Microcantilevers Bend to the Pressure of Clustered Redox Centers
Bibliographic record
Abstract
The redox-activated deflection of microcantilevers has attracted interest for nanoactuation and chemical sensing. Microcantilever sensors are devices that transduce (bio)chemical reactions into a quantifiable nanomechanical motion via surface stress changes. Despite promising applications in analytical science, poor signal-to-noise ratios and a limited understanding of the molecular origins of the surface stress changes that cause the observed deflections remain obstacles to cantilever-based sensing becoming an established (bio)detection method, such as surface plasmon resonance and electrochemistry. We use phase-separated, binary self-assembled monolayers (SAMs) of ferrocenyldodecanethiolate and n-undecanethiolate as a model system to study the effect of the steric crowding of the redox centers on the surface stress change and cantilever deflection produced by the electrochemical oxidation of the surface-tethered ferrocene to ferrocenium. We correlate the measured surface stress change to the fraction of the clustered ferrocenyldodecanethiolate phase in the binary SAMs. The pairing of anions with the sterically crowded clustered ferroceniums induces a collective molecular reorientation which drives the cantilever deflection. The results provide fundamental insights into the response mechanism of microcantilever-based actuating and sensing 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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".