Estimating strongly wetting to non‐wetting contact angles for pure and mixed liquids on solids by considering film pressure
Bibliographic record
Abstract
Abstract In solid–liquid–vapour systems, the effect of film pressure () by vapour adsorbate molecules becomes significant when the solid surface energy is similar to or larger than that of the liquid. We extend Young equation applicability to estimate contact angles of pure and mixed liquids on smooth solids by including , obtained from a novel dimensionless surface energy parameter. We use the Owens–Wendt–Kaelble interfacial tension model and perturbed‐chain polar statistical associating fluid theory (PCP‐SAFT) to calculate dispersion and non‐dispersion surface energy contributions. The model is developed using diverse solid–liquid systems with 39 liquids and 30 solids. For binary liquid mixtures, conventional PCP‐SAFT and molar‐average mixing rules are used to obtain surface energies. We correlate to dispersion and non‐dispersion contributions to the liquid and solid surface energies and use to improve estimation accuracy. Using a comprehensive dataset, comprising of 107 experimental datapoints, the average absolute deviation (AAD) decreases from 6.5° to 3.6° by including . Using the correlation developed for pure systems, we accurately predict contact angles of binary liquid mixtures. Interestingly, the molar‐average mixing rule gives more accurate contact angle results despite its simplicity where PCP‐SAFT is only executed for the pure components comprising the liquid mixture. Overall, for binary mixtures, the AAD decreases from 9.2° to 4.5° using 105 datapoints and by including . Our proposed framework significantly improves the contact angle estimations and enables the estimation of the contact angle of liquid mixtures for diverse systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".