Activation methods for enhancing CO2 mineralization via mine tailings—A critical review
Bibliographic record
Abstract
Greenhouse gas emissions from fossil fuel combustion exacerbate global warming, necessitating scalable and cost-effective carbon capture and storage (CCS) strategies. Mineral carbonation has emerged as a promising solution, permanently converting carbon dioxide (CO 2 ) into stable carbonates while simultaneously repurposing mine tailings for sustainable waste management. Ultramafic and mafic mine tailings, which are rich in Mg- and Ca-bearing minerals, provide abundant and reactive feedstocks for CO 2 sequestration. This review examines the chemical, mineralogical, and physical characteristics of selected tailings from nickel, asbestos, diamond, gold, iron, and platinum group metal (PGM) mines to assess their carbonation potential, and also introduces a mineral-specific analysis of mechanical activation effects across these materials. However, inherent mineralogical differences necessitate tailored activation strategies to increase CO 2 reactivity. To address this, four principal activation methods are evaluated: (1) mechanical activation, which increases the surface area and number of defect sites but has limited dissolution effects; (2) chemical activation, which increases ion availability but raises concerns over reagent costs and waste disposal; (3) thermal activation, which dehydroxylates minerals at ∼650°C to increase reactivity but is energy intensive; and (4) engineered activation, which integrates multiple approaches, such as mechanochemical, thermochemical, and external-field-assisted techniques (e.g., microwaves and ultrasound), to achieve synergistic benefits. However, challenges such as energy optimization, large-scale implementation, and sustainable reagent recovery remain, and these are critically assessed through a cross-method analysis of scalability, cost, and environmental trade-offs. This critical review underscores the transformative potential of mine tailings as valuable resources for commercial-scale CO 2 sequestration, bridging climate change mitigation with circular economy principles and advancing sustainable industrial practices.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".