Pretreatment of <i>Miscanthus</i> with biomass‐degrading bacteria for increasing delignification and enzymatic hydrolysability
Bibliographic record
Abstract
Summary Biomass recalcitrance is still a main challenge for the production of biofuels and high‐value products. Here, an alternative Miscanthus pretreatment method by using lignin‐degrading bacteria was developed. Six efficient Miscanthus ‐degrading bacteria were first cultured to produce laccase by using 0.5% Miscanthus biomass as carbon source. After 1–5 days of incubation, the maximum laccase activities induced by Miscanthus in the six strains were ranged from 103 to 8091 U l −1 . Then, the crude enzymes were directly diluted by equal volumes of citrate buffer and added Miscanthus biomass to a solid concentration at 4% (w/v). The results showed that all bacterial pretreatments significantly decreased the lignin content, especially in the presence of two laccase mediators ( ABTS and HBT ). The lignin removal directly correlated with increases in total sugar and glucose yields after enzymatic hydrolysis. When ABTS was used as a mediator, the best lignin‐degrading bacteria ( Pseudomonas sp. AS 1) can remove up to 50.1% lignin of Miscanthus by obtaining 2.2‐fold glucose yield, compared with that of untreated biomass. Therefore, this study provided an effective Miscanthus pretreatment method by using lignin‐degrading bacteria, which may be potentially used in improving enzymatic hydrolysability of biomass.
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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) | 0.000 | 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) | 0.001 | 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, 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".