Density Functional Theory Calculations on the Complexation of <i>p</i>-Arsanilic Acid with Hydrated Iron Oxide Clusters: Structures, Reaction Energies, and Transition States
Bibliographic record
Abstract
Aromatic organoarsenicals, such as p-arsanilic acid (pAsA), are still used today as feed additives in the poultry and swine industries in developing countries. Through the application of contaminated litter as a fertilizer, these compounds enter the environment and interact with reactive soil components such as iron and aluminum oxides. Little is known about these surface interactions at the molecular level. We report density functional theory (DFT) calculations on the energies, optimal geometries, and vibrational frequencies for hydrated pAsA/iron oxide complexes, as well as changes in Gibbs free energy, enthalpy, and entropy for various types of ligand exchange reactions leading to both inner- and outer-sphere complexes. Similar calculations using arsenate are also shown for comparison, along with activation barriers and transition state geometries between inner-sphere complexes. Minimum energy calculations show that the formation of inner- and outer-sphere pAsA/iron oxide complexes is thermodynamically favorable, with the monodentate mononuclear complexes being the most favorable. Interatomic As-Fe distances are calculated to be between 3.3 and 3.5 Å for inner-sphere complexes and between 5.2 and 5.6 Å for outer-sphere complexes. In addition, transition state calculations show that activation energies greater than 23 kJ/mol are required to form the bidentate binuclear pAsA/iron oxide complexes, and that formation of arsenate bidentate binuclear complexes is thermodynamically -rather than kinetically- driven. Desorption thermodynamics using phosphate ions show that reactions are most favorable using HPO4(2-) species. The significance of our results for the overall surface complexation mechanism of pAsA 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".