Durable and sustainable nano-modified basalt fiber-reinforced composites for elevated temperature applications
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates the performance of nano-modified basalt fiber pellet reinforced cementitious composites (NBFRCC) exposed to elevated temperatures. The composite mixtures have been reinforced with basalt fiber pellet (BFP) coated with a polymeric resin and incorporated cement, slag, nano-silica (Ns) and/or nanofibrillated cellulose (NFC). In total, nine mixtures have been prepared by altering the dosages of BFP (2.5% and 4.5%), Ns (6%) and NFC (0.5%). The mechanical properties like compressive and flexural stress have been explored. The samples are exposed to elevated temperatures of 200°C and 600°C and chloride. Microstructural analysis is also done by SEM and EDX analysis. For most of the mixes, 600˚C exposure for 60 minutes showed up to 15% higher compressive strength than 200˚C, attributed to high percentage of slag (40%). Maximum flexural stress is obtained for 2.5% BFP mixed with both Ns and NFC after 600˚C exposure. The exceptionally high melting point of BFP aids in maintaining higher flexural stress at high temperatures. Nano-modified mixtures show slower declines in flexural stress from room temperature to 600°C, indicating improved mechanical properties and thermal stability. NFC-mixed samples showed the least reduction in flexural strength at 600°C than at 200°C, ranging between 8-10%. Chloride ion penetrability is also reduced from low to very low penetrability class. Performance index (PI) considering mechanical strength, durability, and cost shows that 2.5% BFP with Ns is optimal for sustainable applications. This research will expand the application of NBFRCC, providing a cost-effective and environmentally friendly approach to contemporary construction problems where improved fire resistance and durability are fundamental.
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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) | 0.000 | 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