Solvent Induced Adhesion Interactions between Dichlorotriazine Films
Bibliographic record
Abstract
This article reports adhesion interactions between silicon-supported dichlorotriazine films in various solvents. The formation, chemical composition, and thickness of the overlayer were analyzed by means of X-ray photoelectron spectroscopy (XPS). An atomic force microscopy (AFM) characterization was performed to evaluate the overlayer roughness. Adhesion interactions were measured using chemical force spectrometry (CFS). The purpose of the study is to understand the effect of solvents on the adhesion force between dichlorotriazine films. The tip–surface adhesion forces measured in octane and cyclooctane were found to be relatively weak. Use of solvents that may participate in π–π interactions, such as toluene and trifluoromethyl benzene, as well as a potential monohalogen bond donor, such as CCl 4, did not lead to significant increase in the tip–surface forces. However, the adhesion forces increase considerably when measured in solvents that contain at least two ether groups, such as dioxane, diethylene glycol dimethyl ether, and triethylene glycol dimethyl ether. These most important interactions in ether-type solvents are due to bridging of the solvent between the two surfaces. Molecular dynamics simulations of the functionalized surfaces are consistent with enhanced solvent bridging interactions when the solvent contains ether functional groups.
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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) | 0.002 | 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".