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Record W4252772883 · doi:10.26434/chemrxiv.10049846.v4

Effect of Droplet Size and Counterions on the Spatial Distribution of Ions

2020· preprint· en· W4252772883 on OpenAlexaff
Victor Kwan, Styliani Consta

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsIonChemical physicsHydroniumCounterionScalingElectric fieldRange (aeronautics)Molecular dynamicsCharge (physics)Surface chargeAqueous solutionCharge densityMaterials scienceChemistryMolecular physicsPhysicsComputational chemistryPhysical chemistryGeometry

Abstract

fetched live from OpenAlex

Charged droplets have become a new environment for accelerating chemical reactions by orders of magnitude relative to their bulk analogues. Nevertheless the reaction mechanisms still remain unknown. Here we investigate the ion spatial distributions and surface charge in aqueous droplets with diameters in the range of 5 nm to 16 nm with and without counterions using molecular dynamics. The charge carriers are Na, Cl, I, ions and model hydronium ions. We demonstrate the convergence of ion spatial distributions. Scaling of the ion distributions reveals underlying universal behavior. The convergence allows one to extrapolate the simulation results from nanoscopic dimensions to larger ones, which are still inaccessible to atomistic modeling. The surface excess charge and electric field are also computed. We find that the surface excess charge layer is approximately 1.5 nm-1.7 nm thick and that approximately 55%-33 % (from smaller to larger droplets) of the total number of ions reside in this layer. For the first time droplet sizes that are accessible to experimental scrutiny are modeled atomistically.

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 score0.897
Threshold uncertainty score0.550

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 integrity0.0000.000
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 head0.004
GPT teacher head0.200
Teacher spread0.195 · 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
Published2020
Admission routes1
Has abstractyes

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