Effect of Iron Type on Kinetics and Carbon Isotopic Enrichment of Chlorinated Ethylenes During Abiotic Reduction on Fe(0)
Bibliographic record
Abstract
Four samples of two commercially available iron brands used as substrate for iron permeable reactive barriers (PRBs) were tested for suitability for remediation of perchloroethylene (PCE), trichloroethylene (TCE), cis-dichloroethylene (cDCE) and vinyl chloride (VC). Kinetic studies indicate that rates of reaction are enhanced for cDCE and VC on Connelly iron (2.8 x 10(-4) to 6.9 x 10(-4) L/m2/hr and 2.0 x 10(-4) to 9.0 x 10(-4) L/m2/hr, for cDCE and VC, respectively) vs. Peerless iron (3.1 x 10(-5) to 4.6 x 10(-5) L/m2/hr and 2.4 x 10(-5) to 4.1 x 10(-5) L/m2/hr, for cDCE and VC, respectively). Carbon isotopic analyses of the residual chlorinated ethylene (CE) during degradation indicate significant fractionation occurs during reductive dechlorination, with, for example, up to 70% enrichment in carbon isotopic values observed when VC is more than 99% degraded. Comparison of fractionation factors (epsilon) indicates significant differences in carbon isotopic fractionation for different iron types and for different CEs. For the lower CEs (cDCE and VC) in particular, both slower reaction rates and larger fractionation are observed for degradation on Peerless vs. Connelly iron. This is the first study to establish a correlation between the rate of abiotic degradation on Fe(0) and the extent of isotopic fractionation, and the first to confirm consistent differences in these two parameters as a function of iron type. The possibility that these differences in kinetics and carbon isotopic fractionation for cDCE and VC are related to differences in branching ratios between competing hydrogenolysis and beta-elimination reactions during reductive dechlorination on the iron surfaces is discussed.
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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) | 0.000 | 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) | 0.001 | 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".