Innovative strategies for integrating lignocellulosic biomass and microalgae to produce sustainable bioethanol
Bibliographic record
Abstract
Abstract This study investigated the potential of integrating Parachlorella kessleri biomass with corn stover and tree bark residues as a method for producing bioethanol. The aim was to reduce greenhouse gas emissions, promote environmental sustainability while improving as well food security. The saccharification process involved biomass decrystallization and posthydrolysis, demonstrating the potential use of residual biomass from forests and agriculture. Posthydrolysis resulted in an increase in total reducing sugars in both bark and corn stover. An optimal balance was established to maximize the release of fermentable sugars while minimizing the presence of inhibitors, identifying key factors such as posthydrolysis time for bark and corn stover, the lack of a need for microalgae decrystallization, biomass and microalgae concentration, and the ideal integration point of microalgae in lignocellulosic bioethanol production. Bioethanol production was performed through fermentation assays using Saccharomyces cerevisiae yeast. Despite the higher lignin content of bark, combining it with microalgae provided a higher ethanol yield (33%) than combining microalgae with corn stover (29%). This study is the first to investigate integrating lignocellulosic feedstock and algae biomass in a single bioethanol production system to improve the feasibility of producing advanced renewable biofuels in biorefineries.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".