Thermal Desorption Comprehensive Two-Dimensional Gas Chromatography: An Improved Instrument for In-Situ Speciated Measurements of Organic Aerosols
Bibliographic record
Abstract
The organic fraction is a major constituent of fine atmospheric particulate matter, though its chemical composition is complex and not well understood. This complexity presents an extreme analytical challenge and is well suited to analysis by comprehensive two-dimensional gas chromatography (GC × GC). This has recently been coupled to a thermal desorption aerosol gas (TAG) chromatograph instrument to provide improved in-situ, hourly measurement of speciated organic compounds in atmospheric aerosols (2D-TAG). The original 2D-TAG instrument utilized a flame ionization detector, dual-stage modulator, and a second-dimension column that proved to be thermally unstable under optimal analysis conditions, which limited compound identification, instrument robustness, and time resolution. In this paper, we address these shortcomings by demonstrating the successful integration of a time-of-flight mass spectrometer (TOFMS), development of a simplified hybrid thermo-pneumatic modulator, and incorporation of a more thermally stable secondary column. These improvements resulted in an instrument capable of providing detailed speciated information of organic tracer compounds in atmospheric aerosols in near real time.
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) | 0.001 | 0.000 |
| 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".