Impact of Thermal Decomposition on Thermal Desorption Instruments: Advantage of Thermogram Analysis for Quantifying Volatility Distributions of Organic Species
Bibliographic record
Abstract
We present results from a high-resolution chemical ionization time-of-flight mass spectrometer (HRToF-CIMS), operated with two different thermal desorption inlets, designed to characterize the gas and aerosol composition. Data from two field campaigns at forested sites are shown. Particle volatility distributions are estimated using three different methods: thermograms, elemental formulas, and measured partitioning. Thermogram-based results are consistent with those from an aerosol mass spectrometer (AMS) with a thermal denuder, implying that thermal desorption is reproducible across very different experimental setups. Estimated volatilities from the detected elemental formulas are much higher than from thermograms since many of the detected species are thermal decomposition products rather than actual SOA molecules. We show that up to 65% of citric acid decomposes substantially in the FIGAERO–CIMS, with ∼20% of its mass detected as gas-phase CO 2, CO, and H 2 O. Once thermal decomposition effects on the detected formulas are taken into account, formula-derived volatilities can be reconciled with the thermogram method. The volatility distribution estimated from partitioning measurements is very narrow, likely due to signal-to-noise limits in the measurements. Our findings indicate that many commonly used thermal desorption methods might lead to inaccurate results when estimating volatilities from observed ion formulas found in SOA. The volatility distributions from the thermogram method are likely the closest to the real distributions.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
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".