Characterizing nano-indentation and microstructural properties of mine tailings-based geopolymers
Bibliographic record
Abstract
The mining industry's extraction processes produce vast amounts of waste, including mine tailings (MT), traditionally stored in large ponds, causing significant environmental harm due to a lack of effective recycling methods. This research explores the potential of converting MT into a resource i.e., precursors in geopolymer synthesis. By analyzing the physicochemical and mineralogical properties of MT, the study formulates four geopolymer composites, substituting up to 30 wt% MT for fly ash. The composites' mechanical and microstructural properties show a contribution of MT in the stability of the matrix. Compressive strength of 59 MPa when incorporating 30 wt% MT, along with water absorption, microstructural analysis, and environmental impact assessments, support the hypothesis that the matrix is improved. Nano-indentation techniques further evaluated the nanomechanical properties, such as Young's modulus and fracture toughness. The findings reveal that geopolymers with 30 wt% MT exhibits up to 130 % increased strength. This research highlights the potential for innovative, eco-friendly solutions in waste management and material production, contributing to sustainable mining practices and advancing the field of geopolymer technology.
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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 | 0.000 |
| 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".