Metal 3D‐printed catalytic jet and flame ionization detection for in situ trace carbon oxides analysis by gas chromatography
Bibliographic record
Abstract
Abstract A gas chromatographic approach for the determination and quantification of trace levels of carbon oxides in gas phase matrices for in situ or near‐line/at‐line analysis has been successfully developed. Catalytic conversion of the target compounds to methane via the methanation process was conducted inside a metal 3D‐printed jet that also acted as a hydrogen burner for the flame ionization detector. Modifications made to a field transportable gas chromatograph enabled the leveraging of advantaged microfluidic‐enhanced chromatography capability for improved chromatographic performance and serviceability. The compatibility with adsorption chromatography technology was demonstrated with in‐house constructed columns. Sustained reliable conversion efficiencies of greater than 99% with respectable peak symmetries were attained at 400°C. Quantification of carbon monoxide and carbon dioxide at a parts‐per‐million level over a range from 0.2 ppm to 5% v/v for both compounds with a respectable precision of less than 3% relative standard deviation for peak area (n = 10) and a detection limit of 0.1 ppm v/v was achieved. Linearity with correlation coefficients of R2 greater than 0.9995 and measured recoveries of >99% for spike tests were achieved. The 3D‐printed steel jet was found to be reliable and resilient against potential contamination from the matrices owing to the in situ backflushing capability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".