Theoretical study of thermal response of bimaterial microcantilevers with different coating materials
Bibliographic record
Abstract
Bilayer microcantilevers are a versatile tool in thermal and bio-sensing with responses relying on the mismatch between the two constituting materials. The cantilever response, such as a deflection and resonance frequency shift, could be involved when the cantilever is in contact with an arbitrary heat source in the ambient environment. In this study, thermally induced deflection will be theoretically examined assuming a heat source located at various positions on the cantilever. The combined contributions of heat absorption, thermal conductivity, and material rigidity on the final deflection will be revealed. Selecting an optimal position leads to 1.5 times enhancement of the cantilever deflection in comparison to thermal excitation at the cantilever end in conventional experiments, which implies a significant increase in thermal sensitivity. Furthermore, responses of cantilevers with different coating materials (Au, Al, Cu, or Ni) have been examined and show a dominant sensitivity of Al- and Ni- over Cu- and Au-coated cantilevers. These results could help to explain recent experimental results and to choose an optimal thermal excitation of microcantilevers in sensing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".