What controls the labile cations content in ultramafic minerals and tailings for carbon capture and storage: An experimental approach
Bibliographic record
Abstract
• Batch dissolution test using CO 2 at various concentrations is an efficient and accurate method to quantify reactivity (i.e., labile cations) for carbon mineralization. • Labile Mg content of serpentine group minerals is determined by serpentine polymorphs and reactive surface area. • Labile Mg content in hydrotalcite group minerals is determined by the nature of the divalent and trivalent cations within the mineral structure. • Mineral type, abundance and surface area are fundamental controls of labile Mg in ultramafic rocks and tailings. The growing demand for effective carbon mineralization technologies to combat climate change necessitates precise reactivity characterization of feedstocks. In this study, we introduced and validated a batch dissolution experimental protocol for efficient quantification of labile Mg, an indicator of carbon mineralization reactivity derived from ultramafic rocks, minerals, and tailings. This method is used 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. Antigorite exhibits the lowest labile Mg content within the serpentine mineral group, whereas chrysotile releases the most. Differences in labile Mg content within the hydrotalcite group depend on the trivalent cation species, with Fe 3+ -rich pyroaurite and iowaite demonstrating higher labile Mg content than Cr 3+ , Al 3+ -rich stichtite and hydrotalcite. We found that grain size impacts the reactive surface area of ultramafic rocks and tailings, while protolith composition and rock alteration stages affect mineralogy. Consequently, under consistent geochemical conditions, we identified mineral type, abundance and reactive surface area as primary controls of labile Mg content. Principal Component Analysis (PCA) further validated our findings, showing that mineralogy and reactive surface area could account for over 90 % of the variability in labile Mg measurements. Predicting labile Mg content based on these variables yielded results comparable to experimental outcomes, providing insights into carbon mineralization reactivity and demonstrating methods for accurate evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".