Microcantilever-Based Sensors: Effect of Morphology, Adhesion, and Cleanliness of the Sensing Surface on Surface Stress
Bibliographic record
Abstract
The surface stress response of micromechanical cantilever-based sensors was studied as a function of the morphology, adhesion, and cleanliness of the gold sensing surface. Two model systems were investigated: the adsorption of alkanethiol self-assembled monolayers at the gas-solid interface and the potential-controlled adsorption of anions at the liquid-solid interface. The potential-induced surface stress, on a smooth and continuous polycrystalline Au(111)-textured microcantilever in 0.1 M HClO4, is in excellent agreement with macroscopic Au(111) single-crystal electrode results. It is shown that ambient contaminants on the sensing surface dramatically alter the surface stress-potential response. This observation can be misinterpreted as evidence that for polycrystalline Au(111) microcantilever electrodes, surface stress is dominated by surface energy change. Results for anions adsorption on gold are in contrast to the gas-phase model system. We demonstrate that the average grain size of the gold sensing surface strongly influences the magnitude of the surface stress change induced by the adsorption of octanethiol. A 25-fold amplification of the change in surface stress is observed on increasing the average gold grain size of the sensing surface from 90 to 500 nm.
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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 | 0.001 |
| 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 | 0.001 | 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".