Efficient lignin biodegradation triggered by alkali-tolerant ligninolytic bacteria through improving lignin solubility in alkaline solution
Bibliographic record
Abstract
Low lignin solubility in aqueous solution is one of the major bottlenecks for lignin biodegradation and bioconversion. Alkaline solution contributes to improving lignin solubility, whereas most microbes can not survive in alkaline conditions. Herein, lignin dissolution behaviors in different pH solutions were systematically investigated, which indicated that solution pH above 10.5 contributed to high solubility of alkali lignin. To match with alkaline lignin aqueous system, several alkali-tolerant ligninolytic bacteria were isolated, most of which are distinct to previously reported ones. Then, the ligninolytic capabilities of these isolates were assessed in different pH conditions by determining their assimilation on alkali lignin, lignin-derived monomers and dimers, their decolorization capabilities, and their lignin peroxidase activities. Thereafter, the underlying ligninolytic and alkali-tolerant mechanisms of Sutcliffiella sp. NC1, an alkalophilic bacterium, was analyzed on the basis of its genome information. The results not only provide valuable information for lignin biodegradation and lignin valorization, but also expand knowledge on alkali-tolerant bacteria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".