Characterization of Proton Production and Consumption Associated with Microbial Metabolism
Bibliographic record
Abstract
BACKGROUND: Production or consumption of protons in growth medium during microbial metabolism plays an important role in determining the pH of the environment. Such pH changes resulting from microbial metabolism may influence the geochemical speciation of many elements in subsurface environments. Protons produced or consumed during microbial growth were measured by determining the amount of acid or base added in a 5 L batch bioreactor equipped with pH control for different species including Escherichia coli, Geobacter sulfurreducens, and Geobacter metallireducens. RESULTS: An in silico model was used to predict the proton secretion or consumption rates and the results were compared with the data. The data was found to confirm predictions of proton consumption during aerobic growth of E. coli with acetate as the carbon source. However, in contrast to proton consumption observed during aerobic growth of E. coli with acetate, proton secretion was observed during growth of Geobacter species with acetate as the donor and Fe(III) as the extracellular electron acceptor. CONCLUSIONS: In this study, we have also shown that the final pH of the medium can be either acidic or basic depending on the choice of the electron acceptor for the same electron donor. In all cases, the in silico model could predict qualitatively the proton production/consumption rates obtained from the experimental data. Therefore, measurements of pH equivalents generated or consumed during growth can help characterize the microbial physiology further and can be valuable for optimizing practical applications such as microbial fuel cells, where growth associated pH changes can limit current generation rates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".