Electronic Nose for Recognition of Volatile Vapor Mixtures Using a Nanopore-Enhanced Opto-Calorimetric Spectroscopy
Bibliographic record
Abstract
An electronic nose (e-nose) for identification and quantification of volatile organic compounds (VOCs) vapor mixtures was developed using nanopore-enhanced opto-calorimetric spectroscopy. Opto-calorimetric spectroscopy based on specific molecular vibrational transitions in the mid infrared (IR) "molecular fingerprint" regime allows highly selective detection of VOCs vapor mixtures. Nanoporous anodic aluminum oxide (AAO) microcantilevers, fabricated using a two-step anodization and simple photolithography process, were utilized as highly sensitive thermomechanical sensors for opto-calorimetric signal transduction. The AAO microcantilevers were optimized by fine-tuning AAO nanopore diameter in order to enhance their thermomechanical sensitivity as well as their surface area. The thermomechanical sensitivity of a bilayer AAO microcantilever with a 60 nm pore diameter was approximately 1 μm/K, which is far superior to that of a bilayer plain silicon (Si) microcantilever. The adsorbed molecules of VOCs mixtures on the AAO microcantilever were fully recognized and quantified by variations of peak positions and amplitudes in the opto-calorimetric IR spectra as well as by shifts in the resonance frequency of the AAO microcantilever with the adsorbed molecules. Furthermore, identification of complex organic compounds with a real industrial sample was demonstrated by this e-nose system.
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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) | 0.001 | 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".