Facile synthesis and optimization of reactive bunsenite for the production of thermally stable geopolymeric composite
Bibliographic record
Abstract
In this investigation, the impact of texture characteristics and the degree of crystallinity of the bunsenite phase (NiO) on the mechanical performance and fire resistivity of the geopolymeric composites is addressed for the first time. NiO with different characteristics is prepared by applying two calcination-temperatures (400 and 600°C) to obtain NiO-400 and NiO-600, respectively. Seven mixes were prepared; the control specimen was fabricated by mixing 50% slag+50%fly-ash+NaOH-solution and the other six mixes contained the same component modified with 0.5, 1, 2% NiO-400 or NiO-600. The compressive-strength at 3 and 28-days and fire resistivity up to 1000°C were studied. Also, the phase composition and micro-structure were examined using XRD and TGA/DTG as well as SEM, respectively. The results showed that increasing calcination-temperature leads to decreasing surface area and increasing the degree of crystallinity of NiO. The composites modified with NiO-400 significantly enhanced the strength and fire resistivity of the control specimen, while NiO-600 demonstrated a lower effect. This refers to increasing the calcination-temperature is accompanied by decreasing the degree of reactivity, which reflects on the properties of geopolymeric composites. Regardless of the NiO-type, 1 w.% exhibited the highest strength. Adding NiO induced the formation of NiAl2O4, CaNi(SiO3)2 and NaAlSi2O6.H2O, which is compatible with enhancing mechanical and fire resistivity results.
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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".