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Record W2329151748 · doi:10.1021/jp301577b

Release Mechanisms of Poly(ethylene glycol) Macroions from Aqueous Charged Nanodroplets

2012· article· en· W2329151748 on OpenAlexaff
Jun Kyung Chung, Styliani Consta

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2012
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsChemical physicsEthylene glycolAqueous solutionChemistryIonPEG ratioSolventChemical engineeringAnalytical Chemistry (journal)ChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.245
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2012
Admission routes1
Has abstractyes

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