Role of nanostructured coatings on composite phase change materials for thermal durability enhancement: A review
Bibliographic record
Abstract
With the rapid growth in energy demand and the increasing need for stable thermal storage, the use of phase change materials (PCMs), particularly in the form of phase change composite materials, has received widespread attention. Despite the high advantages of composite phase change materials (CPCMs) in latent heat storage, problems such as leakage, low thermal conductivity, and performance degradation in successive thermal cycles have still limited their use. One of the novel solutions to increase the thermal durability of these materials is the application of nanostructured coatings on their surfaces. By creating physical and chemical barriers, these coatings not only prevent leakage and oxidation but also improve heat transfer and increase structural stability under operational conditions. In this review article, we first introduce the basic principles of PCMs and the structure of CPCMs. Then we investigate the key role of nanostructured coatings in improving thermal stability, reducing supercooling, and increasing thermal cycling. Also, industrial applications of this technology in various fields such as solar energy storage, thermal control of buildings, thermal management of lithium-ion batteries, and electronic systems are reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".