Review on Carbon Dioxide Mineralization in Mafic/Ultramafic Rocks: From Fundamentals to Field Applications
Bibliographic record
Abstract
Preventing the escalating expulsion of carbon dioxide (CO 2 ) into the atmosphere requires effective carbon sequestration strategies. Mineral carbonation (MC) utilizing mafic–ultramafic rocks is a notable technique that ensures long-term CO 2 sequestration. This article presents the core geochemical dissolution and precipitation reactions, analyzing the existing insights as well as advances in upscaling lab outcomes into real–world applications. Ex situ, in situ, and enhanced rock weathering (ERW) MC techniques have been evaluated, along with the significance of governing parameters controlling mineralization performance, such as thermal conditions, CO 2 partial pressure (pCO 2 ), grain size, and the rock’s chemical composition. Moreover, demonstration projects such as the CarbFix pilot project, the Wallula basalt project, and peridotite-based initiatives, with respect to their operational constraints, kinetic barriers, and technological advancements, have been detailed. The assessment of environmental sustainability, financial feasibility, and adverse effects associated with the use of industrial by-products, carbon pricing schemes, and resource recycling strategies has also been discussed. This review also demonstrates the effectiveness of MC as a credible, consistent, and climate-resilient strategy through the integration of fundamental geochemical reactions to field-scale implementations. Future research should emphasize enhancing reaction kinetics through advanced mechanistic understanding guided by high-resolution microscopy, in situ spectroscopy, and AI-driven reactive transport modeling.
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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".