Ultra-low dosages of novel graphene types enhance the rheological properties and buildability of 3D printed binders
Bibliographic record
Abstract
The use of graphene as a high-performance concrete additive is attractive; but, its cost and concerns about production scalability and dispersion efficiency in concrete are impediments to widespread use. This study explores the impact of ultra-low dosages ( ≤ 0.02 % by mass of binder) of two novel graphene types—fractal graphene (FG) and reactive graphene (RG)—produced through a cost-effective, environmentally friendly, and scalable process, on the rheological properties of 3D-printable concrete. Both FG and RG significantly enhance the dynamic and static yield stresses and viscoelastic properties of the binder, with RG-modified mixtures exhibiting slightly more pronounced enhancements due to the presence of functional groups. Temporal evolution of static yield stress (τ s ) and storage modulus (G’) reveal aspects relating to structural build-up facilitated by the graphene particulates (structuration parameter from τ s -t, and rate of structural build-up, and residual structural factor from G’-t relations), that are important in extrusion and shape stability. Experimental buildability tests on hollow cylinders reveal that the selected ultra-low graphene dosages more than double the achievable build heights at 30, 60, and 90 min of mixing. This enhancement is further corroborated by an analytical model for plastic collapse, which incorporates plastic yield stress derived from green compression testing. Finally, this paper introduces an approach wherein the storage modulus and its evolution—determined through oscillatory rheology experiments—serve as versatile indicators of key rheological properties essential for material delivery, extrusion, and layer build-up in concrete 3D printing. This methodology holds promise for paving the way toward a standardized rheological test for 3D-printable binders.
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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 | 0.001 |
| 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".