Optimization of Biomass Delignification by Extrusion and Analysis of Extrudate Characteristics
Bibliographic record
Abstract
Pretreatment of lignocellulosic biomass remains the primary obstacle to the profitable use of this type of biomass in biorefineries. The challenge lies in the recalcitrance of the lignin-carbohydrate complex to pretreatment, especially the difficulty in removing the lignin to access the carbohydrates (cellulose and hemicellulose). This study had two objectives: (i) to investigate the effect of reactive extrusion on lignocellulosic biomass in terms of delignification percentage and the structural characteristics of the resulting extrudates, and (ii) to propose a novel pretreatment approach involving extrusion technology based on the results of the first objective. Two types of biomasses were used: agricultural residue (corn stover) and forest residue (black spruce chips). By optimizing the extrusion conditions via response surface analysis (RSA), the delignification percentages were significantly improved. For corn stover, the delignification yield increased from 2.3% to 27.4%, while increasing from 1% to 25.3% for black spruce chips. The highest percentages were achieved without the use of sodium hydroxide and for temperatures below 65 °C. Furthermore, the optimized extrudates exhibited important structural changes without any formation of p-cresol, furfural, and 5-hydroxymethylfurfural (HMF) (enzymes and microbial growth-inhibiting compounds). Acetic acid however was detected in corn stover extrudate. The structural changes included the disorganization of the most recalcitrant functional groups, reduction of particle sizes, increase of specific surface areas, and the appearance of microscopic roughness on the particles. Analyzing all the data led to propose a new promising approach to the pretreatment of lignocellulosic biomasses. This approach involves combining extrusion and biodelignification with white rot fungi to improve the enzymatic hydrolysis of carbohydrates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".