Hydrogen Isotope Fractionation during the Biodegradation of 1,2-Dichloroethane: Potential for Pathway Identification Using a Multi-element (C, Cl, and H) Isotope Approach
Bibliographic record
Abstract
Even though multi-element isotope fractionation patterns provide crucial information with which to identify contaminant degradation pathways in the field, those involving hydrogen are still lacking for many halogenated groundwater contaminants and degradation pathways. This study investigates for the first time hydrogen isotope fractionation during both aerobic and anaerobic biodegradation of 1,2-dichloroethane (1,2-DCA) using five microbial cultures. Transformation-associated isotope fractionation values (ε bulk H ) were −115 ± 18‰ (aerobic C–H bond oxidation), −34 ± 4‰ and −38 ± 4‰ (aerobic C–Cl bond cleavage via hydrolytic dehalogenation), and −57 ± 3‰ and −77 ± 9‰ (anaerobic C–Cl bond cleavage via reductive dihaloelimination). The dual-element C–H isotope approach (Λ C–H = Δδ 2 H/Δδ 13 C ≈ ε bulk H /ε bulk C, where Δδ 2 H and Δδ 13 C are changes in isotope ratios during degradation) resulted in clearly different Λ C–H values: 28 ± 4 (oxidation), 0.7 ± 0.1 and 0.9 ± 0.1 (hydrolytic dehalogenation), and 1.76 ± 0.05 and 3.5 ± 0.1 (dihaloelimination). This result highlights the potential of this approach to identify 1,2-DCA degradation pathways in the field. In addition, distinct trends were also observed in a multi- (i.e., Δδ 2 H versus Δδ 37 Cl versus Δδ 13 C) isotope plot, which opens further possibilities for pathway identification in future field studies. This is crucial information to understand the mechanisms controlling natural attenuation of 1,2-DCA and to design appropriate strategies to enhance biodegradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".