Probing Interaction of Divalent Cations with Illite Basal Surfaces by Atomic Force Microscopy
Bibliographic record
Abstract
Smooth basal surfaces of illite were prepared successfully, and the interaction forces between a Si₃N₄ tip and illite basal surface with various concentrations of divalent cations in 10 mM KCl solution at pH 8.0 and 10.8 were measured using an atomic force microscope (AFM). The influence of divalent cation concentration on the interaction forces was investigated, and the surface potentials of the basal surfaces in varying concentrations of divalent cation solutions were determined by fitting the interaction forces with the classical Derjaguin–Landau–Verwey–Overbeek (DLVO) theory. With an increasing Caᴵᴵ or Mgᴵᴵ concentration, the surface potentials became less negative in pH 8.0 solution, while the surface potentials reversed from negative to positive after addition of 1.0 mM or more divalent cations in pH 10.8 solution. Electrostatic interaction and specific adsorption were the major contributors to the interaction between divalent cations and illite basal surface in solutions at pH 8.0 and 10.8, respectively. Caᴵᴵ and Mgᴵᴵ cations had a similar effect magnitude on the illite basal surfaces at pH 8.0. In contrast, Mgᴵᴵ cations had a more profound effect than Caᴵᴵ on the surface potential of the illite basal surface in pH 10.8 solution, likely due to the low solubility of Mg(OH)₂ at this pH. This quantitative description of the illite basal surface charge influenced by divalent cations can provide fundamental insight into the mechanisms of tailings treatment and slime coatings in minerals processing.
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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 | 0.001 | 0.001 |
| 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".