Greener approach to the comprehensive utilization of algal biomass and oil using novel Clostridial fusants and bio-based solvents
Bibliographic record
Abstract
A greener method has been tested to utilize algal biomass as a feedstock to produce bio-oil in addition to acetone, butanol, and ethanol (ABE) products. Various hydrolysis treatments were used prior to fermentation including combination of thermal, chemical, and enzymatic, which resulted in maximum sugar release of 27.78 g/L. Bio-based terpenes was used instead of common toxic chemicals together with Clostridial fustants to produce bio-alcoholic fuels. Protoplast fusion technique were used to produce the novel Clostridia fusants (C. beijernickii + C. thermocellum and C. acetobutylicum + C. thermocellocum). Fused strains were then subjected to UV radiation for strain enhancement. Final fusansts showed clear improvement in thermal stability and resistance to biobutanol toxicity. Fermentation experiments showed maximum biobutanol final production of 7.98 g/L using CbCt versus 7.39 g/L using CaCt. Oil extraction from virgin algae was tested using a green, bio-based approach using terpenes with ultrasonication and green Bligh and Dyer method, separately. In preliminary study on algal biomass, the combinations of ultrasonication followed by the green Bligh and Dyer have resulted in oil yield of 46.27% (d-limonene) and 39.85% (p-cymene). Oil extraction from an algae sample following fermentation using the combined extraction method resulted in significantly higher oil yield of 65.04%.
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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 | 0.000 | 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".