Bibliographic record
Abstract
This research encompassed comprehensive analytical and experimental investigations with the main aim of developing sustainable ambient-cured structural geopolymer binders incorporating maximum amounts of glass and construction and demolition wastes as precursors and hardeners. The objective was also to design a numerical method of mixture for quantifying glass waste (GW) and construction and demolition waste (CDW) ingredients, with different chemical and physical properties, in a balanced geopolymer matrix with optimized rheological, mechanical and microstructural performances. The first challenge was to enhance the reactivity of GW in order to increase its contribution in the geopolymer system, which was reached through a devitrification process to form the tridymite-phase of silica in GW. Furthermore, an alkali hardener was produced from dissolved GW and used to reduce the required commercial sodium silicate while meeting the user-friendliness criteria of alkaline solutions. The enhanced GW was incorporated with metakaolin (MK) as an additional source of alumina and the effect of different amounts of this product was studied at fresh and hardened states. The GW-based hardener was utilized with the enhanced GW and MK precursors, after ensuring optimum factors were attained for dissolved silica using the Taguchi design of experiments and hierarchical analysis of variances. Then, ceramic waste (CW) and brick waste (BW), as the main CDW materials, were combined with the resulting GW and MK combinations in CW-GW+MK and BW-GW+MK precursors prepared with GW-based hardener. The solubility of silica species in alkaline media and the microstructural changes of the enhanced glass waste, as well as the rheological, mechanical and microstructural properties of produced geopolymer binders were investigated using spectrophotometry analysis, X-Ray fluorescence, X-ray diffraction, viscometry analysis, compressive strength measurement, Scanning Electron Microscopy, Energy-dispersive X-ray Spectroscopy and Fourier-Transform Infrared Spectroscopy, respectively. Throughtheresults ofthis research,it was possibleto develop a numerical mix design approach and to generate tridymite as an active silica form of GW. This enabled control of the important chemical and physical factors involved in the geopolymerization process and reach stable, user-friendly and enhanced strength binders with major amounts of GW, BW and CW and up to a 76% reduction in commercial sodium silicate content at ambient curing.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".