Ozone alteration for background references using QCL-based mid infrared standoff spectroscopy
Bibliographic record
Abstract
Mid-Infrared standoff spectroscopy using Quantum Cascade Lasers has been a focus of on-going research for many years. When attempting to detect trace analyte residues, the greatest challenge facing this technology is not in the lasers, but the difficulty in creating a spectroscopic background reference for an unknown surface. Such techniques as Differential Location Measurements fail when analyte concentrations are below 1 μg/cm2. To overcome this challenge of unknown surface backgrounds, we propose a technique to alter the IR absorption peaks of a target analyte by exposing the surface to a high intensity, alternating electric field in a standoff fashion. The high intensity electric field generates ozone radicals from the local air, oxidizing organic compounds on the surface. A spectrum of the surface before and after the ozone radicals is obtained. The ozone altered spectrum acts as the reference background and is compared against the un-altered spectrum, generating a differential signal used to identify the target analyte.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".