Reaction Mechanism for the Aqueous-Phase Mineral Carbonation of Heat-Activated Serpentine at Low Temperatures and Pressures in Flue Gas Conditions
Bibliographic record
Abstract
Mineral carbonation is known as one of the safest ways to sequester CO2. Nevertheless, the slow kinetics and low carbonation rates constitute a major barrier for any possible industrial application. To date, no studies have focused on reacting serpentinite with a relatively low partial pressure of CO2 (pCO2) close to flue gas conditions. In this work, finely ground and heat-treated serpentinite [Mg3Si2O5(OH)4] extracted from mining residues was reacted with a 18.2 vol % CO2 gas stream at moderate global pressures to investigate the effect on CO2 solubility and Mg leaching. Serpentinite dissolution rates were also measured to define the rate-limiting step. Successive batches of gas were contacted with the same serpentinite to identify surface-limiting factors using scanning electron microscopy (SEM) analysis. Investigation of the serpentinite carbonation reaction mechanisms under conditions close to a direct flue gas treatment showed that increased dissolution rates could be achieved relative to prior work, with an average Mg dissolution rate of 3.55 × 10(-11) mol cm(-2) s(-1). This study provides another perspective of the feasibility of applying a mineral carbonation process to reduce industrial greenhouse gas (GHG) emissions from large emission sources.
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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 | 0.000 | 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 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".