A Two-Dimensional Laminar Flow Model for Thermodenuders Applied to Vapor Pressure Measurements
Bibliographic record
Abstract
Thermodenuders (TD) have been used to quantify the volatility of aerosol species, frequently with the aid of modeling. Here we present a two-dimensional model of flow, heat transfer, and aerosol dynamics that is fast, yet includes spatial resolution of the complete aerosol size distribution. We first demonstrate the utility of the model by interpreting nonequilibrium TD measurement data previously reported in the literature. It is shown that the thermogram (temperature vs. mass fraction remaining) curve is remarkably insensitive to radial variations in temperature and vapor concentration under typical conditions. Therefore, the discrepancies among vapor pressure estimates determined in TD studies are unlikely to be due to oversimplified flow models, but are instead likely due to faulty assumptions concerning evaporation kinetics. We then show that the best-fit range for the parameters that dictate equilibrium partitioning (saturation vapor pressure at a reference temperature and enthalpy of vaporization) can also be obtained by fitting nonequilibrium TD data using a three-parameter model that accounts for mass transfer limitations (by also fitting the evaporation coefficient). The degree of agreement between experiments and model simulations are examined for two dicarboxylic acids using the model developed in this study. The best-fit parameters were within the uncertainty range previously found using an “equilibrated” TD approach for butanedioic acid, whereas significantly better model-experiment agreement was obtained for a much lower value of enthalpy of vaporization than previously reported for hexanedioic acid. Copyright 2013 American Association for Aerosol Research
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".