Impact of high-temperature exposure on the thermal and physio-mechanical performance of graphene-reinforced geopolymer composites
Bibliographic record
Abstract
Geopolymer composites are emerging as sustainable materials with significant potential in the construction industry. While geopolymers are known for their inherent thermal resistance properties, their mechanical stability at elevated temperatures remains a key challenge due to microstructural degradation and moisture-induced damage. This study investigates the reinforcing effect of graphene oxide (GO) on fly ash-based geopolymer composites under ambient and high-temperature (900°C) conditions. Low-cost, industrial-grade GO with iron impurities was combined into the geopolymer matrix at varying dosages (0.1–0.4 wt% of binder). The mechanical properties and microstructural characteristics of graphene-reinforced geopolymer composites (GRGC) were then compared with plain geopolymer composites (without GO)- control. Results indicated that the addition of 0.2 wt. (%) GO in GRGC composites enhanced the compressive strength by 16.59 (%) and 18.48 (%) at 7 and 28 days of curing, respectively, compared to the control specimens. The strength enhancement in GRGC was more significant at a high-temperature exposure, as reflected by a 104 (%) increase in compressive strength compared with the control specimens. The physio-mechanical behaviour was analysed through microstructural investigations, such as Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Thermogravimetric Analysis (TGA). Microstructural analyses revealed that GO did not contribute to any new phase formation, acting as a nanofiller, refining the pore structure and enhancing matrix densification without altering the primary amorphous gel phase, while also improving thermal stability and reducing mass loss at elevated temperatures. The results suggest that GO enhanced thermal stability by reducing dehydration rates and transforming the amorphous gel matrix into a uniform crystalline structure after high-temperature exposure. These findings demonstrate the potential of GO-reinforced geopolymer composites as thermally stable and mechanically resilient materials for high-temperature structural applications. • GO has beneficial effects on the mechanical strength of geopolymer composites. • High-temperature performance of the geopolymer matrix was improved by the GO fillers. • Ferro-spheres stemming from the iron particles on the GO surface at high temperatures contributed to strength retention.
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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".