Kinetic and Thermodynamic Study of Ag+, Cu2+, and Zn2+ Ion Adsorption on LTA for High-Performance Antibacterial Coating
Bibliographic record
Abstract
Antibacterial powder coatings have attracted increasing attention with the awakening of people’s health awareness. Silver antibacterial agent has been widely used in coating system due to its superior stability and durability. However, silver ions have the problems of excessive release rate and the tendency to cause yellowing of the coating film. The addition of Cu2+ and Zn2+ can effectively alleviate these two phenomena. In this paper, the ternary exchange kinetics of Ag+, Cu2+, and Zn2+ were studied to provide a theoretical basis for the synthesis of LTA-Ag-Cu-Zn. The reaction kinetics study shows that the selectivity and the adsorption capacity of LTA to Ag+ is higher than that of Cu2+ and Zn2+. The thermodynamic analysis discovers that LTA has the highest selectivity for Ag+, and the exchange between the two is spontaneous. In contrast, the selectivity of LTA to Cu2+ and Zn2+ is concentration-dependent. By establishing the three-ion competitive adsorption curve, it is found that the selectivity of Ag+ is the highest, and the selectivity of copper and zinc is similar. These trends result from Ag+ ions’ low hydration energy, small hydration radius, and strong electronegativity. This research lays the groundwork for developing high-performance LTA-Ag-Cu-Zn tri-ion exchange antibacterial agents.
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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 | 0.001 |
| 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".