Passive application of phase change materials (PCMs) for the Trombe wall: a review
Bibliographic record
Abstract
Passive sustainable buildings are crucial for mitigating the energy crisis and addressing global warming by effectively reducing greenhouse-gas emissions and maximizing solar-energy utilization. This is particularly significant considering the energy consumption of heating, ventilation, and air-conditioning systems. The Trombe wall system is regarded as one of the most effective passive building technologies owing to its potential ability to store and release thermal energy to reduce temperature fluctuations and improve thermal comfort. More importantly, these effects can be enhanced by employing appropriate storage materials, particularly phase change materials (PCMs), owing to their unique thermal properties: high heat-storage capacity within narrow temperature variations. Therefore, this study reviews the passive application of PCMs to Trombe walls developed over the last 40 years. This study summarizes the PCM thermal-energy storage mechanism, classification, and encapsulation and provides a comprehensive list of different PCMs appropriate for Trombe walls in the laboratory or on the market. This work also provides a comprehensive and updated review of PCM Trombe wall configurations, including passive heating and passive hybrid (cooling and heating) configurations. Based on the review results, the main directions for future studies are established and proposed.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".