New Generation Graphenes in Cement-Based Materials: Production, Property Enhancement, and Life Cycle Analysis
Bibliographic record
Abstract
Several studies have explored the use of graphene to improve the properties of cement-based materials. However, most commercially available graphenes are expensive, not amenable to mass production, and have high embodied energy and emissions, making their use in concrete less attractive, despite the beneficial mechanical property attributes. This paper discusses the use of two novel graphene types, fractal graphene (FG) and reactive graphene (RG), obtained through a cost-effective and scalable detonation synthesis, in cement-based materials. FG and RG are sheets containing 6–10 layers, with lateral dimensions of 20–50 nm and a z -axis thickness of <5 nm. RG is functionalized with carboxylic groups. An ultrasonication process is employed to ensure dispersion of graphene particles in aqueous solutions. Both FG and RG, when added at very small dosages (≤0.04% by mass of cement), enhance the compressive strength of cement mortars by >70% at early ages and up to 20% at later ages. The beneficial effect of functionalization results in better performance for RG-modified mixtures, even at dosages as low as 0.02%. Concomitant enhancements in heat of hydration, hydrate formation, and rheological response are observed. A significant reduction in porosity and critical pore size (by 50% or more) promises significantly improved concrete durability, and thus reduced life-cycle costs. A comparative life cycle analysis (LCA) is used to show that FG- and RG-modified mortars have normalized (by the 28 d strength) energy demand and global warming potential (GWP) that is up to 15% lower than those of conventional mortars. Overall, this study shows that FG and RG, manufactured through a scalable, cost-, energy-, and CO 2 -efficient detonation synthesis, can beneficially impact the engineering and environmental performance of concretes.
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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 | 0.001 | 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 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".