Laser desorption/laser ionization/time-of-flight mass spectrometer for the detection of polycyclic aromatic hydrocarbons desorbed from soot
Bibliographic record
Abstract
Recent advances in the field of laser desorption/laser ionization mass spectrometry (LD/LI/MS) have renewed interest in these separation methods for fast analysis of chemical species adsorbed on soot particles. These techniques provide mass-separation of the desorbed phase with high selectivity and sensitivity and require very small amounts of soot to be collected. In particular the techniques provide a means to measure adsorbed polycyclic aromatic hydrocarbons (PAHs). PAHs are important precursors of carbonaceous soot particles, thus influence the quantity and morphology of particulate emission from various combustion processes. Furthermore, PAHs absorbed on the surface of the soot particles contribute to the carcinogenicity of the particles. Therefore, there is scientific interest in characterizing and quantifying these adsorbed PAHs in order to provide key information about the mechanism of the soot formation for various fuels and combustors and to understand their health impact. This has motivated various studies; however, an extensive characterization of the PAHs absorbed onto soot particles remains a challenge.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".