Multi-modal, ultrasensitive detection of trace explosives using MEMS devices with quantum cascade lasers
Bibliographic record
Abstract
Multi-modal chemical sensors based on microelectromechanical systems (MEMS) have been developed with an electrical readout. Opto-calorimetric infrared (IR) spectroscopy, capable of obtaining molecular signatures of extremely small quantities of adsorbed explosive molecules, has been realized with a microthermometer/microheater device using a widely tunable quantum cascade laser. A microthermometer/microheater device responds to the heat generated by nonradiative decay process when the adsorbed explosive molecules are resonantly excited with IR light. Monitoring the variation in microthermometer signal as a function of illuminating IR wavelength corresponds to the conventional IR absorption spectrum of the adsorbed molecules. Moreover, the mass of the adsorbed molecules is determined by measuring the resonance frequency shift of the cantilever shape microthermometer for the quantitative opto-calorimetric IR spectroscopy. In addition, micro-differential thermal analysis, which can be used to differentiate exothermic or endothermic reaction of heated molecules, has been performed with the same device to provide additional orthogonal signal for trace explosive detection and sensor surface regeneration. In summary, we have designed, fabricated and tested microcantilever shape devices integrated with a microthermometer/microheater which can provide electrical responses used to acquire both opto-calorimetric IR spectra and microcalorimetric thermal responses. We have demonstrated the successful detection, differentiation, and quantification of trace amounts of explosive molecules and their mixtures (cyclotrimethylene trinitramine (RDX) and pentaerythritol tetranitrate (PETN)) using three orthogonal sensing signals which improve chemical selectivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".