Enriched hydrogen production by bioconversion of biodiesel waste supplemented with ferric citrate and its nano-spray dried particles
Bibliographic record
Abstract
Increasing consumption of fossil fuels as well as the concern over pollution and global climate change has accelerated the development of the sustainable biofuel industry. Biodiesel, bioethanol and biomethane are already commercially available as alternatives of fossil fuels and the search for a more environmentally friendly biofuel, preferentially produced from non-food raw materials and capable of fulfilling the transportation energy requirement of the world for longer duration, is ongoing. In this context, biohydrogen produced from waste biomass is an ideal option. It has higher energy content compared to fossil and biofuels of equivalent mass and produces water as the only major emission during combustion. In the present investigation, crude glycerol (CG) generated as by-product of the biodiesel production process has been used as feedstock for biohydrogen production and different supplements have been evaluated for increasing the product yield. Nano-spray dried ferric citrate particles have been found to enhance the hydrogen production by 50.45%. Hydrogen production using extremely low CG concentration of 100 mg L−1 has been found to produce 22.7 mol-H2 per kg CG; which is 2.75-fold higher than 8.25 mol-H2 per kg CG, known for dark fermentation.
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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".