Co-production of ethanol and cellulose nanocrystals through self-cycling fermentation of wood pulp hydrolysate
Bibliographic record
Abstract
A promising approach to help offset production costs for the cellulosic ethanol industry is to improve ethanol productivity while simultaneously generating value-added by-products. This study reports integration of an advanced fermentation approach (self-cycling fermentation) with the production of cellulose nanocrystals. Specifically, wood pulp was enzymatically hydrolyzed to yield dissolved sugars, which were fed to a self-cycling fermentation system for ethanol production, and residual solids were used for cellulose nanocrystals production via acid hydrolysis. Self-cycling fermentation achieved stable ethanol production for 10 cycles with significantly greater productivity than batch operation: ethanol volumetric productivity increased by 63-95% and annual ethanol productivity by 96 ± 5%. Additionally, the enzyme hydrolysis approach employed did not impede ethanol fermentation, and the cellulose nanocrystals generated displayed properties consistent with previous studies. Taken together, these results highlight the potential of this co-production strategy to produce both cellulosic ethanol and cellulose nanocrystals from a single feedstock.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".