Production of Ceramic from Sulfidic Mine Tailings and Investigation of Fracture Toughness Under Mode I Loading
Bibliographic record
Abstract
ABSTRACT Mine tailings (MTs) are the waste of mining operations after the valuable metals have been extracted. Given the significant quantities produced globally, the deposition of MTs causes problems including occupation of large areas of land and environmental contamination by toxic leachates. The production of sustainable construction materials from MTs is an environmentally friendly solution to reduce the consumption of natural resources and decrease their adverse impacts on the environment. This study investigates the feasibility of using sulfidic MTs in the production of ceramics. A targeted physical, mineralogical, and chemical characterization of the tailings resulted in the development of a production process that involved grinding the raw tailings, mixing with binders, pressing circular specimens, and then sintering. The properties of sintered ceramics, including crystalline phase, density, water absorption, and compressive strength, were investigated. The mode I fracture toughness of the ceramics was assessed by subjecting a notched semi-circular bending (NSCB) specimen to three-point bending loading. The full-field strains on the surface of the specimens were measured using Digital Image Correlation (DIC) technique, and as a result mode I fracture toughness of ceramics and crack propagation from the existing notch were successfully evaluated. INTRODUCTION The disposal of vast amounts of MTs is considered one of the most significant environmental concerns in the mining industry. The presence of contaminated materials after extracting valuable metals such as gold, silver, and copper can lead to harmful environmental consequences. MTs occupy an extensive area of land in the form of mud-like material in tailing ponds or behind storage dams (Barrie et al., 2015; Kossoff et al., 2014). Exposure of sulfide minerals to water and air typically leads to acid mine drainage which is a widespread environmental hazard resulting from seepage and dam failure. This process can impact groundwater and surface water leading to high levels of iron, aluminum, metals, and sulfates (Kinnunen et al., 2018; Nordstrom, 1999). Over the past few years, a number of researchers studied the potential risks to human health and the harm caused by soils that have been contaminated with heavy metal mine tailings (Ngole-Jeme and Fantke, 2017; Ogola et al., 2002; Olobatoke and Mathuthu, 2016).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".