Measurement of thermal conductivity of building insulation foams by modulated differential scanning calorimetry — Critical review & observations
Bibliographic record
Abstract
Abstract The rise in energy prices, the need to conserve energy and the pressure to protect the environment promote the development of innovative eco-friendly thermal insulating foams for building applications. In this quest, a rapid and accurate method to measure the thermal conductivity of new foams is required during the research and product development stage. Temperature-modulated differential scanning calorimetry (MDSC) provides thermal conductivity values from heat capacity measurements on cylindrical samples less than about 20 mg in weight. This method is the basis of the ASTM E1952 standard method “Thermal Conductivity and Thermal Diffusivity by Modulated Differential Scanning Calorimetry”. In this work, the MDSC and the ASTM E1952 test methods are applied to thermal insulating foams used in construction applications. Measurements on polystyrene, polyurethane, and polyisocyanurate insulations demonstrate that MDSC possesses excellent repeatability, but its application through ASTM E 1952 provides inaccurate thermal conductivity values. Two sources of errors were identified, 1) the use of nitrogen as a purge gas, and 2) the use of an equation that inaccurately relates the measured heat capacity to thermal conductivity. Methods around these difficulties exist, but they remain untested with insulating foams.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".