Insights into Enzyme Kinetics of Chloroethane Biodegradation Using Compound Specific Stable Isotopes
Bibliographic record
Abstract
While compound specific isotope analysis (CSIA) has been used extensively to investigate remediation of chlorinated ethenes, to date considerably less information is available on its applicability to chlorinated ethanes. In this study, biodegradation of 1,1,1-trichloroethane (1,1,1-TCA) and 1,1-dichloroethane (1,1-DCA) was carried out by a Dehalobacter-containing mixed culture. Carbon isotope fractionation factors (ε) measured during whole cell degradation demonstrated that values for 1,1,1-TCA and 1,1-DCA (-1.8‰ and -10.5‰, respectively) were significantly smaller than values reported for abiotic reductive dechlorination of these same compounds. Similar results were found in experiments degrading these two priority pollutants by cell free extracts (CFE) where values of -0.8‰ and -7.9‰, respectively, were observed. For 1,1,1-TCA in particular, the large kinetic isotope effect expected for cleavage of a C-Cl bond was almost completely masked during biodegradation by both whole cells and CFE. Comparison to previous studies demonstrates that these patterns of isotopic fractionation are not attributable to transport effects across the cell membrane, as had been seen for other compounds such as PCE. In contrast these results reflect significant differences in the kinetics of the enzymes catalyzing chlorinated ethane degradation.
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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 | 0.001 | 0.000 |
| 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".