Thermal and Moisture Buffering Properties of Novel Hemp-Lime Composites Integrated with Microencapsulated Phase Change Materials
Bibliographic record
Abstract
Higher requirements for buildings' energy performance and indoor environmental quality have prompted new technologies such as latent heat storage with phase change materials capable of storing and releasing significant quantities of heat per unit mass near room temperature. Conventional building materials (e.g., gypsum, concrete) used for mixing with microencapsulated phase change materials (MPCM) often contain high embodied energy. Hempcrete is a sustainable biocomposite material that can significantly reduce a building's embodied energy and energy consumption while enhancing indoor environmental quality. This research aims to develop a new low-carbon latent heat storage material composed of hempcrete and MPCMs with improved hygrothermal properties for sustainable buildings. Eight hempcrete composites were created using different design mixes using hydrated lime, metakaolin, hydraulic lime, and recycled crushed brick. Furthermore, eight hempcrete-MPCM composites were made using two MPCM types, four MPCM melting temperatures, and two MPCM concentrations. The characterization of composites' thermal and moisture properties includes measuring thermal conductivity, volumetric heat capacity, and moisture buffer capacities. The findings suggest that the developed hempcrete-MPCM samples have a higher heat storage capacity than the hempcrete due to their higher volumetric heat capacity. Moreover, hempcrete-MPCM samples have lower thermal conductivity than hempcrete samples in the same density range and testing orientation. The average moisture buffering value for the hempcrete and HPCM samples of 2.78 and 2.76 (gm/m 2 RH%), respectively, indicates excellent moisture buffering performance. The results suggest that the optimal integration of MPCMs requires a thorough consideration of the operating temperature and percentage of MPCMs within the hempcrete concerning the specific application and performance objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".