Nitrogen-containing aromatic compounds: quantitative analysis using gas chromatography with nitrogen phosphorus detector
Bibliographic record
Abstract
Abstract The nitrogen-containing aromatic compounds found in the petrochemical industry are varied and extend beyond classes such as the anilines, pyrroles and pyridines. Quantification of these nitrogen-containing compounds that may occur in complex mixtures has practical application for quality assurance, process development and the evaluation of conversion processes. Selective detection of nitrogen-containing species in complex mixtures is possible by making use of gas chromatography coupled with a nitrogen phosphorous detector (GC-NPD), which is also called a thermionic detector. Despite the linearity of the NPD response to individual nitrogen-containing compounds, the response factor is different for different compounds and even isomers of the same species. Quantitative analysis using an NPD requires species-specific calibration. The reason for the sensitivity of the NPD to structure is related to the ease of forming the cyano-radical that is ionized to the cyanide anion, which is detected. The operation of the NPD was related to the processes of pyrolysis and subsequent ionization. It was possible to offer plausible explanations for differences in response factors for isomers based on pyrolysis chemistry. Due to this relationship, the NPD response can in the same way be used to provide information of practical relevance beyond its analytical value and a few possible applications were outlined.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".