Gas Chromatography with State-of-the-Art Micromachined Differential Mobility Detection: Operation and Industrial Applications
Bibliographic record
Abstract
Ion mobility spectrometry (IMS) has potential analytical applications in very diverse fields such as chemical, petrochemical, environmental, and, more recently, in drug, chemical warfare agent, and explosives detection. Commercially available IMS instruments are based on time-of-flight (TOF) mass spectrometry. IMS is inherently suitable for field operation as it uses relatively simple microfluidic devices and operates at atmospheric pressure. It is portable, highly sensitive with tunable selectivity, yet can be produced at relatively low cost. Key limitations of this analytical detection technique are low duty cycle, ion cluster formation, short linear dynamic range, and restriction to only positive or negative ion collection in a single analysis. Microelectromechanical system, radio frequency modulated IMS (MEMS RF-IMS), also known as differential mobility spectrometry, has recently been developed and commercialized. The technology is based on IMS, and MEMS RF-IMS offers substantially better performance. In this study, the strengths and limitations of the recently introduced differential mobility detector when used with gas chromatography in trace analyses are discussed and illustrated with applications of industrial significance.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".