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Record W4240200192 · doi:10.26434/chemrxiv.13788865

The Relation Between Ejection Mechanism and Ion Abundance in the Electric Double Layer of Drops

2021· preprint· en· W4240200192 on OpenAlexafffund
Victor Kwan, Ryan O’Dwyer, David R. Laur, Styliani Consta

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsWestern University
FundersUniversidad de Buenos AiresNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of Toronto
KeywordsIonChemistryEvaporationChemical physicsMolecular dynamicsAtomic physicsAnalytical Chemistry (journal)PhysicsComputational chemistryThermodynamicsChromatography

Abstract

fetched live from OpenAlex

<div> Charged droplets have been associated with distinct chemical reactivity. It is assumed that the composition of the surface layer plays a critical role in enhancing the reaction rates in the droplets relative to their bulk counterparts. We use atomistic modeling to relate the localization of the ions in the surface layer to their ejection propensity. We find that<br>the ion ejection takes place via a two-stage process. Firstly, a conical protrusion emerges as a result of a global droplet deformation that is insensitive to the locations of single ions. The ions are subsequently ejected as they enter the conical regions. The study provides mechanistic insight into the<br> ion-evaporation mechanism, which can be used to revise the commonly used ion-evaporation models. We argue that atomistic molecular dynamics simulations of minute nano-drops, do not sufficiently distinguish the ion-evaporation mechanism from a Rayleigh fission. We explain mass spectrometry data on the charge state of small globular proteins and the existence of super-charged droplet states (above the Rayleigh limit) that have been detected in experiments. <br></div><div><br></div><div><br></div><div><br></div>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2021
Admission routes2
Has abstractyes

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