Extrusion-Biodelignification Approach for Biomass Pretreatment
Bibliographic record
Abstract
This work presents a new approach for lignocellulosic biomass pretreatment. The process is a sequential combination of extrusion (Ex) and semi-solid fermentation (SSF). To assess the Ex-SSF pretreatment efficiency, black spruce chips (wood residues) and corn stover (crop residues) were subjected to the process. The negative controls were the pretreatment of both residues with SSF alone without extrusion. Lignin peroxidase was the main ligninolytic enzyme contributing to the delignification in the negative controls. High lignin peroxide (LiP) activities were recorded for raw black spruce (53.7 ± 2.7 U/L) and corn stover (16.4 ± 0.8 U/L) compared to the Ex-SSF pretreated biomasses where the highest LiP activity recorded was 6.0 ± 0.3 U/L (corn residues). However, with the negative controls, only a maximum of 17% delignification was achieved for both biomasses. As for the Ex-SSF process, the pretreatments were preceded by the optimization of the extrusion (Ex) step and the semi-solid fermentation (SSF) step via experimental designs. The Ex-SSF pretreatments led to interesting results and offered cost-effective advantages compared to existing pretreatments. Biomass delignification reached 59.1% and 65.4% for black spruce and corn stover, respectively. For the analyses performed, it was found that manganese peroxidase (MnP) was the main contributor to delignification during the SSF step. MnP activity was up to 13.8 U/L for Ex-SSF pretreated black spruce, and 32.0 U/L for Ex-SSF pretreated corn stover, while the maximum MnP recorded in the negative controls was 1.4 ± 0.1 U/L. Ex-SSF pretreatment increased the cellulose crystallinity index (CrI) by 13% for black spruce and 4% for corn stover. But enzymatic digestibility of the Ex-SSF pretreated biomasses with 0.25 mL/g of enzyme led to 7.6 mg/L sugar recovery for black spruce, which is 2.3 times the raw biomass yield. The Ex-SSF pretreated corn stover led to 17.0 mg/L sugar recovery, which is a 44% improvement in sugar concentration compared to raw corn stover. However, increasing the enzyme content from 0.25 mL/g to 0.50 mg/L and 0.75 mg/L generated lower hydrolysis efficiency (the sugar recovery decreased).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".