Effect of Crude Glycerol from Biodiesel Production on the Performance and Anaerobic Metabolism of Catalysts in a Glycerol Oxidizing Microbial Fuel Cell
Bibliographic record
Abstract
Use of waste glycerol as fuel in microbial fuel cells (MFCs) would result in the production of valuable metabolites and electricity, to the benefit of biodiesel operations. In this research, the effect of salt and other compounds found in waste glycerol from biodiesel production on the metabolism and performance of three cultures (Escherichia coli W3110, Propionibacterium freudenreichii ssp. shermanii and mixed culture AR2), used as anodic catalysts in an MFC was studied. MFC experiments were performed in parallel with serum bottle fermentations to allow for comparison of glycerol consumption and metabolite yield. The effect of salt content on the performance of all three cultures was positive in most cases and negligible in others. Using waste glycerol with an increased concentration of other compounds (other than salt) only reduced the performance of AR2, however an inhibitory effect on the rate of glycerol consumption was observed with both AR2 and P. freudenreichii ssp. shermanii. For all strains, the rate of glycerol consumption was slower in MFCs than in fermentations as a result of the electrochemical environment; the yield of various metabolites also differed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".