Unveiling hidden reaction kinetics of carbon dioxide in supercritical aqueous solutions
Bibliographic record
Abstract
Dissolution of CO 2 in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite numerous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics (AIMD) simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO 2 in supercritical water both in the bulk and nanoconfined states. The integration of unsupervised learning with first-principles data allows us to identify complex reaction coordinates and pathways automatically instead of a priori human speculation. Interestingly, our unbiased modeling found an unknown pathway of dissolving CO 2 (aq) under graphene nanoconfinement, involving the pyrocarbonate anion [C 2 O 5 2 − (aq)] as an intermediate state. The pyrocarbonate anion was previously hypothesized to have a fleeting existence in water; however, our study reveals that it is a crucial reaction intermediate and stable carbon species in the nanoconfined solutions. We even observed the formation of pyrocarbonic acid [H 2 C 2 O 5 (aq)], which was unknown in water, in our AIMD simulations. The unexpected appearance of pyrocarbonates is related to the superionic behavior of the confined solutions. We also found that carbonation reactions involve collective proton transfer along transient water wires, which exhibits concerted behavior in the bulk solution but proceeds stepwise under nanoconfinement. The first-principles Markov state models show substantial promise for elucidating complex reaction kinetics in aqueous solutions. Our study highlights the importance of large oxocarbons in aqueous carbon reactions, with great implications for the deep carbon cycle and the sequestration of CO 2 .
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".