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Record W2993768076 · doi:10.1039/c9sm02232e

Poisson–Boltzmann modeling and molecular dynamics simulations of polyelectrolyte gel diodes in the static regime

2019· article· en· W2993768076 on OpenAlexafffund
Vasilii Triandafilidi, Savvas G. Hatzikiriakos, Jörg Rottler

Bibliographic record

VenueSoft Matter · 2019
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersUniversity of British ColumbiaAlexander von Humboldt-Stiftung
KeywordsMolecular dynamicsPolyelectrolytePoisson–Boltzmann equationDynamics (music)Statistical physicsPoisson distributionChemical physicsPhysicsDiodeMaterials scienceQuantum mechanicsIonPolymerMathematics

Abstract

fetched live from OpenAlex

We present molecular dynamics (MD) simulations of a model polyelectrolyte gel diode formed by a junction of two oppositely charged crosslinked polyelectrolyte networks, and compare the ion densities and electrostatic field to a corresponding continuum Poisson-Boltzmann (PB) model. At low electrostatic coupling strength (i.e. low Bjerrum lengths), the PB model reproduces the MD simulation results for density and electric field throughout the gel very well. At higher electrostatic coupling (i.e. large Bjerrum lengths) and higher degrees of ionization, the standard PB fails to predict the MD profiles at the diode interface due to counterion condensation, network collapse and field-induced gel deformation. In fact MD simulations predict that the rectifying behavior of diodes operating in such regimes will be much reduced. We develop a modified PB model that accounts for these effects, and show that it produces better agreement with the MD results. The updated PB model can be used for improved modeling of experimental results on PE gel diodes.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.232
Teacher spread0.227 · 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 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

Citations10
Published2019
Admission routes2
Has abstractyes

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