Measurement of diffusion coefficients of VOCs for building materials: review and development of a calculation procedure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The measurement and prediction of building material emission rates have been the subject of intensive research over the past decade, resulting in the development of advanced sensory and chemical analysis measurement techniques as well as the development of analytical and numerical models. One of the important input parameters for these models is the diffusion coefficient. Several experimental techniques have been applied to estimate the diffusion coefficient. An extensive literature review of the techniques used to measure this coefficient was carried out, for building materials exposed to volatile organic compounds (VOC). This paper reviews these techniques; it also analyses the results and discusses the possible causes of difference in the reported data. It was noted that the discrepancy between the different results was mainly because of the assumptions made in and the techniques used to analyze the data. For a given technique, the results show that there can be a difference of up to 700% in the reported data. Moreover, the paper proposes what is referred to as the mass exchanger method, to calculate diffusion coefficients considering both diffusion and convection. The results obtained by this mass exchanger method were compared with those obtained by the existing method considering only diffusion. It was demonstrated that, for porous materials, the convection resistance could not be ignored when compared with the diffusion resistance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it