Determination of Anisotropic Surface Characteristics of Different Phyllosilicates by Direct Force Measurements
Bibliographic record
Abstract
To fundamentally understand the electrokinetic behavior of clay minerals, it is necessary to study the anisotropic surface charge properties of clay surfaces. In this study, two 2:1 layer natural minerals, talc and muscovite, were chosen as representatives of magnesium and aluminum phyllosilicate minerals, respectively. The molecularly smooth basal planes of both platy minerals were obtained by cleavage along the basal planes, while suitable edge surfaces were prepared by an ultramicrotome cutting technique. Silicon nitride atomic force microscopy tip was used as a probe to study the interaction forces between the tip and clay basal/edge surfaces in aqueous solutions of various pH values. The measured interaction force profiles between the tip and clay basal/edge surfaces were fitted with the classical DLVO (Derjaguin-Landau-Verwey-Overbeek) theory, which allows direct determination of electrical surface potential of talc and muscovite surfaces. The surface potential of muscovite basal planes was found to be significantly more negative than the basal plane of talc, both being pH insensitive. In contrast, the surface potential of edge surfaces was highly pH-dependent, exhibiting a point of zero charge (PZC) at pH 7.5 and 8.1 for edges of muscovite and talc, respectively. The observed differences in surface potential of basal planes and edge surfaces for both talc and muscovite are closely related to their crystal structure and ionization characteristics. The protonation reactivity and the contribution of each surface group to the surface charging behavior are modeled using their protonation constants.
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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 | 0.001 | 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".