Nanoengineered Geopolymer Composites with Biomass-Based 2D Graphitic Carbon Nanoplatelets
Bibliographic record
Abstract
This research studied an innovative method for improving the properties of fly ash–based geopolymer mortar by using ultrafine two-dimensional (2D) graphitic carbon nanoplatelets (GCNPs) derived from sustainable biomass sources. These low-cost and eco-friendly GCNPs were synthesized through a thermochemical process involving biomass-derived sucrose solution. Both fly ash and GCNPs were processed and activated to optimize their performance in the geopolymerization process. The graphitic carbon reinforced geopolymer mortar (GCGPM) composite was optimized by employing different dosages of GCNPs (0, 0.1, 0.2, 0.3% [by weight of binder]), and the resulting GCGPM was examined through various instrumental analyses. It was observed that the addition of GCNPs results in reduced workability of the geopolymer mortar. Importantly, the maximum compressive strength of the GCGPM was significantly enhanced, up to 34.21%, with a 0.2% GCNPs addition over a 28-day curing period. Furthermore, incorporating 0.1% GCNPs into the composite led to an increased composite density, resulting in a substantial reduction of water absorption, up to 76.49%. These outcomes hold promise for achieving a more compact microstructure through the integration of GCNPs into geopolymer composites. The study suggests that novel synthesized GCNPs can effectively and sustainably enhance the properties of geopolymer composites in a cost-effective manner.
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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".