Ejection of Solvated Ions from Electrosprayed Methanol/Water Nanodroplets Studied by Molecular Dynamics Simulations
Bibliographic record
Abstract
The ejection of solvated small ions from nanometer-sized droplets plays a central role during electrospray ionization (ESI). Molecular dynamics (MD) simulations can provide insights into the nanodroplet behavior. Earlier MD studies have largely focused on aqueous systems, whereas most practical ESI applications involve the use of organic cosolvents. We conduct simulations on mixed water/methanol droplets that carry excess NH(4)(+) ions. Methanol is found to compromise the H-bonding network, resulting in greatly increased rates of ion ejection and solvent evaporation. Considerable differences in the water and methanol escape rates cause time-dependent changes in droplet composition. Segregation occurs at low methanol concentration, such that layered droplets with a methanol-enriched periphery are formed. This phenomenon will enhance the partitioning of analyte molecules, with possible implications for their ESI efficiencies. Solvated ions are ejected from the tip of surface protrusions. Solvent bridging prior to ion secession is more extensive for methanol/water droplets than for purely aqueous systems. The ejection of solvated NH(4)(+) is visualized as diffusion-mediated escape from a metastable basin. The process involves thermally activated crossing of a ~30 kJ mol(-1) free energy barrier, in close agreement with the predictions of the classical ion evaporation model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".