We report detailed simulation results on the formation dynamics of an electrical double layer (EDL) inside an electrochemical cell featuring room-temperature ionic liquids (RTILs) enclosed between two planar electrodes. Under relatively small charging currents, the evolution of cell potential from molecular dynamics (MD) simulations during charging can be suitably predicted by the Landau-Ginzburg-type continuum model proposed recently (Bazant et al 2011 Phys. Rev. Lett. 106 046102). Under very large charging currents, the cell potential from MD simulations shows pronounced oscillation during the initial stage of charging, a feature not captured by the continuum model. Such oscillation originates from the sequential growth of the ionic space charge layers near the electrode surface. This allows the evolution of EDLs in RTILs with time, an atomistic process difficult to visualize experimentally, to be studied by analyzing the cell potential under constant-current charging conditions. While the continuum model cannot predict the potential oscillation under such far-from-equilibrium charging conditions, it can nevertheless qualitatively capture the growth of cell potential during the later stage of charging. Improving the continuum model by introducing frequency-dependent dielectric constant and density-dependent ion diffusion coefficients may help to further extend the applicability of the model. The evolution of ion density profiles is also compared between the MD and the continuum model, showing good agreement.

Download full-text PDF

Source
http://dx.doi.org/10.1088/0953-8984/26/28/284109DOI Listing

Publication Analysis

Top Keywords

continuum model
20
cell potential
16
charging conditions
12
dynamics electrical
8
electrical double
8
double layer
8
room-temperature ionic
8
ionic liquids
8
charging
8
constant-current charging
8

Similar Publications

Quantifying natural amyloid plaque accumulation in the continuum of Alzheimer's disease using ADNI.

J Pharmacokinet Pharmacodyn

January 2025

Global PK/PD/PMx, Eli Lilly and Company, 8 Arlington Square West, Downshire Way, Bracknell, Berkshire, RG12 1PU, UK.

Brain amyloid beta neuritic plaque accumulation is associated with an increased risk of progression to Alzheimer's disease (AD) [Pfeil, J., et al. in Neurobiol Aging 106: 119-129, 2021].

View Article and Find Full Text PDF

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.

View Article and Find Full Text PDF

Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.

J Pediatr Surg

January 2025

McGill University Faculty of Medicine and Health Sciences, Canada; Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Qc, Canada.

Purpose: This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.

Methods: An anonymized clinical and operative dataset was retrieved from the medical records of children undergoing emergency appendectomy between 2014 and 2021. We developed an ML pipeline that pre-processed the dataset and developed algorithms to predict 5 appendicitis grades (1 - non-perforated, 2 - localized perforation, 3 - abscess, 4 - generalized peritonitis, and 5 - generalized peritonitis with abscess).

View Article and Find Full Text PDF

Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health.

Psychiatry Clin Neurosci

January 2025

Shanghai Artificial Intelligence Laboratory, Shanghai, China.

Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This review aims to offer a perspective within the actor-network framework, exploring the technical architectures, linguistic dynamics, and psychological effects underlying human-LLMs interactions.

View Article and Find Full Text PDF

We combine atomistic and continuum simulation methods to study the defect chemistry of a model grain boundary in UO. Using atomistic methods, we calculate the formation energies of oxygen interstitials, uranium vacancies, and hole polarons (U ions) across the Σ5(310)[001] symmetric tilt grain boundary. This information is then used as input in a continuum model of point-defect concentrations at the grain boundary and in its vicinity, taking into account electrostatic (space-charge) effects.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!