Optimization of Solid-State Fermentation of Switchgrass Using White-Rot Fungi for Biofuel Production
Bibliographic record
Abstract
Biological delignification using white-rot fungi is a possible approach in the pretreatment of lignocellulosic biomass. Despite the considerable promise of this low-input, environmentally-friendly pretreatment strategy, its large-scale application is still limited. Therefore, understanding the best combination of factors which affect biological pretreatment and its impact on enzymatic hydrolysis is essential for its commercialization. The present study was conducted to evaluate the impact of fungal pretreatment on the enzymatic digestibility of switchgrass under solid-state fermentation (SSF) using Phanerochaete chrysosporium (PC), Trametes versicolor 52J (Tv 52J), and a mutant strain of Trametes versicolor that is cellobiose dehydrogenase-deficient (Tv m4D). Response surface methodology and analysis of variance (ANOVA) were employed to ascertain the optimum pretreatment conditions and the effects of pretreatment factors on delignification, cellulose loss, and total available carbohydrate (TAC). Pretreatment with Tv m4D gave the highest TAC (73.4%), while the highest delignification (23.6%) was observed in the PC-treated sample. Fermentation temperature significantly affected the response variables for the wild-type fungal strains, while fermentation time was the main significant factor for Tv m4D. The result of enzymatic hydrolysis with fungus-treated switchgrass at optimum pretreatment conditions showed that pretreatment with the white-rot fungi enhanced enzymatic digestibility with wild-type T. versicolor (52J)-treated switchgrass, yielding approximately 64.9% and 74% more total reducing sugar before and after densification, respectively, than the untreated switchgrass sample. Pretreatment using PC and Tv 52J at low severity positively contributed to enzymatic digestibility but resulted in switchgrass pellets with low unit density and tensile strength compared to the pellets from the untreated switchgrass.
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 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".