Antifouling Modification of Gold Surfaces for Acoustic Wave Sensor Applications
Bibliographic record
Abstract
This study aims to develop a robust and reproducible method for fabricating efficient ultrathin antifouling coatings on gold surfaces by leveraging hydroxylation-based surface modifications. An ultrathin antifouling coating of a monoethylene glycol silane derivative, known to reduce fouling by at least 90% on flat hydroxylated surfaces, was successfully replicated on flat gold (reducing fouling by ~75%) by hydroxylating its surface with β-mercaptoethanol. This tandem coating contains the monoethylene glycol silane layer on top of the β-mercaptoethanol on the gold. Characterization was performed using contact angle goniometry, atomic force microscopy, x-ray photoelectron spectroscopy, and antifouling measurements. The results from these techniques, consistent with the literature, confirmed the successful and reproducible application of the tandem coating. Through heterogeneities, including defects and incomplete coverage, the AFM data revealed distinct visible layers of the tandem coating. The direct application of monoethylene glycol silane onto gold resulted in superior antifouling performance (88% reduction), demonstrating that direct silylation exploits pre-existing oxygen-containing species on the gold surface for a more effective antifouling layer. These findings offer a scalable approach for engineering antifouling coatings on gold substrates, with potential applications in biosensing and implantable device antifouling technologies.
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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) | 0.001 | 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 | 0.001 | 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".