Review—Nanomechanical Calorimetric Infrared Spectroscopy using Bi-Material Microfluidic Cantilevers
Bibliographic record
Abstract
Receptor-free, in-situ sensing of chemical and biological analytes with high selectivity and sensitivity is a highly sought-after goal. While a resonating microfluidic, hollow channel microcantilever is an ideal platform for sensing pico liters (pL) of liquid analytes based on changes in the specific gravity it does not offer any chemical selectivity. Fabricating these hollow channel cantilevers as bi-material beams allows them to be extremely sensitive to small changes in temperature as well. When a liquid confined in such a cantilever is illuminated with tunable IR radiation, it undergoes bending whenever the liquid analyte absorbs the light at a particular wavelength. Monitoring the cantilever bending as a function of illuminating wavelength provides IR spectrum of the analyte confined in the channel. This method combines the selectivity of IR spectroscopy and the sensitivity of a cantilever for molecular recognition of pL volume of liquid samples. This nanomechanical calorimetric infrared spectroscopy is an ideal technique for physical and chemical characterization of pico liter volumes of liquid analytes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".