Sulfolane-Induced Supercharging of Electrosprayed Salt Clusters: An Experimental/Computational Perspective
Bibliographic record
Abstract
It is well-known that supercharging agents (SCAs) such as sulfolane enhance the electrospray ionization (ESI) charge states of proteins, although the mechanistic origins of this effect remain contentious. Only very few studies have explored SCA effects on analytes other than proteins or peptides. This work examines how sulfolane affects electrosprayed NaI salt clusters. Such alkali metal halide clusters have played a key role for earlier ESI mechanistic studies, making them interesting targets for supercharging investigations. ESI of aqueous NaI solutions predominantly generated singly charged [Na n I ( n –1) ] + clusters. The addition of sulfolane resulted in abundant doubly charged [Na n I ( n –2) Sulfolane s ] 2+ species. These experimental data for the first time demonstrate that electrosprayed salt clusters can undergo supercharging. Molecular dynamics (MD) simulations of aqueous ESI nanodroplets containing Na + /I – with and without sulfolane were conducted to obtain atomistic insights into the supercharging mechanism. The simulations produced [Na n I i ] z + and [Na n I i Sulfolane s ] z + clusters similar to those observed experimentally. The MD trajectories demonstrated that these clusters were released into the gas phase upon droplet evaporation to dryness, in line with the charged residue model. Sulfolane was found to evaporate much more slowly than water. This slow evaporation, in conjunction with the large dipole moment of sulfolane, resulted in electrostatic stabilization of the shrinking ESI droplets and the final clusters. Hence, charge–dipole stabilization causes the sulfolane-containing droplets and clusters to retain more charge, thereby providing the mechanistic foundation of salt cluster supercharging.
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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 | 0.001 | 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".