Modelling permeation passive sampling: intra-particle resistance to mass transfer and comprehensive sensitivity analysis
Bibliographic record
Abstract
A mathematical model developed previously to describe the sampling process in permeation passive samplers with non-porous adsorbents and evaluated using the Waterloo Membrane Sampler (WMS) is here extended to include adsorbents with porous particles. This work was motivated by the need to expand the model applicability to include the various types of adsorbents used in the WMS, and to develop a deep understanding of the model sensitivity towards required parameters. The effects of intraparticle porosity on the effective diffusivity of the analyte in a bed of porous particles and on the mass transfer coefficient for analyte transport from the interparticle void phase to the porous solid phase are both evaluated. Experimental validation of the applicability of the model on adsorbents with microporous particles was carried out using the WMS containing Anasorb 747, a carbon-based adsorbent with highly porous particles. Good agreement between the experimental and model results was found. A comprehensive sensitivity analysis was also conducted to identify the parameters with the greatest influence on the results of the calculated uptake rate. This analysis included two types of adsorbents with different sorption strengths. The results showed that the uptake rate sensitivity is limited to parameters related to mass transfer in the membrane for strong adsorbents. On the other hand, sensitivity to parameters related to mass transfer in the sorbent bed becomes more significant as the strength of the adsorbent decreases; however, this effect can be reduced by increasing the membrane thickness. Influential parameters in the sorbent bed are also affected by the temperature. Nevertheless, the contribution of this change to the total effect of temperature change on the uptake rate is expected to be negligible within the small range of temperature variations usually encountered during a single environmental sampling event, especially in soil-gas sampling which is the most widely used application of the WMS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 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".