Release Mechanisms of Poly(ethylene glycol) Macroions from Aqueous Charged Nanodroplets
Bibliographic record
Abstract
Ion-release processes in nanodroplets that contain excess charge are of central importance in atmospheric aerosols as well as in determining the charge state distributions of macroions that are detected in electrospray mass spectrometry (ESMS) experiments. We performed molecular simulations of systems of a poly(ethylene glycol) (PEG) associated with various ions (Na(+), Li(+),Ca(2+)) in aqueous charged nanodroplets in order to investigate the manner that the macroion emerges from an aqueous nanodroplet as well as its final charge state. In the study we focused on a specific region of the parameter space with respect to charge and size of droplets that is close to the Rayleigh limit. We found that for sizes of droplets with linear dimensions of several nanometers and length of PEG up to 100 monomers, the PEG macroion emerges from the droplet following a three-step process: (i) phase separation, (ii) gradual extension of the macroion out of the droplet, and (iii) drying-out of the solvent or spontaneous detachment of the macroion from the droplet. The third step is determined by the ratio of charge on the macroion to the ions in the water portion of the droplet. The chemical transformation that is caused in PEG by the transfer of ions from the solvent into PEG determines its release mechanism. When the charge is carried by macroions, the charge-induced instability manifests by following one of the expected scenarios of Rayleigh instability; however, the assumptions of the Rayleigh model break down. We also examined the release of the macroion below the Rayleigh limit, and we found that the macroion emerges from the droplet by drying-out of the solvent. On balance of phenomenological evidence, we concluded that the ion-evaporation mechanism (IEM) in its most common meaning is not the followed mechanism for the parameter space of the systems that we studied. The final charge state of the macroion is in excellent agreement with the experimental data of Fenn and co-workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".