Effect of blast furnace slag on the fresh and hardened properties of volcanic tuff-based geopolymer mortars
Bibliographic record
Abstract
Volcanic tuffs are abundant in several regions of the world, and their use has emerged as an economically viable alternative for geopolymer production. This study investigates the effects of incorporating 0 to 30% blast furnace slag (BFS) into volcanic tuff (VT)-based geopolymer mortars cured at room temperature and 80 °C on various properties, including setting time, mechanical strength (compressive and flexural), workability, water absorption and microstructure. Infrared spectroscopy (FTIR) was used to characterize the geopolymer mortars. Sodium hydroxide and sodium silicate are used as alkaline activators. Results revealed that pure VT pastes exhibited exceptionally long setting times, approximately 48 h. However, replacing 10% of VT with Blast Furnace Slag (BFS) reduced this to 435 min. The effect of BFS on compressive strength was time-sensitive. At 7 days, a substantial increase from 2.42 MPa to 9.03 MPa was observed with 30% BFS incorporation. Conversely, under ambient curing, 28-day strength decreased. However, curing at 80 °C for 48 h improved 28-day strength to 15.03 MPa with 30% BFS. Furthermore, results revealed that incorporating 30% BFS significantly enhanced workability, resulting in a 92.08% reduction in flow time. Absorption water and Microstructural analysis confirmed a strong correlation between the degree of geopolymerization and mechanical performance. These findings highlight BFS as a promising additive for optimizing VT-based geopolymer mortars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".