Preparation of Geopolymers with Nanosilica and Water-in-Air Pickering Emulsion: Mechanisms Underlying Its Rheology, Polymerization, and Strength
Bibliographic record
Abstract
Geopolymers are alkaline-activated aluminosilicate binders recognized as a promising alternative to traditional Portland cement due to their significantly lower greenhouse emissions, energy consumption, and carbon footprint. However, the challenge is meeting or exceeding the strength of Portland cement concrete while being prepared within a desired setting time and possessing workable rheology. A "water-in-air" Pickering emulsion, also called dry water, was prepared by stabilizing water droplets with hydrophobic nano silica and using them to control the geopolymer's strength, setting time, and workability. The mechanisms that underlie the effects of dry water on the rheology, setting, and strength were studied in detail through a combination of rheological, thermal, morphological, chemical, and microstructural assessments. A reduction in the viscosity and yield shear stress manifests in a higher flow diameter, principally due to the particle size coarsening in the precursor and the flowability of hydrophobic nano silica. There was a rapid rise in temperature during the setting process as the dry water temporarily increased the local alkalinity in the mixture, which boosted the dissolution of the precursor and, hence, the reaction. Outcomes from X-ray diffraction, thermogravimetric analysis, and Fourier-transform infrared confirm the highest degree of polycondensation for the principal N-A-S-H framework in mixtures containing dry water. These eventually correspond to a denser microstructure under scanning electron microscopy and, in turn, a superior mechanical strength. Depending on the unique combination of characteristics, including size coarsening, temporary water encapsulation, microfilling effect, and supplementary silica source, dry water resolves the "trade-off" between geopolymer's fresh and hardened properties when introducing nanoparticles.
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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".