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

Voltammetry: Stripping Methods01:13

Voltammetry: Stripping Methods

177
Anodic Stripping Voltammetry (ASV), Cathodic Stripping Voltammetry (CSV), and Adsorptive Stripping Voltammetry (AdSV) are electrochemical techniques used to determine trace amounts of analytes in solution. These methods involve applying a potential to an electrode and measuring the resulting current.
Anodic Stripping Voltammetry (ASV)
ASV is used to determine metals and metalloids at trace levels. It involves two steps: deposition and stripping. First, a negative potential is applied to the...
177
Voltammetry: Overview01:20

Voltammetry: Overview

1.2K
Voltammetry is an electroanalytical technique in which the current flowing through an electrochemical cell is measured as a function of applied potential, typically under conditions of concentration polarization. The technique provides valuable information about redox-active species, and the current response is plotted as a voltammogram.
A voltammetric cell uses three electrodes: a working electrode, a reference electrode, and an auxiliary electrode. The redox reactions occur in the working...
1.2K
Electrogravimetric Analysis: Overview01:30

Electrogravimetric Analysis: Overview

202
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...
202
Electrodeposition01:08

Electrodeposition

597
Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...
597
Voltammetric Techniques: Cyclic Voltammetry01:10

Voltammetric Techniques: Cyclic Voltammetry

368
Cyclic voltammetry (CV) is an electrochemical technique used to investigate the redox properties of a chemical species. It involves measuring the current response of an electrochemical cell as a function of the applied potential. The setup for cyclic voltammetry typically consists of a working electrode, a reference electrode, and a counter electrode—all immersed in an electrolyte solution. The working electrode is where the redox reaction of interest occurs, while the reference electrode...
368
Controlled-Potential Coulometry: Electrolytic Methods01:17

Controlled-Potential Coulometry: Electrolytic Methods

136
Controlled-potential coulometry, also known as potentiostatic coulometry, employs a three-electrode system in which the working electrode's potential is precisely regulated using a potentiostat. Platinum working electrodes are utilized for positive potentials, while mercury pool electrodes are favored for extremely negative potentials. The platinum counter electrode is separated from the analyte using a membrane or salt bridge to avoid interference in the analysis.
The chosen potential...
136

You might also read

Related Articles

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

Sort by
Same author

First High-Throughput Evaluation of Dark Matter Detector Materials.

Physical review letters·2026
Same author

Ion correlations explain kinetic selectivity in diffusion-limited solid-state synthesis reactions.

Nature materials·2026
Same author

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks.

Journal of the American Chemical Society·2026
Same author

Generative Models for Crystalline Materials.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Li<sup>+</sup>/H<sup>+</sup> Exchange in Solid-State Oxide Li-Ion Conductors.

ACS energy letters·2026
Same author

Microscopic Mechanisms of Superionic Na-ion Conductivity in Crystalline and Amorphous NaMOCl<sub>4</sub> (M = Nb, Ta) Solid Electrolytes.

ACS energy letters·2026

Related Experiment Video

Updated: Jun 5, 2025

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

36.6K

Voltage Mining for (De)lithiation-Stabilized Cathodes and a Machine Learning Model for Li-Ion Cathode Voltage.

Haoming Howard Li1, Qian Chen2, Gerbrand Ceder1,2

  • 1Department of Material Science and Engineering, University of California, Berkeley, California 94720, United States.

ACS Applied Materials & Interfaces
|December 9, 2024
PubMed
Summary

Researchers explored Li-free cathode materials for advanced batteries. They identified design principles for high-voltage cathodes and developed a machine learning model for accurate voltage prediction, advancing battery material discovery.

Keywords:
Li-ion batteriesbattery voltagecathodesdata miningmachine learningmaterials design

More Related Videos

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.5K
Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells
12:28

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Published on: February 1, 2016

21.5K

Related Experiment Videos

Last Updated: Jun 5, 2025

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

36.6K
Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway
11:25

Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

Published on: March 7, 2022

4.5K
Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells
12:28

Non-aqueous Electrode Processing and Construction of Lithium-ion Coin Cells

Published on: February 1, 2016

21.5K

Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Lithium-metal anodes drive interest in Li-free cathode discovery.
  • Commercial lithium-ion battery cathodes are stable in discharged states, unlike many Li-free candidates found in charged states.

Purpose of the Study:

  • To analyze voltage distributions for Li-free and commercial cathode materials.
  • To establish design principles for high-voltage cathode discovery.
  • To develop and validate a machine learning model for cathode voltage prediction.

Main Methods:

  • Calculated cathode voltage data for 5577 charged-state and 2423 discharged-state unique structure pairs.
  • Analysis of voltage distributions based on redox pairs and anion types.
  • Training a machine learning model using chemical formulas for voltage prediction.

Main Results:

  • High-voltage cathodes favor later Period 4 transition metals and electronegative anions (e.g., fluorine, polyanions).
  • Charged-state cathodes generally exhibit lower voltages than lithiated counterparts, with deviations linked to anion distribution.
  • The developed machine learning model achieved state-of-the-art performance compared to Roost and CrabNet.

Conclusions:

  • Key design principles for high-voltage Li-free cathodes are identified.
  • Machine learning offers a powerful tool for predicting cathode voltages and accelerating materials discovery.
  • Understanding anion distribution is crucial for predicting cathode voltage behavior.