Impact of bio-based phase change materials on the thermal inertia of panels made from medium-density fiberboard (MDF) residues
Bibliographic record
Abstract
Phase change materials (PCMs) are characterized by their ability to absorb, store, and release thermal energy. These characteristics make them highly attractive for enhancing the thermal mass of various construction materials, including wood and, specifically, wood-based panels. This study evaluates the thermal properties of fiberboard panels manufactured from medium density fiberboard residues and PCMs. Two types of adhesives, two types of PCMs, and three percentages of PCM content were considered for a total of 12 different combinations. The panels were tested to determine their specific heat, total heat storage capacity, thermal conductivity, and reaction to fire. Additionally, the panels’ physical and mechanical properties were analyzed. The results indicate that panels with PCM additions exhibited specific heat values reaching 7300 J/kg K, representing an increase of over 400 % relative to the 1453.8 J/kg K measured in the control panels. The total heat storage test demonstrated that adding PCMs increased the heat storage capacity of the panels by up to 76 %. The highest latent heat of fusion obtained was 136 J/g with a microencapsulated phase change material (MPCM) content of 18 %. Incorporating MPCM increased the panels’ thermal conductivity by 9 %. As the panels’ PCM content increased, their mechanical properties decreased. However, it was still possible to meet the minimum values required by standards for use in various applications. This study demonstrates the potential of a new fiberboard made from medium density fiberboard residues and phase change materials (PCMs) for thermal energy storage, enhancing thermal inertia.
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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".