Compound-Specific Carbon, Nitrogen, and Hydrogen Isotope Analysis to Characterize Aerobic Biodegradation of 2,3-Dichloroaniline by a Mixed Enrichment Culture
Bibliographic record
Abstract
Compound-specific isotope analysis (CSIA) is an established tool to track the in situ transformation of organic chemicals at contaminated sites. In this work, we evaluated the potential of multi-element CSIA to assess biodegradation of 2,3-dichloroaniline (2,3-DCA), which is a major industrial feedstock. Using controlled laboratory experiments, we determined, for the first time, negligible carbon (<0.5‰) and hydrogen (<10‰) isotope fractionation and a significant inverse nitrogen isotope fractionation (>10‰) during aerobic 2,3-DCA biodegradation by a mixed enrichment culture. The tentative identification of a glutamate conjugate of 2,3-DCA as a reaction intermediate indicates that the initial multistep enzymatic reaction may be rate-limiting. The formation of the glutamate adduct would increase the bond energy at the N atom, thus likely explaining the observed inverse N isotope fractionation. The corresponding nitrogen enrichment factor was +6.8 ± 0.6‰. This value was applied to investigate the in situ 2,3-DCA biodegradation at a contaminated site where the carbon and nitrogen isotope signatures from field samples suggested similar aerobic processes by native microorganisms. Under the assumption of the applicability of the Rayleigh model in a pilot wetland treating contaminated groundwater, the extent of biodegradation was estimated to be up to 80–90%. This study proposes multi-element CSIA as a novel application to study 2,3-DCA fate in groundwater and surface water and provides insights into biodegradation pathways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".