AFM-assisted fabrication of thiol SAM pattern with alternating quantified surface potential
Bibliographic record
Abstract
Thiol self-assembled monolayers (SAMs) are widely used in many nano- and bio-technology applications. We report a new approach to create and characterize a thiol SAMs micropattern with alternating charges on a flat gold-coated substrate using atomic force microscopy (AFM) and Kelvin probe force microscopy (KPFM). We produced SAMs-patterns made of alternating positively charged, negatively charged, and hydrophobic-terminated thiols by an automated AFM-assisted manipulation, or nanografting. We show that these thiol patterns possess only small topographical differences as revealed by AFM, and distinguished differences in surface potential (20-50 mV), revealed by KPFM. The pattern can be helpful in the development of biosensor technologies, specifically for selective binding of biomolecules based on charge and hydrophobicity, and serve as a model for creating surfaces with quantified alternating surface potential distribution.
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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".