Biological Pretreatment by Solid-State Fermentation of Oat Straw to Enhance Physical Quality of Pellets
Bibliographic record
Abstract
Pelleting can increase the efficiency of handling and transportation of biomass. Pretreatment obtains lignin fragments by disrupting the lignocellulosic structure of biomass and ensures the high-quality compressed pellets. In this study, solid-state fermentation (SSF) is used as a biological method to improve the quality of pellets of oat straw. SSF of oat straw using Trametes versicolor 52J (TV52J) and Phanerochaete chrysosporium (PC) was conducted. Response surface methodology (RSM) was employed by using a four-factor, three-level Box–Behnken design with fermentation time (days), moisture content (%), particle size (mm), and fermentation temperature (°C) as independent parameters. Pellet density, dimensional stability, and tensile strength were the response variables. The optimization options of fermentation time (33.96 and 35 days), moisture content (70%), particle size (150 and 50 mm), and fermentation temperature (22°C) of oat straw pretreated with these two fungal strains were obtained. The microscopic structural changes of oat straw caused by biological pretreatment were investigated by scanning electron microscopy (SEM). Observation results of SEM showed that the connection between single fibers became relatively loose, and this was beneficial to improve the physical quality of the pellets.
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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 | 0.001 | 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".