Mine wastes based geopolymers: A critical review
Bibliographic record
Abstract
The quantities of waste rocks and tailings generated by the mining industry have been increased in the last decades. The accumulation and the surface storage of these mine wastes represents a real challenge in terms of environmental and health issues. Thus, the recycling and valorization of these mine wastes is one of the most effective ways of reducing their volume and mitigating their negative environmental impact. Among the recent and sustainable management strategies, geopolymerization technology offers many advantages, (i) the stabilization of polluted/inert mine wastes in the geopolymer matrix, (ii) valorization of a large volume of wastes in the construction sector and consequently minimization of environmental impacts, and finally (iii) the significant reduction of greenhouse gas emissions generated by the use of ordinary Portland cement (OPC) in the construction sector. This paper is intended to present an updated and critical review of the existing literature about mine wastes based geopolymers, by focusing mainly on the mechanical performances of each type of waste. The fundamentals of geopolymers synthesis and the effect of metakaolin substitution by mine wastes are investigated. The influence of the chemical composition of mine wastes was linked to the compressive strength. Results of recent studies showed that geopolymeric materials elaborated using mine wastes presented similar or better mechanical, physical, and durability properties compared to OPC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".