Dual Mechanism Conceptual Model for Cr Isotope Fractionation during Reduction by Zerovalent Iron under Saturated Flow Conditions
Bibliographic record
Abstract
Chromium isotope analysis is rapidly becoming a valuable complementary tool for tracking Cr(VI) treatment in groundwater. Evaluation of various treatment materials has demonstrated that the degree of isotope fractionation is a function of the reaction mechanism, where reduction of Cr(VI) to Cr(III) induces the largest fractionation. However, it has also been observed that uniform flow conditions can contribute complexity to isotope measurements. Here, laboratory batch and column experiments were conducted to assess Cr isotope fractionation during Cr(VI) reduction by zerovalent iron under both static and saturated flow conditions. Isotope measurements were accompanied by traditional aqueous geochemical measurements (pH, Eh, concentrations) and solid-phase analysis by scanning electron microscopy and X-ray absorption spectroscopy. Increasing δ(53)Cr values were associated with decreasing Cr(VI) concentrations, which indicates reduction; solid-phase analysis showed an accumulation of Cr(III) on the iron. Reactive transport modeling implemented a dual mechanism approach to simulate the fractionation observed in the experiments. The faster heterogeneous reaction pathway was associated with minimal fractionation (ε=-0.2‰), while the slower homogeneous pathway exhibited a greater degree of fractionation (ε=-0.9‰ for the batch experiment, and ε=-1.5‰ for the column experiment).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".