Determination of Seven Nitrobenzene Compounds in Mainstream Cigarette Smoke with Heart-Cutting Two-Dimensional Gas Chromatography
Bibliographic record
Abstract
A heart-cutting two-dimensional gas chromatography (GC) method was developed for the determination of nitrobenzene compounds (NBCs) in mainstream cigarette smoke. For the method, the particulate matter of cigarette smoke was extracted with cyclohexane, purified with a silica solid-phase extraction (SPE) cartridge and analyzed by heart-cutting two-dimensional GC equipped with two electron capture detectors. The heart-cutting two-dimensional GC was achieved by a single-column GC oven equipped with a microfluidic pressure balanced device (Deans switch). Two-dimensional GC was compared to single-dimensional GC and found to be clearly better for the separation of seven NBCs from a complex smoke matrix. The limits of detection ranged from 1.28 to 9.83 ng/mL, spiked recoveries were between 88.3 and 106.8% and relative standard deviation ranged from 2.79 to 12.78%. The NBCs yields of six kinds of Chinese and international cigarettes brands, which were all smoked according to two smoking protocols (International Organization for Standardization and Health Canada Intense smoking regimens), were determined and compared.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".