A comparison of direct thermal desorption with solvent extraction for gas chromatography-mass spectrometry analysis of semivolatile organic compounds in diesel particulate matter
Bibliographic record
Abstract
Direct thermal desorption-gas chromatography-mass spectrometry (DTD-GC-MS) is a technique that is finding application in the characterisation of the semivolatile organic carbon fraction of ambient and combustion source particulate matter (PM) collected on filters. In this study, three DTD-GC-MS methods were assessed and compared to a conventional solvent extraction method for analysis of a mixture of target analytes in solution and of diesel PM collected on quartz filters. The target analytes included n-alkanes, hopanes, steranes and polycyclic aromatic hydrocarbons. This study showed that while the three DTD-GC-MS methods were generally comparable to the solvent extraction method, (1) the choice of calibration strategy and calibration materials has a significant impact on the measured accuracy of a method; (2) very large variations were seen in all methods for the more volatile compounds such as C10 to C13 n-alkanes and naphthalene; (3) accuracy, defined as difference from the known concentration of a liquid sample, ranged from 5% to 32%; (4) precision, defined as the relative standard deviation, ranged from 4% to 16%. The average difference of DTD-GC-MS results from the solvent extraction results for the diesel PM filters ranged from 20% to 40%. This difference was driven by the large number of target analytes present at relatively low concentrations (<25 pg/mm2) and their corresponding higher variability. Differences in performance among the compound classes were noted. Minimum detection limits for the DTD-GC-MS methods were on the order of 0.1 to 1 pg/mm2 and were as good as or better than those obtained for the solvent extraction method.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".