Feasibility of a Mineral Carbonation Technique Using Iron-Silicate Mining Waste by Direct Flue Gas CO2 Capture and Cation Complexation Using 2,2′-Bipyridine
Bibliographic record
Abstract
Mineral carbonation is gaining increasing attention for its ability to sequester CO2. The main challenge is doing it economically and energy-efficiently. Recently, many studies have focused on the aqueous reaction of carbon dioxide with the alkaline earth minerals such as serpentine, Mg-rich olivine and wollastonite. Nevertheless, Fe-rich olivines have been poorly studied because of their high energy demand, which make them unfeasible for industrial implementation. This article describes the feasibility of an indirect mineral carbonation process using silicic, Fe-rich mining waste with direct flue gas CO2 via iron complexation using 2,2′-bipyridine. The overall process was performed in three main steps: leaching, iron complexation, and aqueous mineral carbonation reactions. The preferential parameters resulted in a recirculation scenario, where 38% of Fe cations were leached, complexed, and reacted under mild conditions. CO2 uptake of 57.3% was achieved, obtaining a Fe-rich carbonate. These results are promising for the application of mineral carbonation to reduce CO2 emissions. Furthermore, the greenhouse gas balance had a global vision of the overall reaction’s feasibility. The results showed a positive balance in CO2 removal, with an estimated 130 kg CO2/ton of residue. Although an exhaustive study should be done, the new and innovative mineral carbonation CO2 sequestration approach in this study is promising.
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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 | 0.001 | 0.000 |
| 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".