High‐performance thermal insulator based on polymer foam and silica xerogel
Bibliographic record
Abstract
Abstract In this work, methyltrimethoxysilane (MTMS) was used as a precursor, n‐hexadecyltrimethylammonium bromide (CTAB) as a surfactant, and EtOH/H2O as a co‐solvent, while 10 mM HCl and urea were used as acid and base catalysts to prepare silica xerogels and xerogel inside the cells of a polyethylene (PE) foam via a sol–gel process combined with ambient pressure drying. The xerogels and PE‐silica xerogel foams were characterized by scanning electron microscope (SEM), Brunauer–Emmett–Teller method (BET), and thermal conductivity. The results indicate that the xerogels have a three‐dimensional nanoporous structure, and the specific surface areas of the SiO2 xerogel and PE‐xerogel foam are 536 and 377 m2/g, respectively. The PE‐xerogel foam exhibited very low thermal conductivity (22–24 mW/m.K) compared with conventional insulation materials on the market (30–50 mW/m.K). The thermal insulation properties of PE‐silica xerogels were increased by 45% and 49% compared with neat PE foam. Additionally, the compressive modulus and density of the PE‐xerogel foam was increased by the presence of the silica xerogel. These PE‐xerogel foams are believed to be excellent candidates for thermal insulation applications in terms of cost/performance.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".