Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Batteries and Fuel Cells03:12

Batteries and Fuel Cells

27.4K
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...
27.4K
Electrogravimetric Analysis: Overview01:30

Electrogravimetric Analysis: Overview

230
Electrogravimetric analysis measures the weight of an analyte deposited electrolytically onto a suitable working electrode. This method involves applying a potential to a pre-weighed electrode submerged in a solution, which results in the desired substance being deposited through reduction at the cathode or oxidation at the anode. The electrode's weight is recorded after deposition, and the difference in weight gives the analyte's weight in the solution.
To test the completeness of the...
230
Voltammetry: Factors Affecting Measurements01:21

Voltammetry: Factors Affecting Measurements

157
A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...
157
DC Battery01:21

DC Battery

795
A conductor needs to be a component of a path that creates a closed loop or full circuit to have a continuous current flowing through it. A current starts to flow if an electric field is created inside an isolated conductor that is not part of a full circuit. The conductor quickly develops a net positive charge at one end and a net negative charge at the other. These charges generate an electric field opposite the direction of the applied electric field, which reduces the current. Eventually,...
795

You might also read

Related Articles

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

Sort by
Same author

Genomic signature of repeated transitions to diurnality in spiders.

Molecular biology and evolution·2026
Same author

The α1 subunit-containing GABA<sub>A</sub> receptor-mediated inhibitory transmission in the lateral orbitofrontal cortex contributes to anxiety- and depression-like behaviors in Parkinsonian rats.

Neuropharmacology·2026
Same author

The CIDR-GPG Protocol Improves Reproductive Efficiency in Yaks and Lowers the Body Condition Requirements for Success.

Animals : an open access journal from MDPI·2026
Same author

Glycolysis Dominates Over Photorespiration in Governing Oxalate Accumulation in Rice.

Plant physiology·2026
Same author

Corrigendum to "Multi-time scale dynamic effective brain networks reveal accelerated brain aging in individuals with major depressive disorder" [J. Psychiatr. Res. 196 (2026) 306-313].

Journal of psychiatric research·2026
Same author

External field-enhanced membrane demulsification: Dynamic interfacial behaviors and separation mechanisms.

Advances in colloid and interface science·2026
Same journal

Optimizing the Development Process in Direct Photolithography for Efficient PeLEDs.

Small methods·2026
Same journal

Fluorinated Diluents Enable Crowded Solvation Environments to Form Anion-Rich SEIs for High-Performance Potassium-Ion Batteries.

Small methods·2026
Same journal

Delocalized Redox Framework of Indanthrone Enables Low-Strain and Durable Mn<sup>2+</sup>/H<sup>+</sup> Storage in Aqueous Batteries.

Small methods·2026
Same journal

Sandgrouse Feather-Inspired Multiscale Hierarchical Microstructured Surfaces via IICSA for Controlled Liquid Regulation.

Small methods·2026
Same journal

Smart Antibacterial Janus Fabric Based on PVDF/Ag-Decorated-MXene for Unidirectional Water Transport and Thermal Management.

Small methods·2026
Same journal

Synergistic Anion Confinement in a Poly(Ionic Liquid)/MOF Composite Electrolyte Decouples Ionic Conductivity and Mechanical Strength for High-Performance Solid-State Lithium Metal Batteries.

Small methods·2026
See all related articles

Related Experiment Video

Updated: Jul 5, 2025

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries
11:25

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries

Published on: November 10, 2014

15.8K

Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects.

Shanling Ji1, Jianxiong Zhu1,2, Yaxin Yang1

  • 1School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China.

Small Methods
|January 12, 2024
PubMed
Summary
This summary is machine-generated.

Data-driven artificial intelligence enhances battery characterization and prognosis for safer operation. This review highlights advanced methods and the role of interpretable learning in accelerating next-generation battery development.

Keywords:
battery characterizationbattery prognosisdata‐driven methodsexplainable artificial intelligencephysics‐informed learning

More Related Videos

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
07:55

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Published on: April 17, 2018

12.7K
Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries
10:41

Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries

Published on: May 22, 2018

36.9K

Related Experiment Videos

Last Updated: Jul 5, 2025

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries
11:25

In Situ Neutron Powder Diffraction Using Custom-made Lithium-ion Batteries

Published on: November 10, 2014

15.8K
Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
07:55

Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering

Published on: April 17, 2018

12.7K
Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries
10:41

Three-electrode Coin Cell Preparation and Electrodeposition Analytics for Lithium-ion Batteries

Published on: May 22, 2018

36.9K

Area of Science:

  • Materials Science
  • Electrochemistry
  • Artificial Intelligence

Background:

  • Battery characterization and prognosis are crucial for understanding electrochemical mechanisms and ensuring operational safety.
  • Data-driven artificial intelligence (AI) systems offer advanced capabilities for battery analysis.
  • Recent progress has focused on integrating AI with various characterization techniques.

Purpose of the Study:

  • To provide a comprehensive review of data-driven methods for battery characterization and prognosis.
  • To highlight the role of physics-informed interpretable learning in accelerating energy device development.
  • To discuss challenges and future prospects in the field.

Main Methods:

  • Summarizing recent advances in informative image characterization and impedance spectroscopy.
  • Reviewing high-throughput screening approaches for revealing electrochemical mechanisms.
  • Comparing various physics-informed modeling strategies for battery prognosis.

Main Results:

  • Data-driven AI significantly enhances the ability to characterize and predict battery performance.
  • Physics-informed interpretable learning is key to unlocking insights from large battery datasets.
  • Advanced methods improve electrochemical transparency and generalization in battery models.

Conclusions:

  • Data-driven approaches, particularly physics-informed interpretable learning, are vital for next-generation battery development.
  • Enhanced electrochemical transparency and generalization are achievable through these advanced methods.
  • This review offers insights for future research in battery technology.