Enhanced Thermo-Physical Properties of Gypsum Composites Using Olive Pomace Waste Reinforcement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Today, the construction sector consumes 30-40% of the world's total energy and contributes one-third of total greenhouse gas emissions.Consequently, the development of new eco-friendly building materials with improved properties is becoming increasingly important.Olive pomace waste is released into the environment, which has a negative impact on it.Recycling this olive pomace waste as an alternative raw material in the construction industry can protect the environment and at the same time reduce the additional costs of managing and disposing of this waste for local authorities.It is an environmentally friendly and sustainable solution to waste recycling.This study investigated the effect of adding olive pomace (OP) to building materials.Four proportions of this additive (4%, 8%, 12% and 16%) were used.Physical, thermal properties (conductivity and diffusivity) as well as mechanical properties (compressive and flexural strength) of the composites were carried out.The traditional gypsum-based composites had a thermal conductivity of 0.478 W.m -1 .K -1 , while the composites of gypsum with additive show an interesting thermal conductivity of 0.390 W.m -1 .K -1 for a percentage of 16% (OP16) with a reduction rate of 22.56%, and mechanical properties lower than those of the reference gypsum-based composite but in accordance with the standard EN 133279-1, with compressive strength of almost 4.10MPa for a percentage of 16% (OP16), and flexural strength equal to 2MPa.This is due to the increase in porosity as indicated by the microstructure of the composites.We also tested water absorption by capillary action for each specimen, and found that this coefficient increased with increasing percentage of waste.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it