Biodelignification of lignocellulose substrates: An intrinsic and sustainable pretreatment strategy for clean energy production
Bibliographic record
Abstract
Lignocellulosic biomass (LB) is a promising sugar feedstock for biofuels and other high-value chemical commodities. The recalcitrance of LB, however, impedes carbohydrate accessibility and its conversion into commercially significant products. Two important factors for the overall economization of biofuel production is LB pretreatment to liberate fermentable sugars followed by conversion into ethanol. Sustainable biofuel production must overcome issues such as minimizing water and energy usage, reducing chemical usage and process intensification. Amongst available pretreatment methods, microorganism-mediated pretreatments are the safest, green, and sustainable. Native biodelignifying agents such as Phanerochaete chrysosporium, Pycnoporous cinnabarinus, Ceriporiopsis subvermispora and Cyathus stercoreus can remove lignin, making the remaining substrates amenable for saccharification. The development of a robust, integrated bioprocessing (IBP) approach for economic ethanol production would incorporate all essential steps including pretreatment, cellulase production, enzyme hydrolysis and fermentation of the released sugars into ethanol. IBP represents an inexpensive, environmentally friendly, low energy and low capital approach for second-generation ethanol production. This paper reviews the advancements in microbial-assisted pretreatment for the delignification of lignocellulosic substrates, system metabolic engineering for biorefineries and highlights the possibilities of process integration for sustainable and economic ethanol 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".