Physically Based Modelling of the Material and Gaseous Contaminant Interactions in Buildings: Models, Experimental Data and Future Developments
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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