Optimizing Precursors and Reagents for the Development of Alkali-Activated Binders in Ambient Curing Conditions
Bibliographic record
Abstract
Alkali-activated binders (AABs) are developed through the activation of aluminosilicate-rich materials using alkaline reagents. The characteristics of AABs developed using a novel dry-mixing technique incorporating powder-based reagents/activators are extensively explored. A total of forty-four binder mixes are assessed in terms of their fresh and hardened state properties. The influence of mono/binary/ternary combinations of supplementary cementitious materials (SCMs)/precursors and different types/combinations/dosages of powder-based reagents on the strength and workability properties of different binder mixes are assessed to determine the optimum composition of precursors and the reagents. The binary (55% fly ash class C and 45% ground granulated blast furnace slag) and ternary (25% fly ash class C, 35% fly ash class F and 40% ground granulated blast furnace slag) binders with reagent-2 (calcium hydroxide and sodium sulfate = 2.5:1) exhibited desired workability and 28-day compressive strengths of 56 and 52 MPa, respectively. Microstructural analyses (in terms of SEM/EDS and XRD) revealed the formation of additional calcium aluminosilicate hydrate with sodium or mixed Ca/Na compounds in binary and ternary binders incorporating reagent-2, resulting in higher compressive strength. This research confirms the potential of producing powder-based cement-free green AABs incorporating binary/ternary combinations of SCMs having the desired fresh and hardened state properties under ambient curing conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".