The Quantification of Ultramafic Mine Waste Reactivity for Carbon Mineralization
Bibliographic record
Abstract
The urgent need for net-negative greenhouse gas emissions in the face of climate change is driving the global energy transition. Essential to this transition is the growing demand for critical metals, which leads to the need for more sustainable mining activities. Carbon mineralization via ultramafic-type minerals and tailings is one of the many strategies that can effectively reduce the carbon footprint associated with mining. The process involves the liberation of cations through dissolution and the subsequent precipitation of carbonate minerals to capture and store CO2 permanently. In this context, the rate and capacity of cation liberation are crucial, dictating the suitability of ultramafic mine wastes for carbon sequestration.Our earlier research focused on the characterization of 'labile cations,' derived from transient, early-stage dissolutions, which signify a critical aspect of the reactivity and carbon capture potential of ultramafic tailings. Labile cations, predominantly governed by mineral content, are essential for rapid and cost-effective carbon capture using these tailings. Despite recognizing the concept of labile cations, understanding the sources and controls of labile Mg in ultramafic minerals, rocks, and tailings remains limited. Moreover, there is a pressing need for the development of efficient, user-friendly, and cost-effective experimental, numerical, and technical tools. Addressing this, our study employs batch dissolution experiments and data science techniques, including Multiple Linear Regression (MLR) and Principal Component Analysis (PCA), to assess carbon mineralization reactivity. We report on the extraction of labile Mg from various sources such as serpentine, hydrotalcite group minerals, serpentinite, and ultramafic tailings, examining the impact of factors like grain size, ore heterogeneity, and brucite content.Mineral content is a primary control on labile Mg content. Interestingly, labile Mg content is quite variable within mineral groups. For instance, within the serpentine group, chrysotile and lizardite contribute notably higher Mg than antigorite. Likewise, the reactivity of hydrotalcite minerals is influenced more by the nature of their divalent and trivalent cations rather than by anion species. We also find that the original rock composition and mineral alteration progression are crucial in determining brucite abundance, serpentine type, and labile Mg accessibility. MLR and PCA analysis highlights the critical role of mineralogy and reactive surface area in predicting carbon mineralization reactivity. Overall, the results from this study offer significant advancements in the assessment of carbon mineralization potential in ultramafic mine wastes. These insights are instrumental in refining both ex-situ and in-situ carbonation strategies and extend their applicability to a broader range of alkaline solid wastes for CO2 capture and storage.
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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".