Direct Patterning of Self-Assembled Monolayers on Gold Using a Laser Beam
Bibliographic record
Abstract
The development of a methodology to manipulate surface properties of a self-assembled monolayer (SAM) of alkanethiol on a gold film using direct laser patterning is the objective of this paper. The present study demonstrates proof of the concept for the feasibility of laser patterning monolayers and outlines theoretical modeling of the process to predict the resulting feature size. This approach is unique in that it eliminates the need for photolithography, is noncontact, and can be extended to other systems such as SAMs on silicon wafers or potentially polymeric substrates. A homogeneous SAM made of 1-hexadecanethiol is formed on a 300-A sputtered film of gold (supported by a soda lime glass substrate). Localized regions are then desorbed by scanning the focal spot of a 488-nm continuous-wave argon ion laser beam under a nitrogen atmosphere. The desorption occurs as a result of a high substrate temperature produced by the moving laser beam with a Gaussian spatial profile at a constant speed of 200 microm/s. 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, and line widths are measured using both wetting with tridecane under a light microscope and scanning electron microscopy. Using the direct laser patterning method, we have produced straight line patterns with widths of 28-170 microm. A thermal model was constructed to predict the line width of the desorbed monolayer. The effect of the laser power, beam waist, and temperature dependence of the substrate conductivity on the theoretical predictions is considered. It is shown that the theoretical predictions are in good agreement with the experimental results, and, thus, the model can effectively be used to predict experimental results.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".