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

Electrical Conductivity01:13

Electrical Conductivity

1.7K
In perfect conductors, the electric field inside is always zero due to the abundance of free electrons, which nullify any field by flowing. As a result, any residual charge resides on the surface.
In a practical conductor, an applied electric field may be sustained, causing a flow of electrons, which produce a current. The differential form of the current, the current density, is related to the electric field.
More generally, it is related to the force per unit charge, which involves the...
1.7K

You might also read

Related Articles

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

Sort by
Same author

One-Pot, One-Step Mn-bis(imino)pyridine Complexes through Sonochemistry.

Inorganic chemistry·2026
Same author

The clinical significance and optimization strategies of HBeAg seroclearance in chronic hepatitis B treatment.

Frontiers in cellular and infection microbiology·2026
Same author

Ion-backbone accessibility enables unity doping efficiency in organic electrochemical transistors.

Nature communications·2026
Same author

Short-Term Clinical and Metabolic Outcomes of Laparoscopic Sleeve Gastrectomy in Korean Adolescents with Severe Obesity: A Retrospective Analysis.

Obesity surgery·2026
Same author

Factors influencing residents' willingness to choose medical institutions first for treatment in the highland agricultural and pastoral areas of Qinghai province: a study based on the Anderson model.

Frontiers in health services·2026
Same author

Spin-orbit-resolved strong-field ionization from real-time relativistic dynamics.

The Journal of chemical physics·2026

Related Experiment Video

Updated: Jan 16, 2026

Using Cyclic Voltammetry, UV-Vis-NIR, and EPR Spectroelectrochemistry to Analyze Organic Compounds
11:44

Using Cyclic Voltammetry, UV-Vis-NIR, and EPR Spectroelectrochemistry to Analyze Organic Compounds

Published on: October 18, 2018

27.4K

Fusion Deep Learning for Predicting Conductivity in Electron-Doped Organic Polymers.

Ziyu Zhang1, Xinzheng Yang1, Liang Yan2

  • 1Department of Chemistry, University of Washington, Seattle, Washington 98195, United States.

Journal of the American Chemical Society
|September 26, 2025
PubMed
Summary

This study introduces a deep learning model to predict the conductivity of n-type organic semiconductors, crucial for advanced electronics. The model accurately forecasts material performance, accelerating the design of efficient organic electronic devices.

More Related Videos

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM
08:59

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM

Published on: January 23, 2013

12.1K
Electrochemical Preparation of Poly3,4-Ethylenedioxythiophene Layers on Gold Microelectrodes for Uric Acid-Sensing Applications
10:48

Electrochemical Preparation of Poly3,4-Ethylenedioxythiophene Layers on Gold Microelectrodes for Uric Acid-Sensing Applications

Published on: July 28, 2021

4.5K

Related Experiment Videos

Last Updated: Jan 16, 2026

Using Cyclic Voltammetry, UV-Vis-NIR, and EPR Spectroelectrochemistry to Analyze Organic Compounds
11:44

Using Cyclic Voltammetry, UV-Vis-NIR, and EPR Spectroelectrochemistry to Analyze Organic Compounds

Published on: October 18, 2018

27.4K
Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM
08:59

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM

Published on: January 23, 2013

12.1K
Electrochemical Preparation of Poly3,4-Ethylenedioxythiophene Layers on Gold Microelectrodes for Uric Acid-Sensing Applications
10:48

Electrochemical Preparation of Poly3,4-Ethylenedioxythiophene Layers on Gold Microelectrodes for Uric Acid-Sensing Applications

Published on: July 28, 2021

4.5K

Area of Science:

  • Materials Science
  • Organic Electronics
  • Computational Chemistry

Background:

  • Efficient and air-stable n-type organic semiconductors are vital for high-performance organic electronic devices.
  • Molecular n-doping is key to achieving desired semiconductor properties.
  • Machine learning (ML) accelerates the discovery and design of novel materials by identifying structure-property relationships.

Purpose of the Study:

  • To develop and validate a data-driven approach for predicting the conductivity of n-type organic semiconductors.
  • To construct a comprehensive database of n-type conductive polymers with experimental and computational data.
  • To design and implement a fusion deep learning model for accurate property prediction.

Main Methods:

  • A curated database of 84 n-type conductive polymers was created, including experimental n-doping data with 4-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)phenyl dimethylamine (N-DMBI-H) and density functional theory (DFT) calculations.
  • A fusion deep learning model, integrating convolutional neural networks (CNNs) and fully connected artificial neural networks (ANNs), was developed to analyze polymer structures and properties.
  • The model was trained and validated using leave-one-out cross-validation and applied to predict the conductivity of polymers with oligoethylene glycol (OEG) side chains.

Main Results:

  • The fusion deep learning model successfully identified key physical factors influencing conductivity in n-type polymers doped with N-DMBI-H.
  • A double-blind experiment validated the model's predictive capability, with conductivity predictions for N2200-type and BDPPV-type polymers agreeing with experimental values within the same order of magnitude.
  • The model demonstrated reliability in predicting the conductivity of doped n-type organic semiconductors.

Conclusions:

  • The developed fusion deep learning model is a reliable tool for predicting the conductivity of n-type organic semiconductors.
  • This data-driven approach provides a strong foundation for the rational design of high-conductivity n-type polymers.
  • The findings facilitate the advancement of durable and high-performance organic electronic devices through accelerated materials discovery.