Photothermal cantilever deflection spectroscopy
Bibliographic record
Abstract
Microcantilever sensors offer high sensitivity in the detection of adsorbed molecules based either on resonance frequency shift or changes in cantilever deflection, as both of these signals can be detected with very high resolution. Despite the high sensitivity offered by this platform, cantilevers suffer from poor selectivity due to the lack of sufficiently selective interfacial layers which can be immobilized on cantilever surfaces. This problem can be overcome by using photothermal cantilever deflection spectroscopy (PCDS), which exploits the high thermomechanical sensitivity of bi-material microcantilevers. A bi-material cantilever responds to heat generated by the nonradiative decay process when the adsorbed molecules are resonantly excited with infrared (IR) light. The variation in the cantilever deflection as a function of illuminating IR wavelength corresponds to the conventional IR absorption spectrum of the adsorbed molecules. In addition, the mass of the adsorbed molecules can be determined by measuring the resonance frequency shift of the cantilever as an orthogonal signal for the quantitative analysis. This multi-modal PCDS offers unprecedented opportunities for obtaining very high selectivity in chemical and biological sensing without using selective interfacial layers or extrinsic labels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".