The Impact of Solution Agglomeration on the Deposition of Self-Assembled Monolayers
Bibliographic record
Abstract
Self-assembled monolayers (SAMs) are commonly produced by immersing substrates in organic solutions containing trichlorosilane coupling agents. Unfortunately, such deposition solutions can also form alternate structures, including inverse micelles and lamellar phases. The formation of alternate phases is one reason for the sensitivity of SAM depositions to factors such as the water content of the deposition solvent. If such phases are present, the performance of thin films used for applications such as the minimization of friction and stiction in micromachines can be seriously compromised. Inverse micelle formation has been studied in detail for depositions involving 1H,1H,2H,2H-perfluorodecyltrichlorosilane (FDTS) in isooctane. Nuclear magnetic resonance experiments have been used to monitor the kinetics of hydrolysis and condensation reactions between water and FDTS. Light-scattering experiments show that there is a burst of nucleation at a critical concentration of hydrolyzed FDTS to form high concentrations of spherical agglomerates. Atomic force microscopy results show that the agglomerates then deposit on substrate surfaces. Deposition conditions leading to monolayer formation involve using deposition times that are short relative to the induction time for agglomeration. After deposition, inverse micelles can be converted into lamellar or monolayer structures with appropriate heat treatments if surface concentrations are relatively low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".