Molecular Interactions and Responsive Locations of CO<sub>2</sub>-Responsive Surfactants in an Oil–Water–Surfactant System: Molecular Dynamics Simulation and Free Energy Perturbation
Bibliographic record
Abstract
Understanding the mechanism of a CO 2 -responsive surfactant is essential for enhancing its industrial applications. Conventional experimental methods face challenges in pinpointing the exact location of proton transfer within the system and in accurately describing the impact of intermolecular and intramolecular interactions on the CO 2 responsiveness of such substances. To address this gap, this study employs molecular dynamics simulations and free energy perturbation methods to investigate the proton transfer process between a CO 2 -responsive cationic surfactant N ′-dodecyl- N, N -dimethylacetamidinium (DMAAH + ) and its counterion bicarbonate ion at the oil–water interface and micelle surface and in the bulk aqueous phase. Molecular dynamics simulations identified potential locations for the proton transfer process within the system and elucidated the types of interactions contributing to changes in Gibbs free energy. Subsequently, free energy perturbation was employed to calculate Gibbs free energy changes associated with proton transfer at different locations. The respective contributions of various intramolecular and intermolecular interactions were then compared and analyzed. It has been revealed that the deprotonation process is not thermodynamically spontaneous at all three responsive locations. The proton transfer occurs more frequently at the oil–water interface than at the micelle surface and is less common in the bulk aqueous phase. The findings enhance our understanding of the fundamental mechanisms governing the responsiveness of CO 2 -responsive surfactants and provide valuable insights for their practical application in industrial processes.
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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".