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

The Electrical Double Layer01:30

The Electrical Double Layer

241
In the region where two bulk phases meet, an intricate electric charge distribution arises due to charge transfer, ion adsorption, molecular orientation, and charge distortion. This complex distribution is commonly referred to as the electrical double layer.When a solid electrode interfaces with ions in an electrolyte solution, the speed of electron transfer dictates the rates of oxidation and reduction. The electrode acquires a charge through the escape of atoms into the solution as cations or...
241

You might also read

Related Articles

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

Sort by
Same author

High-performance activated carbons from <i>Canarium schweinfurthii</i> and <i>Ricinodendron heudelotii</i> shells for the efficient removal of indigo carmine from water.

RSC advances·2026
Same author

Siderophore-Producing Bacteria from the Santiago River: A Quantitative Study and Biocomposite Applications.

Microorganisms·2026
Same author

Carbon paste electrode-based electroanalytical method for electrochemical fingerprinting of flavored agave syrups.

Food chemistry·2026
Same author

Early-Stage Electrochemical Kinetics of Agave Distillates: Impact of Barrel Toasting on Polyphenol Extraction Dynamics.

Foods (Basel, Switzerland)·2026
Same author

Insights into Structure-Properties Relationship of Cationic Copolymers with Quaternary Ammonium Moieties via RAFT Polymerization as Potential Binders for Lithium-Sulfur Batteries.

Macromolecular rapid communications·2025
Same author

Use of Artificial Neural Networks for Recycled Pellets Identification: Polypropylene-Based Composites.

Polymers·2025

Related Experiment Video

Updated: May 1, 2026

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
10:03

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Published on: November 11, 2013

25.4K

Predicting Sodium-Ion Battery Performance through Surface Chemistry Analysis and Textural Properties of

Walter M Warren-Vega1, Ana I Zárate-Guzmán1, Francisco Carrasco-Marín2

  • 1Grupo de Investigación en Materiales y Fenómenos de Superficie, Departamento de Biotecnológicas y Ambientales, Universidad Autónoma de Guadalajara, Av. Patria 1201, C.P., Zapopan 45129, Mexico.

Materials (Basel, Switzerland)
|September 14, 2024
PubMed
Summary

Machine learning accurately predicts sodium-ion battery performance using multiple material properties. This approach optimizes functionalized hard carbon anodes derived from grapefruit peels for better energy storage.

Keywords:
MATLAB-Simulinkartificial intelligenceartificial neural networkenergy storage mechanismmachine 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
Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy
07:20

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Published on: January 20, 2023

2.5K

Related Experiment Videos

Last Updated: May 1, 2026

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
10:03

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Published on: November 11, 2013

25.4K
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
Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy
07:20

Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy

Published on: January 20, 2023

2.5K

Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Sodium-ion battery performance prediction traditionally relied on single electrode characteristics, which is now understood to be insufficient.
  • Battery performance is influenced by a complex interplay of multiple physical and chemical variables, necessitating advanced predictive models.

Purpose of the Study:

  • To introduce machine learning as an innovative strategy for predicting the performance of functionalized hard carbon anodes.
  • To utilize grapefruit peel-derived materials for sustainable anode development in sodium-ion batteries.

Main Methods:

  • Development of a three-layer feed-forward Artificial Neural Network (ANN) using Bayesian regularization.
  • Input features included physicochemical properties (porosity, elemental analysis, Raman ID/IG ratio, XPS data), cycle number, and C-rate.
  • ANN architecture comprised sigmoid and log-sigmoid transfer functions across layers with 10 neurons each.

Main Results:

  • The proposed ANN model achieved high prediction accuracy, with R2 values exceeding 0.99 for all materials tested.
  • The model effectively correlated diverse material characteristics with battery performance outcomes.

Conclusions:

  • Machine learning, specifically ANNs, offers a powerful and accurate method for predicting sodium-ion battery performance.
  • This strategy provides crucial insights for optimizing material synthesis and accelerating the development of tailored battery components.