Adsorption isotherm of building materials at different temperatures: A review and measurement
Bibliographic record
Abstract
The temperature dependency of the adsorption isotherm renders its significance as one of the primary input parameters in models dealing with moisture transport. The present study involved the determination of the adsorption isotherm of sixteen building materials from various categories, including cladding, building paper, wood, sheathing boards, and insulations. The measurements were conducted under nine climatic conditions, a combination of three temperatures (3 °C (37.4 °F), 21 °C (69.8 °F), 45 °C (113 °F)) and three relative humidity levels (50%, 70%, 90%). The study indicated that relative humidity increased the building materials’ equilibrium moisture content (EMC). In opposition, an increase in temperature decreased the EMC of the tested materials. It was discovered that cellulose fiber and wood-based materials had a greater capacity to store moisture than others. Conversely, the minimum moisture content was observed in brick, Densglass gold gypsum, and EPS products. Furthermore, we used the ABC model as an adaptable mathematical equation for a novel nonlinear surface fitting of adsorption isotherms of tested materials to make optimal use of the experimental data and effectively analyze the simultaneous impact of all temperatures and relative humidities. Tested against the measured experimental data, we obtained the best R-squared values from the second-order polynomial Equation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".