Stability of Silanols and Grafted Alkylsilane Monolayers on Plasma-Activated Mica Surfaces
Bibliographic record
Abstract
We investigated the effect of physical and chemical modifications of mica surfaces induced by water vapor-based plasma treatments on the stability of silanols and grafted alkylsilane monolayers. The plasma-activated substrates were characterized using XPS, TOF-SIMS, and contact angle measurements. They revealed a large surface coverage of silanol groups (Si-OH) and a loss of aluminum atoms compared to freshly cleaved mica surfaces. The stability of plasma-induced silanol groups was investigated by contact angle measurements using ethylene glycol as a probe liquid. The Si-OH surface coverage decreased rapidly under vacuum or thermal treatment to give rise to hydrophobic dehydrated surfaces. The stability of end-grafted monofunctionalized n-alkylsilanes was investigated in different solvents and at different pH using water contact angle measurements. The degrafting of alkylsilanes from the activated mica was promoted in acidic aqueous solutions. This detachment was associated with the hydrolysis of covalent bonds between the alkylsilanes and the mica surface. The monolayer stability was enhanced by increasing the length of the alkyl chains that probably act as a hydrophobic protective layer against hydrolysis reactions. Stable alkylsilane monolayers in water with pH greater than 5.5 were obtained on mica surfaces activated at low plasma pressure. We attributed this stability to the loss of surface Al atoms induced by the plasma treatment.
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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".