Wetting Behavior of Nanoscale Thin Films of Selected Organic Compounds and Water on Model Basal Surfaces of Kaolinite
Bibliographic record
Abstract
Molecular dynamics (MD) simulation and density functional theory (DFT) were applied to study the wetting behavior of nanoscale thin films of n -heptane, toluene, pyridine, and water at different thicknesses on the two model basal surfaces of kaolinite at room temperature. Despite the hydrophilicity of the basal surfaces, water exhibited the lowest affinity for them, but was statistically comparable to that of n -heptane, as quantified by the corresponding calculated monomolecular layer works of adhesion. The results are consistent with the simulations in which water molecules in monomolecular layer originally sandwiched between a monomolecular layer of aromatic compounds and the two basal surfaces departed from the surfaces, while the aromatic molecules migrated to the surfaces. However, such behavior was not observed in the cases in which water thin films contained multiple molecular layers of water. Interestingly, the corresponding calculated multimolecular layer works of adhesion of water were the highest among the compounds of interest. Analysis of the simulation data on both basal surfaces suggests that such observation is attributed to the water/water long-range charge–charge (69%, averaged over the two basal surfaces) and short-range hydrogen-bond (31%) interactions. Here, interfacial hydrogen bonds play a relatively minor role in the wetting behavior of water.
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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".