Direct writing of self-assembled monolayers on gold coated substrates using a CW argon laser
Bibliographic record
Abstract
The ability to engineer surface properties such as hydrophobicity, charge, and adhesion at the micrometer scale is the key to developments in emerging technologies (e.g. bio-sensors, and barrier-free microfluidic systems). Development of a methodology to manipulate surface properties of a self-assembled monolayer of alkanethiol on a gold film was the objective of this paper. This system is broadly studied and widely believed to serve as the platform of choice to develop a variety of biological technologies. The proposed approach is unique in that it eliminates the need for photolithography, is non-contact, and can be extended to other systems such as SAMs on silicon wafers or polymeric substrates. For this study, an initial hydrophobic monolayer of l-hexadecanethiol on a 300 /spl Aring/ gold sputtered film is used. Localized regions are then desorbed in a nitrogen atmosphere by scanning the focal spot of a 488 nm CW Argon ion laser beam. The beam with a Gaussian spatial profile was scanned at a rate slower than the heat diffusion rate along the surface. After completing the scans, the sample is dipped into a dilute solution of 16-mercaptohexadecanoic acid and a hydrophilic monolayer self-assembles along the previously irradiated regions. The resultant lines are viewed by wetting with tridecane.
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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".