What controls the labile cations content in ultramafic minerals and tailings for carbon capture and storage: An experimental approach
Bibliographic record
Abstract
The growing demand for effective carbon mineralization technologies to combat climate change requires precise reactivity characterization of feedstocks. In this study, we introduced and validated a batch dissolution experimental protocol for rapid and straightforward quantification of labile Mg, a representative indicator of carbon mineralization reactivity derived from ultramafic rocks, minerals, and tailings. This method was applied to characterize labile Mg content in various ultramafic minerals, including serpentine and hydrotalcite group minerals, as well as rocks like serpentinite, dunite, harzburgite, and tailings. Results indicate that, within the serpentine group, antigorite exhibits the lowest labile Mg content, whereas chrysotile releases the most. Differences in labile Mg content within the hydrotalcite group were found to be dependent on trivalent cation species, with pyroaurite and iowaite (Fe3+ rich) demonstrating higher labile Mg compared to stichtite and hydrotalcite (Cr3+ rich). We find that grain size impacts reactive surface area for ultramafic rocks and tailings, while protolith composition and rock alteration stages affect mineralogy. Consequently, under consistent geochemical conditions, we identified reactive surface area and mineral composition as primary controls of labile Mg content. Principal Component Analysis (PCA) was employed to validate these findings further. Prediction of labile Mg content based on these variables yielded results comparable to experimental outcomes, providing insight into carbon mineralization reactivity and demonstrating methods for accurate evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".