Pretreatment of <i>Miscanthus</i> with biomass‐degrading bacteria for increasing delignification and enzymatic hydrolysability
Why this work is in the frame
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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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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 it