Interfacial water structure effect on CO2 solubility in water-saturated silica confinements: A molecular perspective
Bibliographic record
Abstract
Hypothesis Solubility trapping is a key mechanism in geological carbon sequestration (GCS), yet CO 2 solubility in water-filled nanopores often deviates markedly from bulk behavior. We hypothesize that variations in CO 2 solubility within silica nanopores originate from differences in interfacial water structure that are controlled by surface chemistry. In particular, specific Si-OH arrangements on Q2, Q3, and Q4 silica surfaces (defined by the number of Si atoms bonded through oxygen to a central Si atom) modulate hydrogen-bonding (HB) environments and adsorptive volumes that regulate CO 2 -water-solid interactions. Simulations We conducted molecular dynamics simulations of water-saturated Q2, Q3, and Q4 silica confinements under representative GCS conditions. Interfacial density profiles, HB distributions, and CO 2 spatial probability maps were analyzed to quantify fluid-solid interactions and to evaluate CO 2 solubility relative to bulk water. Findings Hydrophilic Q3 surfaces exhibit enhanced CO 2 solubility compared to the bulk liquid, arising from CO 2 -water co-adsorption facilitated by a dense interfacial HB network. Hydrophobic Q4 confinements, by contrast, show pronounced over-solubility dominated by strong direct CO 2 adsorption within enlarged low-HB regions. Q2 surfaces display intermediate behavior reflecting mixed hydrophilic-hydrophobic character. We introduce two mechanistic descriptors, adsorptive volume and HB site density. High HB site density promotes hydrophilicity and co-adsorption, whereas large adsorptive volume favors direct CO 2 adsorption and over-solubility. Overall, these results demonstrate that CO 2 solubility in silica nanopores is jointly governed by interfacial water structure and surface chemistry. The findings provide molecular-scale insights into solubility trapping in silica-rich formations and inform the design of engineered materials for CO 2 capture and storage.
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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) | 0.000 | 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".