Dispersion Effects on the Thermodynamics and Transition States of Dimethylarsinic Acid Adsorption on Hydrated Iron (Oxyhydr)oxide Clusters from Density Functional Theory Calculations
Bibliographic record
Abstract
Reaction pathway information and transition states are crucial for understanding adsorption mechanisms of pollutants, such as dimethylarsinic acid (DMA), at the liquid–solid interface. We report a detailed computational analysis of the complexes of DMA on iron (oxyhydr)oxides, including activation energies, transition states, Gibbs free energies of adsorption, Mulliken charges, charge redistribution upon adsorption, and stretching frequencies of As–O bonds for comparison with experimental spectroscopic data. Calculations were performed using density functional theory (DFT) at the B3LYP/6-311+G(d,p) level using both implicit and explicit hydration. For comparison, calculations were also performed for arsenate. Dispersion corrections were included since experimental data showed that DMA forms mostly outer-sphere complexes. Calculated electronic energies indicate that dispersion corrections are important when dealing with outer-sphere complexes, and that there is a high activation barrier of ca. 43 kJ mol –1 to transition from mono- to bidentate DMA complexes. Additionally, extending the modeled iron (oxyhydr)oxides surface to include four Fe centers and analyzing the charge distribution upon adsorption of DMA reveals that electrostatics play a role in the transition from outer-sphere to monodentate complexes. The significance of our results for the overall surface complexation mechanism of DMA and arsenate 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".