Effect of counterions on the charging mechanisms of a macromolecule in aqueous nanodrops
Bibliographic record
Abstract
We report the first molecular dynamics study of the effect of counterions on the charging mechanisms of a macromolecule found in an aqueous droplet that contains excess charge. To investigate the principles of the charging mechanisms of a macromolecule in a droplet, we simulate aqueous droplets that contain a poly(ethylene glycol) (PEG) molecule, sodium, and chloride ions. We study the effect of counterions by varying the concentration of the chloride ions and the temperature of the droplets. We find that the size of the droplet from which the macromolecule is released is determined by the competition between the counterions and the macromolecule for capturing the sodium ions. In droplets with radii in the range of 4 nm and smaller, [Na2Cl](+) ion complexes and sodium chloride aggregates are formed. The smaller the droplet the more pronounced is the formation of the NaCl aggregates. At very high temperature, in the larger droplets the Na(+) ions are distributed throughout the entire droplet. Therefore, the sodiated PEG is released with a higher average charge than from droplets with no counterions because it has access to a higher concentration of Na(+) ions. At moderately high temperature, the NaCl aggregates do not affect the final charge state of the macromolecule relative to the no-counterion droplets. We also report that regardless of the concentration of the counterions, the temperature plays a critical role in determining the nature of the droplet shape fluctuations that are responsible for the charging of a macromolecule and its extrusion from a droplet. At high temperature the macromolecule is released by the formation of a Taylor cone that transports ions onto the macromolecule. Differently, at lower temperature the Taylor cones are absent or have subsided. These findings provide insight into the mechanisms that macromolecules acquire their charge in droplets produced in electrospray ionization experiments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".