Nano-Optomechanical Systems for Gas Chromatography
Bibliographic record
Abstract
Microgas chromatography (GC) is promising for portable chemical analysis. We demonstrate a nano-optomechanical system (NOMS) as an ultrasensitive mass detector in gas chromatography. Bare, native oxide, silicon surfaces are sensitive enough to monitor volatile organic compounds at ppm levels, while simultaneously demonstrating chemical selectivity. The NOMS is able to sense GC peaks from derivatized metabolites at physiological concentrations. This is an important milestone for small-molecule quantitation assays in next generation metabolite analyses for applications such as disease diagnosis and personalized medicine. The optical microring, which plays an important role in the nanomechanical signal transduction mechanism, can also be used as an analyte concentration sensor. Different adsorption kinetics regimes are realized at different temperatures allowing temporary condensation of the analyte onto the sensor surfaces. This effect amplifies the signal, resulting in a 1 ppb level limit of detection, without partition enhancement from absorbing media. This sensitivity bodes well for NOMS as universal, ultrasensitive detectors in micro-GC, breath analysis, and other chemical-sensing applications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".