgrading, but several defaults of low working mass, high sugar loss and extra cost of solid-liquid separation and water washing hinder their large-scale application in industry. Besides, the valorization of lignin-rich residue from pretreated biomass after hydrolysis or fermentation greatly contributes to the economy and sustainability of lignocellulosic biorefinery , which is usually underestimated. This study developed a densification pretreatment with binary chemicals (densifying lignocellulosic biomass with sulfuric acid (SA) and metal salt (MS) followed by autoclave treatment ((DLCA(SA-MS)), which was conducted under mild condition (121 °C) with a biomass working mass as high as 400 kg/m 3 . The DLCA(SA-MS) biomass achieved over 95% sugar retention, 90% enzymatic sugar conversion and a high concentration of fermentable sugar (212.3 g/L) with superior fermentability. Furthermore, bio-adsorbent derived from DLCA(SA-MS) biomass residue was highly adsorptive and suitable for dyeing wastewater treatment , providing a feasible and eco-friendly method for lignin-rich residue valorization. These findings indicated that DLCA(SA-MS) pretreatment enables the full-component utilization of biomass and boosts the economic viability of lignocellulosic biorefinery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".