Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Batteries and Fuel Cells

Batteries and Fuel Cells

A battery is a galvanic cell that is used as a source of electrical power for specific applications. Modern batteries exist in a multitude of forms to accommodate various applications, from tiny button batteries such as those that power wristwatches to the very large batteries used to supply backup energy to municipal power grids. Some batteries are designed for single-use applications and cannot be recharged (primary cells), while others are based on conveniently reversible cell reactions that...

Standard Electrode Potentials

Standard Electrode Potentials

On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Ion Transport in Charged Membranes: Linking Electric-Field-Driven Mechanisms to Pore Size via Perturbation Analysis.

Langmuir : the ACS journal of surfaces and colloids·2026

Same author

Variation in the Spin-Splitting Magnitude from Dynamic Structural Distortions in 2D Hybrid Perovskites.

The journal of physical chemistry letters·2026

Same author

An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.

Network neuroscience (Cambridge, Mass.)·2026

Same author

Identifying structural and dynamic features of proteins for machine learning models that predict rates of energy transfer.

The Journal of chemical physics·2026

Same author

Diving into AI? Exploring the Potential for AI to Tackle Complex Water Quality Challenges.

Environmental science & technology·2026

Same author

Molecular understanding of ion transport in a zwitterionic electrolyte.

The Journal of chemical physics·2025

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

Development of Machine-Learned Interatomic Potentials to Predict Structure, Transport, and Reactivity in

Kamron Fazel¹, Sam Brown², Jacob Clary³

¹Materials Science & Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.

|June 29, 2026

Summary

Researchers developed a machine-learned interatomic potential (MLIP) for hydrated Nafion ionomers and platinum catalysts. This model accurately predicts fuel cell component structure and reactions, aiding performance optimization.

More Related Videos

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Published on: April 27, 2018

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

Published on: May 18, 2020

Related Experiment Videos

Last Updated: Jun 30, 2026

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Synthesis of Platinum-nickel Nanowires and Optimization for Oxygen Reduction Performance

Published on: April 27, 2018

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

A Salt-Templated Synthesis Method for Porous Platinum-based Macrobeams and Macrotubes

Published on: May 18, 2020

Area of Science:

Computational materials science
Chemical engineering
Catalysis

Background:

Machine-learned interatomic potentials (MLIPs) offer high accuracy and speed for materials simulations.
Training robust MLIPs for complex multicomponent systems like fuel cell components remains challenging.
Nafion ionomers and platinum catalysts are critical for fuel cell performance.

Purpose of the Study:

To develop and train a robust MLIP for hydrated Nafion ionomers and platinum catalysts.
To investigate the structure, proton mobility, and reaction dynamics of the platinum-Nafion interface.
To leverage MLIP insights for optimizing fuel cell performance.

Main Methods:

Constructed a diverse training dataset for bulk Nafion and platinum-Nafion interfaces.
Trained a machine-learned interatomic potential (MLIP) model.
Utilized the MLIP to simulate polymer structure, proton mobility, and interfacial reactions.

Main Results:

Achieved excellent accuracy for predicting structures and reactions within the training set.
Captured both vehicular and Grotthuss proton transport mechanisms.
Identified challenges in calculating converged diffusivities due to simulation timescale limitations.

Conclusions:

The developed MLIP provides valuable insights into platinum-Nafion system properties.
The approach can be extended to optimize fuel cell performance and study other chemical processes.
Further development of MLIPs is needed for long-timescale transport simulations.