Solution additives promoting the onset of MgCO3 nucleation
Bibliographic record
Abstract
Formed via aqueous carbonation of Mg2+ ions, the crystallization of magnesite (MgCO3) is a promising carbon capture and reuse technology, albeit limited by the slow precipitation of MgCO3. Although magnesite is naturally abundant, forming at low temperature conditions, its production is an energy-intensive process due to the temperatures required to prevent the formation of hydrated phases. The principle difficulty arises from the very strong Mg2+···H2O interaction, raising barriers to dehydration. Using atomistic simulations, we have investigated the influence of thirty additive anions (Xn–, n = 1–3), ranging from simple halides to more complex molecules, on the first two steps of MgCO3 aggregation from solution: Mg2+ dehydration and Mg2+∙∙∙CO32– pairing. We have computed the thermodynamic stability of solvent shared ion pairs, Mg2+···H2O···Xn–, and contact ion pairs, Mg2+···Xn–, with Mg2+ to reveal the propensity of solution additives to inhibit Mg2+∙∙∙CO32– formation. We have determined the stabilization of undercoordinated hydrated Mg2+ states with a vacant coordination site to which CO32– can bind, subsequently initiating MgCO3 nucleation or Mg2+ incorporation into the crystal lattice. Extensive molecular dynamics simulations of electrolyte solutions containing Na2CO3 with different sources of Mg2+, MgCl2, MgSO4 and Mg(CH3COO)2, further shows that the degree of dehydration of Mg2+ and the structure of prenucleation MgCO3 clusters changes depending on the type counterion. Through a fundamental understanding of the role of solution additives in the mechanism of Mg2+ dehydration, our computational study can rationalize previously reported experimental observation of the effect of solvation environments on the growth of magnesite. This understanding may contribute to identifying solution composition conditions that could promote the low-temperature CO2 conversion into MgCO3.
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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 | 0.001 |
| 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".