Modeling the microbial pretreatment of camelina straw and switchgrass by Trametes versicolor and Phanerochaete chrysosporium via solid-state fermentation process: A growth kinetic sub-model in the context of biomass-based biorefineries
Bibliographic record
Abstract
Advancing microbial pretreatment of lignocellulose has the potential not only to reduce the carbon footprint and environmental impacts of the pretreatment processes from cradle-to-grave, but also increase biomass valorization, support agricultural growers, and boost the bioeconomy. Mathematical modeling of microbial pretreatment of lignocellulose provides insights into the metabolic activities of the microorganisms as responses to substrate and environment and provides baseline targets for the design, development, and optimization of solid-state-fermentation (SSF) bioreactors, including substrate concentrations, heat and mass transfer. In this study, the growth of Trametes versicolor 52J (TV52J), Trametes versicolor m4D (TVm4D), and Phanerochaete chrysosporium (PC) on camelina straw (CS) and switchgrass (SG) during an SSF process was examined. While TV52J illustrated the highest specific growth rate and maximum cell concentration, a mutant strain deficient in cellulose catabolism, TVm4D, performed best in terms of holocellulose preservation and delignification. The hybrid logistic-Monod equation along with holocellulose consumption and delignification models described well the growth kinetics. The oxygen uptake rate and carbon dioxide production rate were directly correlated to the fungal biomass concentration; however, a more sophisticated non-linear relationship might explain those correlations better than a linear model. This study provides an informative baseline for developing SSF systems to integrate fungal pretreatment into a large-scale, on-farm, wet-storage process for the utilization of agricultural residues as feedstocks for biofuel production.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".