Impact of Structural Perturbation of Aluminum Hydroxides by Tannate on Arsenate Adsorption
Bibliographic record
Abstract
The impacts of the biomolecule‐induced structural perturbation of Al hydroxides and the resultant alteration of their surface reactivity toward the adsorption of nutrients and contaminants have received, to date, scant attention, in spite of their significance in determining the mineralogy and surface chemistry of these mineral colloids. This study investigated the equilibria and kinetics of As(V) adsorption on a crystalline Al hydroxide, a pure amorphous Al hydroxide and a short‐range ordered Al–tannate coprecipitate. Isotherms and kinetics of As(V) adsorption were conducted at pH 6.5; the kinetic experiments (0.083–24 h) were performed at 288, 298, 308, and 318 K. The adsorption data followed multiple second‐order kinetics, with an initial fast reaction step, followed by a slow reaction. While As(V) adsorption on the crystalline Al hydroxide was a rapid process, the poorly ordered minerals required longer contact intervals and greater activation energies. Compared with the pure amorphous Al hydroxide, the incorporation of tannate into the structural network of Al hydroxide decreased the adsorption rate, capacity, and affinity for As(V). These effects were attributable to the blocking of part of the adsorption sites by tannate, to the electrostatic repulsion induced by the net negative charge caused by the deprotonated organic molecules exposed on the surface of the Al–tannate coprecipitate, and to the steric hindrance of tannate, hampering access of the adsorbate to the micropores. These findings are of fundamental significance in understanding the sorption behavior and mobility of As as influenced by biomolecule‐induced structural perturbation of Al hydroxides in the environment.
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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 | 0.000 |
| 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) | 0.001 | 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".