Kinetics of Selenite Adsorption on Hydroxyaluminum‐ and Hydroxyaluminosilicate‐Montmorillonite Complexes
Bibliographic record
Abstract
A lack of understanding about the selenite adsorption behavior on hydroxyaluminum (HyA)‐ and hydroxyaluminosilicate (HAS)‐interlayered phyllosilicates led us to conduct the present study. The kinetics of selenite adsorption on montmorillonite (Mt), HyA(OH/Al = 2.0)‐Mt, HAS1(OH/Al = 2.0; Si/Al = 0.24)‐Mt, and HAS2(OH/Al = 2.0; Si/Al = 0.48)‐Mt were studied at pH 4.5, with an initial selenite concentration of 0.025 m M , a clay concentration of 0.5 g L −1 , temperatures of 288, 298, 308, and 318 K, and background electrolyte concentration of 10 −2 M NaNO 3 Of the six different kinetic models tested, the second‐order rate equation best described the kinetic data obtained for the initial fast reaction (5–30 min) followed by a slow reaction (30–180 min) in the adsorption systems. Elevated temperatures brought about a substantial increase in the rate constants. Compared with Mt, different HyA/HAS‐Mts had 2 to 21 times higher rate constants for the fast reaction and up to five times higher rate constants for the slow reaction. Silication of HyA‐Mt to form HAS1‐Mt and HAS2‐Mt substantially lowered the rate constants for both the fast and slow reactions. For the fast reaction, Mt had the highest activation energy and HyA‐Mt had the lowest activation energy (around four times lower than Mt); silication increased the activation energy of selenite adsorption on the HAS‐Mts. The pre‐exponential factor, an index of the frequency of selenite collision with the clay surface, was remarkably lower for the HyA/HAS‐Mts in comparison with Mt. The data obtained in the present study are of fundamental significance in understanding the role of Al interlayering and coating and silication of Al polymers on expansible phyllosilicates in influencing the dynamics of Se in soil and related environments.
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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".