Influence of Iron Substitution and Solution Composition on Brucite Carbonation
Bibliographic record
Abstract
Carbon neutral or negative mining can potentially be achieved by integrating carbon mineralization processes into the mine design, operations, and closure plans. Brucite [Mg(OH) 2 ] is a highly reactive mineral present in some ultramafic mine tailings with the potential to be rapidly carbonated and can contain significant amounts of ferrous iron [Fe(II)] substituted for Mg; however, the influence of this substitution on carbon mineralization reaction products and efficiency has not been thoroughly constrained. To better assess the efficiency of carbon storage in brucite-bearing tailings, we performed carbonation experiments using synthetic Fe(II)-substituted brucite (0, 6, 23, and 44 mol % Fe) slurries in oxic and anoxic conditions with 10% CO 2 . Additionally, the carbonation process was evaluated using different background electrolytes (NaCl, Na 2 SO 4, and Na 4 SiO 4 ). Our results indicate that carbonation efficiency decreases with increasing Fe(II) substitution. In oxic conditions, precipitation of ferrihydrite [Fe 10 III O 14 (OH) 2 ] and layered double hydroxides {e.g., pyroaurite [Mg 6 Fe 2 III (OH) 16 CO 3 ·4H 2 O]} limited carbonation efficiency. Carbonation in anoxic environments led to the formation of Fe(II)-substituted nesquehonite (MgCO 3 ·3H 2 O) and dypingite [Mg 5 (CO 3 ) 4 (OH) 2 ·∼5H 2 O], as well as chukanovite [Fe 2 II CO 3 (OH) 2 ] in the case of 23 and 44 mol % Fe(II)-brucite carbonation. Carbonation efficiencies were consistent between chloride- and sulfate-rich solutions but declined in the presence of dissolved Si due to the formation of amorphous SiO 2 · n H 2 O and Fe–Mg silicates. Overall, our results indicate that carbonation efficiency and the long-term fate of stored CO 2 may depend on the amount of substituted Fe(II) in both feedstock minerals and carbonate products.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".