Durable and sustainable nano-modified basalt fiber-reinforced composites for elevated temperature applications
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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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".