Kinetics of Substrate Biodegradation under the Cumulative Effects of Bioavailability and Self-Inhibition
Bibliographic record
Abstract
Microbial degradation is an important process in many environments controlling for instance the cycling of nutrients or the biodegradation of contaminants. At high substrate concentrations toxic effects may inhibit the degradation process. Bioavailability limitations of a degradable substrate can therefore either improve the overall dynamics of degradation by softening the contaminant toxicity effects to microorganisms, or slow down the biodegradation by reducing the microbial access to the substrate. Many studies on biodegradation kinetics of a self-inhibitive substrate have mainly focused on physiological responses of the bacteria to substrate concentration levels without considering the substrate bioavailability limitations rising from different geophysical and geochemical dynamics at pore-scale. In this regard, the role of bioavailability effects on the kinetics of self-inhibiting substrates is poorly understood. In this study, we theoretically analyze this role and assess the interactions between self-inhibition and mass transfer-limitations using analytical/numerical solutions, and show the findings practical relevance for a simple model scenario. Although individually self-inhibition and mass-transfer limitations negatively impact biodegradation, their combined effect may enhance biodegradation rates above a concentration threshold. To our knowledge, this is the first theoretical study describing the cumulative effects of the two mechanisms together.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".