Wrong expectation of superinsulation behavior from largely-expanded nanocellular foams
Bibliographic record
Abstract
This work aims to predict the thermal conductivity of microcellular and nanocellular thermal insulation foams to explore the correlation between the cellular structure and the thermal insulating properties. Closed-cell foam consisting of cell walls and struts was used as the base geometry for modeling. The mathematical correlations to calculate the thickness of cell walls and the diameter of struts for a given cell size, the void fraction and the volume fraction of polymer located in struts were investigated. Then, a mathematical model for the conductive thermal conductivity including the dependency on the void fraction, the strut fraction and the Knudsen effect for gas was introduced. The radiative thermal conductivity was determined by analyzing the attenuation of radiative energy by absorption and scattering based on Mie's theory together with electromagnetic wave interference, as well as interference of propagating waves and tunneling of the radiative energy by evanescent waves in the cells. The thermal conductivity model was validated by experimental data and used to predict the thermal conductivity of polystyrene (PS) and poly(methyl methacrylate) (PMMA) foams at various cell sizes and volume expansion ratios. It was found that the radiative thermal conductivity plays a crucial role in nanocellular foam. The trade-off between the cell size and cell wall thickness when cell walls become thinner and highly transparent to thermal radiation was demonstrated, leading to the optimal volume expansion ratio at which the thermal conductivities were minimized. Perspectives for the manufacture of high-performance thermal insulation foams are also discussed.
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.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".