Use of phase change materials in wood and wood-based composites for thermal energy storage: A Review
Bibliographic record
Abstract
Using phase change materials (PCMs) is an efficient solution for reducing energy consumption in buildings. These materials have a large capacity for storing thermal energy, making them an appealing option for energy management purposes. Phase change materials have been successfully incorporated into various construction materials such as concrete, brick, or plaster. The primary objective of this review is to examine previous studies conducted on the application of PCMs in wood. The initial section presents an overview of the direct impregnation techniques utilized for wooden materials. This is followed by a discussion on the implementation of macroencapsulated PCMs in wooden structures that are typically present in residential buildings. In addition, the use of shape-stabilized PCM/wood composites, preventing potential leaks during the phase change transition, is explored. Finally, patents related to the use of PCMs in wood are described. Future challenges include the incorporation of PCMs into wood composites to improve their thermal properties. This literature review shows that there is a gap in knowledge regarding the utilization of phase change materials in wood-based panels such as oriented strandboards, fiberboards, and particleboards. This provides an opportunity for future research to improve the performance of the products manufactured by the wood-based panels industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".