Major considerations for using surface energy models as a tool for surface modification and adhesion improvement purposes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• As-placed contact angles fail to capture improved surface functionality in some cases. • Receding contact angle measurements are preferred for use in adhesion improvement studies. • The contribution of non-dispersive interactions is often underestimated by SFE models. • For adhesion studies, our method proposes using advancing and receding contact angles. • Small increases in surface functionality can lead to >100 % adhesion improvement. Direct measurement of Surface Free Energy (SFE) for solid surfaces is not feasible; thus, the Young-Dupré equation, in conjunction with appropriate SFE models, is employed for its estimation. This study explores the impact of surface treatment of polyethylene (PE) on its SFE and adhesion properties and addresses two main challenges: identifying the ideal contact angle (CA) and selecting a reliable SFE model. Five distinct surface treatments were applied to PE and as-placed, advancing, and receding CAs were measured with five probe liquids. Comparative analysis with Attenuated Total Reflectance - Fourier Transform Infrared Spectroscopy (ATR-FTIR) and X-ray Photoelectron Spectroscopy (XPS) revealed inconsistencies with as-placed and advancing CAs, while receding CAs provided better correlation with surface modifications. SFE and work of adhesion (W ad ) calculations were performed using multiple SFE models, including the two-component OWRK, the three-component VOGC, and a novel four-component Partial Solvation Parameters (PSPs) model. Using as-placed CAs led to unreliable adhesion predictions, while employing receding CAs aligned more closely with adhesion measurements. All models underestimated the contribution of non-dispersive interactions to the total SFE and W ad , particularly when using as-placed CA. A new two-step method utilizing both advancing and receding CAs is proposed, demonstrating improved correlation with FTIR, XPS, and adhesion data, and attributing most adhesion improvements to non-dispersive interactions. This work highlights the need for refined SFE models and CA measurement techniques for accurately assessing modified surfaces in adhesion studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it