Surface Charge of Electrosprayed Water Nanodroplets: A Molecular Dynamics Study
Bibliographic record
Abstract
Aqueous nanodroplets that contain excess charge carriers play a central role during the electrospray ionization (ESI) process. An interesting question concerns the charge carrier location in these systems. In analogy to the behavior of metallic conductors, it is often assumed that excess ions are confined to a thin layer on the droplet surface. However, it is unclear whether simple electrostatic arguments adequately reflect the nanodroplet behavior. In particular, most ions tend to be heavily solvated, such that placing them at the liquid/vapor interface would be enthalpically unfavorable. In this work, molecular dynamics simulations are used to study the properties of Na(+)-containing water nanodroplets close to the Rayleigh limit. In apparent violation of the surface charge paradigm, it is found that the ions reside inside the droplet. Electrostatic mapping reveals that all of the excess charge is nonetheless located on the surface. This conundrum is resolved by considering the effects of orientational water polarization. Buried Na(+) ions cause large-scale dipole ordering that extends all the way to the droplet periphery. Here, the positive ends of water dipoles preferentially point into the vapor phase. These half-dipoles in the outermost droplet layers assume the role of surface charge, while solvation effectively neutralizes Na(+) ions in the interior. Overall, our data reaffirm the validity of the surface charge paradigm for ESI nanodroplets, albeit with the caveat that this paradigm does NOT require charge carriers (ions) to be located at the water/vapor interface.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".