Comparative Study on Quality of Fuel Pellets from Switchgrass Treated with Different White-Rot Fungi
Bibliographic record
Abstract
Fungal pretreatment of switchgrass using Phanerochaete chrysosporium (PC), Trametes versicolor 52J (TV52J), and the Trametes versicolor mutant strain (m4D) under solid-state fermentation was conducted to improve its pellet quality. For all three fungal strains, the fermentation temperature had a significant effect (p < 0.05) on pellet unit density and tensile strength. The p-values of the quadratic models for all the response variables showed highly significant regression models (p < 0.01) except for dimensional stability. In addition, 3.1-fold and 2.8-fold increase in pellet tensile strength were obtained from P. chrysosporium- and T. versicolor 52J-treated materials, respectively. Microstructural examination showed that fungal pretreatment reduced pores in the pellets and enhanced pellet particle bonding. Among the fungal strains, PC had the shortest optimum fermentation time (21 d) and most positive impact on the pellet tensile strength and hydrophobicity. Therefore, switchgrass pretreatment using PC has the potential for resolving the challenges of switchgrass pellet transportation and storage and reducing the overall pelletization cost. However, a detailed comparative technoeconomic analysis would be required to make definitive cost comparisons.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".