Bioconversion of lignocellulose in ensiled Caragana korshinskii Kom. into bioethanol by ferulic acid esterase-producing Limosilactobacillus reuteri A4-2 and Acremonium cellulase
Bibliographic record
Abstract
Woody and agricultural residues, such as Caragana korshinskii Kom. (Korshinsk pea shrub), have gained significant attention as a sustainable feedstock for biofuel production. This study developed a novel ensiling approach by combining ferulic acid esterase-producing Limosilactobacillus reuteri A4–2 (LR) with Acremonium cellulase (AC) as additives to enhance bioethanol production from C . korshinskii . Four treatments were: biomass ensiled without additive (control), or with LR, AC, and a combination of LR with AC for 3, 7, 14, 30, and 60 d. The addition of LR, AC, or both improved fermentation quality by inhibiting undesirable bacteria and modifying the microbial communities, leading to increased lactic acid, ferulic acid and crude protein contents, with a lower ammonia nitrogen content in C. korshinskii silages. These treatments also enhanced the degradability of structural carbohydrates releasing more fermentable sugars after ensiling, with the LR+AC showing the best results. Spectroscopic analyses provided visual evidence of chemical bond disruption, morphological changes, and decrystallization in the ensiled C. korshinskii . Separate hydrolysis and fermentation (SHF) experiments conducted with the silages which revealed that pretreatment with LR+AC improved cellulose conversion and ethanol yield by 9.9 % and 53.4 % compared to the control, achieving a maximum ethanol yield of 29.4 % during SHF process. The Sankey diagram showed that L. reuteri abundance was positively correlated with improved silage quality, which in turn was correlated to higher ethanol yield. Overall, ensiling C. korshinskii with L. reuteri A4–2 and Acremonium cellulase provides a promising strategy for efficient biofuel production from lignocellulose 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) | 0.000 | 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".