Impregnation of Wood with Microencapsulated Bio-Based Phase Change Materials for High Thermal Mass Engineered Wood Flooring
Bibliographic record
Abstract
Wood is a porous material that can be impregnated and have enhanced properties. Two species of hardwood, red oak (Quercus rubra L.) and sugar maple (Acer saccharum Marsh.), were impregnated in a reactor with a microencapsulated phase change material. The objective was to enhance the thermal mass of wood boards used as surface layers for engineered wood flooring manufacturing. Preliminary experiments were conducted on small samples in order to define suitable impregnation parameters, based on the Bethell cycle. Thin wood boards were impregnated with a microencapsulated phase change material dispersed into distilled water. Several cycles of pressure were applied. Heating storage of the impregnated wood boards was determined using a dynamic heat flow meter apparatus method. A latent heat storage of 7.6 J/g over 3 °C was measured for impregnated red oak samples. This corresponds to a heat storage enhancement of 77.0%. Sugar maple was found to be harder to impregnate and thus his thermal enhancement was lower. Impregnated samples were observed by reflective optical microscopy. Microcapsules were found mainly in the large vessels of red oak, forming aggregates. Pull-off tests were conducted on varnished samples to assess the influence of an impregnation on varnish adhesion and no significant influence was revealed. Engineered wood flooring manufactured with impregnated boards such as characterized in this study could store solar energy and thus improve buildings energy efficiency. Although wood is a material with a low-conductivity, the thermal exchange between the PCM and the building air could be good enough as the microcapsules are positioned in the surface layer. Furthermore, flooring is an area with frequent sunrays exposure. Such high thermal mass EWF could lead to energy savings and to an enhancement of occupant’s thermal comfort. This study aimed to characterize the potential of impregnation with MPCM of two wood species in order to make high thermal mass EWF.
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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) | 0.000 | 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".