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

Electrochemical Systems

Electrochemical Systems

Electrochemical systems provide a fascinating insight into the dynamic interplay of charged species within various phases. One notable example is the interaction between a membrane permeable to K⁺ ions but not to Cl⁻ ions, separating an aqueous KCl solution from pure water. As K⁺ ions diffuse through the membrane, they generate net charges on each phase, leading to a potential difference between them.Similarly, when a piece of Zn is immersed in an aqueous ZnSO₄ solution, the Zn metal, composed...

You might also read

Related Articles

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

Sort by

Same author

Metabolomic and lifestyle profiles refine BMI-metabolic phenotypes in older adults.

Cell reports. Medicine·2026

Same author

A novel insight into the risk of depression and anxiety onset in Parkinson's disease: the implications of GBA1 and LRRK2.

Parkinsonism & related disorders·2026

Same author

Multimodal clocks of human aging.

Cell·2026

Same author

Flow velocity regulates gonadal development via actin cytoskeleton and lipid metabolic pathways: A transcriptomic study in juvenile Acrossocheilus fasciatus (Steindachner, 1892).

Comparative biochemistry and physiology. Part D, Genomics & proteomics·2026

Same author

Ag Nanocluster-Triggered Lattice Oxygen Activation in Amorphous Sulfide Matrices for Superior Oxygen Evolution.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Duhuo Jisheng decoction alleviates knee osteoarthritis via synovial mesenchymal stem cell-derived exosomes mediating the microRNA-194-5p/wnt signaling pathway.

Journal of ethnopharmacology·2026

Same journal

Bioinspired Electrostatic-Field Perturbated Sensing for General Material Noncontact Perception.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Engineering Layered Magnetic Hydrogels for Cell Placement via Shear and Magnetic Field-Induced Assembly.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Interfacial Acid Sites-Mediated ZnO-Based Electrocatalysts for Sustainable Dual-Pathway H<sub>2</sub>O<sub>2</sub> Production and Rechargeable Zn-H<sub>2</sub>O<sub>2</sub> Electrochemical Cell.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Zein-Ceria Hybrid Microparticles Enable Long-Term ROS-Scavenging Oxygenation for Osteogenic Microtissues Engineering.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

Toward Practical Solid-State Lithium Batteries With High-Nickel Cathodes: An Interface-Centered Perspective.

Advanced materials (Deerfield Beach, Fla.)·2026

Same journal

A Planarity-Hindrance Co-Balance Strategy to Develop Antiparallel H-Aggregates With Minimal Absorbance Blueshift for Type I Photodynamic Therapy.

Advanced materials (Deerfield Beach, Fla.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 13, 2026

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

Published on: March 20, 2019

Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to

Tianyi Gao¹, Honghao Huang¹, Yang Liu¹

¹Department of Materials Science, Fudan University, Shanghai, 200433, China.

Advanced Materials (Deerfield Beach, Fla.)

|November 26, 2025

Summary

This summary is machine-generated.

Machine learning (ML) accelerates the discovery of high-performance nanocatalysts by optimizing synthesis and linking structure to function. This approach enables intelligent nanomaterial design through data-driven methods and autonomous experimentation.

Keywords:

data‐driven design electrocatalysis large language models machine learning nanomaterials

More Related Videos

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

Author Spotlight: Tracking Electrochemistry on Single Nanoparticles with Surface-Enhanced Raman Scattering Spectroscopy and Microscopy

Author Spotlight: Tracking Electrochemistry on Single Nanoparticles with Surface-Enhanced Raman Scattering Spectroscopy and Microscopy

Published on: May 12, 2023

Related Experiment Videos

Last Updated: Jun 13, 2026

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

A Closed-Type Wireless Nanopore Electrode for Analyzing Single Nanoparticles

Published on: March 20, 2019

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production

Published on: December 6, 2021

Author Spotlight: Tracking Electrochemistry on Single Nanoparticles with Surface-Enhanced Raman Scattering Spectroscopy and Microscopy

Author Spotlight: Tracking Electrochemistry on Single Nanoparticles with Surface-Enhanced Raman Scattering Spectroscopy and Microscopy

Published on: May 12, 2023

Area of Science:

Materials Science
Nanotechnology
Catalysis

Background:

Designing functional nanomaterials is challenging due to complex synthesis-structure-performance relationships.
Nanomaterials are crucial in electrocatalysis, but precise synthesis control is needed to optimize performance.
Machine learning (ML) offers a transformative approach to overcome these challenges.

Purpose of the Study:

To review how ML integrates data, algorithms, and modeling for nanomaterials research.
To outline ML's role in enabling controllable synthesis and optimizing reaction conditions.
To demonstrate how ML links structural complexity to catalytic function for intelligent nanomaterial design.

Main Methods:

Data curation and algorithmic development for predictive modeling.
Reaction condition optimization and multimodal descriptor learning for synthesis control.
Interpretable learning frameworks to connect structure with catalytic performance.

Main Results:

ML provides a unified foundation for nanomaterials research and development.
ML enables data-driven synthesis optimization and autonomous experimentation.
ML facilitates the design of nanocatalysts with enhanced activity and selectivity.

Conclusions:

ML is redefining materials innovation through physics-informed models and autonomous platforms.
ML supports closed-loop, end-to-end strategies for nanocatalyst design.
ML lays the foundation for a new paradigm in intelligent nanomaterials discovery.