Bioethanol Production from Biologically Pretreated Prosopis africana Pods using Pichia kudriavzevii SY4
Bibliographic record
Abstract
High costs and ethical issues have prompted research into novel non-food feedstocks for the fermentation-based manufacture of sustainable fuels.In this study, Prosopis africana pods (PAP), an underutilized substrate, was examined for its ability to produce bioethanol.The biomass was pretreated with four mushrooms to delignify it and enhance hydrolysis.A scanning electron microscopy (SEM) was performed on raw and pretreated PAP.The optimum hydrolysis conditions for the pretreated biomass were then determined using the Design of Experiment (DOE) approach.The acid type (HNO3 and H3PO4), concentration (1 %, 3 % and 5 %), solid loading (SL; 5 %, 10 % and 20 %) and contact time (15, 30 and 60 minutes) were optimized using a full-factorial design.The most tolerant yeast isolate from different sources was then molecularly identified after being tested for ethanol tolerance.A half-factorial design was used to screen the fermentation factors, and the Box-Behnken design was used to optimize the relevant components.Ganoderma lucidum showed the most luxurious growth during PAP pretreatment and SEM revealed reduction in biomass crystallinity.The hydrolysis conditions of 5 % HNO3, 20 % SL and 15 minutes contact time were optimal, producing 43.37 ± 0.35 g/L of reducing sugars.The most ethanol-tolerant strain, identified as Pichia kudriavzevii SY4, produced 38.26 g/L bioethanol concentration after RSM optimisation.Similarly, optimisation raised bioethanol concentrations from 26.62 ± 0.00 to 38.26 ± 0.18 g/L, a 43.73 % increase.This work is the first report on utilising Prosopis africana pods in bioethanol 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) | 0.000 | 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".