Hygrothermal and mechanical characterization of novel hemp-lime composites with enhanced consistency
Bibliographic record
Abstract
Hemp-lime composites have captured attention in the construction industry due to their sustainability and excellent hygrothermal performance. However, variability and inconsistent performance have hindered their widespread adoption. This research introduces a novel approach to improve the uniformity and hygrothermal characteristics, aiming for reproducibility and consistency comparable to traditional insulation materials. This method included (1) reducing hemp particle size to coarse (1.33 mm), medium (0.92 mm), and fine (0.72 mm) particles; (2) maximizing the hemp proportion to 70 % by weight; and (3) standardizing dry density using vibration techniques. The findings indicate dry density variability reduction in all samples, with a coefficient of variation ranging from 0.16 % to 2.36 %. The hygrothermal analysis demonstrates enhanced insulation and moisture-buffering properties, along with reduced directional disparity in thermal conductivity (1.2–6.8 %) compared to the control sample, particularly in samples with fine particle sizes. Thermal conductivity was within the range of 0.0535–0.0667 W/m K, considerably lower than previously reported values. Also, a positive correlation is observed between moisture-buffering and hemp ratio, indicating that higher hemp ratios in the composite lead to increased moisture capacity, with moisture buffer values of 2.47, 2.28, and 2.12 g/m² RH corresponding to the binder-to-hemp ratio of 30:70, 40:60, and 50:50 by weight, respectively. • Three uniform hemp shiv sizes and vibration casting reduced property variability. • The dry density variation coefficient ranged from 0.16 % to 2.36 %. • Thermal conductivity was from 0.054 W/m K (140 kg/m³) to 0.067 W/m K (200 kg/m³). • The new production approach reduced the anisotropic behavior to 1.2–6.8 %. • Average moisture buffer values ranged between 2.12 and 2.47 g/m² RH.
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 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".