Cation Exchange in Smectites as a New Approach to Mineral Carbonation
Bibliographic record
Abstract
Mineral carbonation of alkaline mine residues is a carbon dioxide removal (CDR) strategy that can be employed by the mining industry. Here, we describe the mineralogy and reactivity of processed kimberlites and kimberlite ore from Venetia (South Africa) and Gahcho Kué (Canada) diamond mines, which are smectite-rich (2.3–44.1 wt.%). Whereas, serpentines, olivines, hydrotalcites and brucite have been traditionally used for mineral carbonation, little is known about the reactivity of smectites to CO 2 . The smectite from both mines is distributed as a fine-matrix and is saponite, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msubsup><mml:mrow><mml:mtext>M</mml:mtext></mml:mrow><mml:mrow><mml:mstyle class="text"><mml:mi>x</mml:mi></mml:mstyle><mml:mo>/</mml:mo><mml:mstyle class="text"><mml:mi>m</mml:mi></mml:mstyle></mml:mrow><mml:mrow><mml:mstyle class="text"><mml:mi>m</mml:mi></mml:mstyle><mml:mo>+</mml:mo></mml:mrow></mml:msubsup></mml:math> Mg 3 (Al x Si 4−x )O 10 (OH) 2 · n H 2 O, where the layer charge deficiency is balanced by labile, hydrated interlayer cations (M m + ). A positive correlation between cation exchange capacity and saponite content indicates that smectite is the most reactive phase within these ultramafic rocks and that it can be used as a source of labile Mg 2+ and Ca 2+ for carbonation reactions. Our work shows that smectites provide the fast reactivity of kimberlite to CO 2 in the absence of the highly reactive mineral brucite [Mg(OH) 2 ]. It opens up the possibility of using other, previously inaccessible rock types for mineral carbonation including tailings from smectite-rich sediment-hosted metal deposits and oil sands tailings. We present a decision tree for accelerated mineral carbonation at mines based on this revised understanding of mineralogical controls on carbonation potential.
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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) | 0.002 | 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".