Molecular Dynamics Simulations of Electrosprayed Water Nanodroplets: Internal Potential Gradients, Location of Excess Charge Centers, and “Hopping” Protons
Bibliographic record
Abstract
Water nanodroplets charged with excess protons play a central role during electrospray ionization (ESI). In the current study molecular dynamics (MD) simulations were used for gaining insights into the nanodroplet behavior based on classical mechanics. The SPC/E water model was modified to permit the inclusion of protons as highly mobile point charges at minimum computational cost. A spherical trapping potential was assigned to every SPC/E oxygen, thereby allowing the formation of protonated water molecules. Within a tightly packed nanodroplet the individual potential wells merge to form a three-dimensional energy landscape that facilitates rapid proton hopping between water molecules. This approach requires short-range modifications to the standard Coulomb potential for modeling electrostatic proton-water interactions. Simulations on nanodroplets consisting of 1248 water molecules and 10 protons (radius, ca. 21 A) result in a proton diffusion coefficient that is in agreement with the value measured in bulk solution. Radial proton distributions extracted from 1 ns MD runs exhibit a large peak around 14 A, in addition to substantial population density closer to the droplet center. Similar radial distributions were found for nanodroplets charged with Na+ ions. This behavior is dramatically different from that expected on the basis of continuum electrostatic theory, which predicts that excess charge should be confined to a thin layer on the droplet surface. One important contributor to this effect seems to be the ordering of water molecules at the liquid/vacuum interface. This ordering results in an electrical double layer, generating a potential gradient that tends to pull positive charge carriers (such as protons, but also others such as Na+ ions) toward the droplet interior. This deviation from the widely assumed surface charge paradigm could have implications for the mechanism by which protonated analyte ions are formed during ESI.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".