Development of Multifunctional Slag and Bauxite Residue-Based Geopolymers with Heavyweight Aggregate Enhancement
Bibliographic record
Abstract
The growing demand for sustainable and multifunctional construction materials, particularly those capable of addressing durability and energy challenges, has motivated the development of conductive and photothermally active geopolymers. This study investigated the use of an Fe-rich spinel aggregate (FSA) as a high-density filler in geopolymers composed of ground granulated blast furnace slag and bauxite residue, with a fixed addition of 1 wt% graphite (binder-based) to enhance electrical conductivity. The effects of different FSA replacement percentages (0–100%) on compressive strength, electrical conductivity, photothermal efficiency, and chemical resistance were evaluated. An increase in the FSA content translated to an increase in the final compressive strength, with 100% FSA replacement achieving the highest value of 45.5 ± 2.5 MPa at 28 days. As the FSA content increased, the electrical resistivity decreased to as low as 42 Ω·m at 100% replacement. Under simulated solar flux conditions (1 kW/m2), photothermal analysis revealed that the 100% FSA mixtures exhibited the highest surface temperature increase of 9.8 °C after 300 s, indicating their superior thermal responsiveness. Furthermore, acid immersion in 10% HCl for 28 days showed mass gain in all geopolymers, with the highest gain observed at 50% FSA (+11.51%). Similarly, the strength increased after acid exposure up to a 75% FSA content. These findings highlight the multifunctional potential of FSA-enhanced geopolymers for high-mechanical-performance, electrically conductive, photothermally active, and chemically durable materials as multifunctional 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 | 0.000 | 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) | 0.001 | 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".