Evolution of FTIR technology as applied to chemical detection and quantification
Bibliographic record
Abstract
Both Fourier Transform Infrared (FTIR) spectrometers and sampling techniques have seen a paradigm shift over the past 20 years. Infrared (IR) spectroscopy using the mid IR “fingerprint” region shows excellent specificity for determining the presence and quantity of well over 50000 organic chemical species. Tiny amounts of sample suffice for identification using a chemically inert scratch resistant diamond micro internal reflection crystal. For air quality, FTIR can be used as a point monitor, sniffing air samples in an IR cell or using a long open-air path with a remote reflector or direct passive remote sensing. This makes IR ideal for first responders and haz/mat professionals provided the FTIR is compact, rugged and easy to use in the field. Already FTIR is widely used in industrial plants often directly at the process. In parallel FTIR is increasingly used in mobile field environments including airborne platforms as well as for satellite-based sounders. This paper presents a resume of the evolution of FTIR and sampling technology and the boundaries of applicability of field deployed FTIR chemical sensors for the assessment of suspect substances as well as air pollution at the site of an emergency situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".