Thermo-Mechanical Characterization of Gypsum–Nano Silica Modified Fired Clay Bricks at Elevated Temperatures
Bibliographic record
Abstract
The current research aims to develop fired clay bricks with gypsum and nanosilica from rice straw as additives to improve the thermomechanical properties of conventional clay bricks.Laboratory-based clay brick samples were prepared by adding commercial gypsum (G) at 0% and 5%, nanosilica (S) prepared from rice straw waste materials at 0%, 5%, and 10% proportions, and firing at temperatures of 900, 1000, and 1100.The mineral and phase transformations of prepared nanosilica and brick samples were investigated using X-ray diffraction (XRD), X-ray fluorescence (XRF), thermogravimetric analysis-derivative differential thermal analysis (TGA-drDTA), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) techniques.Properties were compared to those of conventional bricks that did not contain additives.Synthesized nanosilica was found to be 127 nm and suitable as an additive.The results of the fireclay bricks with gypsum and nanosilica additives indicate that increasing the silicate polymerization and glaze phase formation from 25% to 45% reduces the microcracking density from 4.4 to 2.8 cracks/mm at 1100, with a composition of G5%-S10%.Reduce compressive strength (from 35 to 12.8 MPa), water absorption capacity (18.6 to 13.6%), and thermal conductivity (0.28-0.76 W/m.K) to meet ASTM requirements.Finally, it was concluded that the addition of gypsum and nanosilica additive improves the thermochemical stability and mechanical properties of fired clay bricks, making them suitable for energy-efficient and fire-safe construction materials.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".