Molecular Dynamics Simulations of the Electrospray Process: Formation of NaCl Clusters via the Charged Residue Mechanism
Bibliographic record
Abstract
Electrospray ionization (ESI) produces desolvated ions from solution phase analytes for mass spectrometric detection. The final steps of gas phase ion formation from nanometer-sized solvent droplets remain a matter of debate. According to the ion evaporation model (IEM), analytes are ejected from the droplet surface via field emission, whereas the charged residue model (CRM) envisions that ions are released upon droplet evaporation to dryness. Exposure of salt solutions to ESI conditions produces a range of cluster ions. Despite the rich literature on these systems, it is still unclear if these salt clusters form via the CRM or the IEM. The current study explores the formation of Na(n)Cl(m)((n-m)+) clusters from aqueous sodium chloride solution under positive and negative polarity conditions. Molecular dynamics (MD) methods are used for simulating the temporal evolution of charged NaCl-containing water droplets. A trajectory stitching approach is developed for continuously removing evaporated moieties from the simulation, thereby dramatically reducing computational cost. In addition, this procedure ensures adequate temperature control and eliminates evaporative cooling that would otherwise slow down the process. Continuous water evaporation leads to progressive droplet shrinkage, while the emission of solvated single ions ensures that the system remains at ca. 90% of the Rayleigh limit. Early during the process all ions in the droplet behave as freely dissolved species, but after a few nanoseconds at 370 K the systems gradually morph into amorphous wet salt aggregates. Ultimately, free Na(n)Cl(m)((n-m)+) clusters form as the last solvent molecules evaporate. Our data therefore provide direct evidence that sodium chloride cluster formation during ESI proceeds via the CRM. The IEM nonetheless plays an ancillary role, as it allows the system to shed charge (mostly in the form of hydrated Na(+) or Cl(-)) during droplet shrinkage. It appears that this study marks the first successful MD simulation of complete CRM processes.
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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 | 0.001 |
| 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) | 0.003 | 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".