Comprehensive review on alkaline dissolution of aluminosilicates and its role in the mechanisms and properties of geopolymers and alkali-activated materials
Bibliographic record
Abstract
Using industrial by-products as a source of aluminosilicate precursors (APs) for alkali-activated materials (AAMs) and geopolymers synthesis offers a valuable solution for managing increasing waste streams and reducing landfill reliance. Nevertheless, to successfully convert diverse APs into materials with consistent performance at an industrial scale, unified dissolution and testing methods for evaluating geopolymerization reactivity are essential. The dissolution kinetics of aluminosilicate minerals significantly influence the nanostructural evolution of geopolymeric materials, and consequently their final properties, by determining the composition and characteristics of the resulting hydrated phases. However, the primary factors governing alkali–aluminosilicate reactions are not yet fully understood. To this end, this review meticulously examines the theoretical and experimental mechanisms, as well as advanced simulation models used to elucidate geopolymerization. Additionally, it addresses the primary factors influencing the dissolution of aluminosilicate precursors in alkaline environments, including alkalinity, activator type and concentration, precursor fineness, impurities, leaching temperature, and contact time. The findings revealed that the dissolution of aluminosilicates critically influences the properties of geopolymeric materials. Consequently, understanding the initial dissolution behavior of Al, Si, and other elements in alkaline solution under varying conditions is essential for selecting appropriate precursors and tailoring the formulation of geopolymers and alkali‑activated binders for specific applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".