Physically Based Modelling of the Material and Gaseous Contaminant Interactions in Buildings: Models, Experimental Data and Future Developments
Bibliographic record
Abstract
Although potentially having a significant influence on indoor air quality (IAQ), interactions between building materials and gaseous contaminants have often been neglected or crudely modelled in IAQ simulation tools. During the past 20 years, empirical source and sink models have progressively given way to physically based models; but confusion still remains on their applicability, as well as on the adequate experimental data to input for the model parameters. Thus, demonstration is first made that models relating macroscopically the room air phase and material concentrations through adsorption and desorption constants are not scientifically sound. Instead, elemental models combining diffusion equations and local sorption equilibria should be used. The compilation of sorption and diffusion data presented in the second part of this chapter underlines the fact that such data cannot be considered independently from the mass transport equations used to fit the measurements. As a result, a thorough analysis of diffusion processes in polymers and porous media is presented in order to define and relate the diffusion coefficients. Finally, the last part of the chapter discusses the way in which existing models could be extended to account for the contributions of temperature, multi-component mixtures, humidity and chemical transformations within materials. Still based on fundamental considerations, the proposed methodology consists of implementing new functionalities to describe the temperature dependence of the model parameters, elemental models representing the interactions between gaseous contaminants and water, as well as kinetic models coming from the fields of atmospheric and surface sciences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".