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Use of phase change materials in wood and wood-based composites for thermal energy storage: A Review

2023· review· en· W4388142045 on OpenAlexaff
Gustavo Rodríguez, Cecilia Bustos, Alain Cloutier

Bibliographic record

VenueBioResources · 2023
Typereview
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials sciencePhase changeComposite materialEngineered woodBrickPhase-change materialThermal energy storageEngineeringEngineering physics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.249
GPT teacher head0.378
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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