Surface Free Energy Estimation: A New Methodology for Solid Surfaces
Bibliographic record
Abstract
Abstract An interpretation of solid surfaces is generated based on physical considerations and the laws of thermodynamics. Like the widely used Owens–Wendt (OW) method, the proposed method uses liquids for characterization. Each liquid provides an absolute lower bound on the surface energy with some uncertainty from measurement variations. If multiple liquids are employed, the largest lower bound is taken as the most accurate, with uncertainty due to measurement errors. The more liquids used, the more accurate is the greatest lower bound. This method links generalizations of the Good–Girifalco equation with a general thermodynamic inequality relating the three‐interfacial tensions in a three‐phase equilibrium system. The method always satisfies this inequality with better than a 65% certainty. However, the OW seldom, if ever, conforms to this inequality and even then, the degree of satisfaction is insignificant. A reconciliation of the two methods is proposed based on rescaling the OW surface energies to conform to the inequality. This enables interpretations of dispersion and polar components of the surface energy, which are thermodynamically self‐consistent. The proposed method is also capable of dealing with material exchange between liquid and solid phases, when the surface tension and contact angle of the saturated liquids can be measured.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".