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

P-N junction01:11

P-N junction

A p-n junction is formed when p-type and n-type semiconductor materials are joined together. At the interface of the p-n junction, holes from the p-side and electrons from the n-side begin to diffuse into the opposite sides due to the concentration gradient. This diffusion of carriers leads to a region around the junction where there are no free charge carriers, known as the depletion region. The charge density within the depletion region for the n-side and p-side can be described by the...

You might also read

Related Articles

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

Sort by
Same author

Axially coordinated cobalt single-atom membrane enabling electron-transfer-singlet oxygen synergy for highly selective micropollutant oxidation.

Journal of hazardous materials·2026
Same author

Dual-functional acetogenin nanofibers: bridging biomedical activity with brain-inspired neuromorphic devices.

Scientific reports·2026
Same author

Insight into the Impact of Wide Bandgap Transparent Conducting Oxide on the Performance of Thin Film Solar Cells.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

High Polarity Doping of CoFe Layered Hydroxides: Bifunctional and Corrosion-Resistant Anion Exchange Membrane Seawater Electrolyzers.

Nano-micro letters·2026
Same author

Crystallization-Driven Stable Resistive Switching and Reproducible Synaptic Learning in GeSe-Based Artificial Synapses.

ACS applied materials & interfaces·2026
Same author

Janus Reaction-Zone Catalytic Membrane for Sequential and Selective Degradation of Multiple Micropollutants: Performance and Mechanistic Insights.

Environmental science & technology·2026

Related Experiment Video

Updated: May 13, 2026

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO
08:14

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO

Published on: July 31, 2016

12.4K

Machine Learning Drives a Path to Defect Engineering for Suppressing Nonradiative Recombination Losses in

Vijay C Karade1,2, Kuldeep Singh Gour3,4, Mingrui He5

  • 1Department of Materials Science and Engineering, and Optoelectronics Convergence Research Center, Chonnam National University, Gwangju 61186, Republic of Korea.

ACS Applied Materials & Interfaces
|June 6, 2025
PubMed
Summary

Machine learning optimized germanium (Ge) incorporation in copper zinc tin sulfide selenide (CZTSSe) solar cells. This strategy reduced defects and improved device performance by over 20%, achieving 11.32% efficiency.

Keywords:
CZTSSeincorporationkesteritemachine learningsolar cell

More Related Videos

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells
09:19

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells

Published on: October 3, 2018

8.5K
Close-Space Sublimation-Deposited Ultra-Thin CdSeTe/CdTe Solar Cells for Enhanced Short-Circuit Current Density and Photoluminescence
12:21

Close-Space Sublimation-Deposited Ultra-Thin CdSeTe/CdTe Solar Cells for Enhanced Short-Circuit Current Density and Photoluminescence

Published on: March 6, 2020

8.4K

Related Experiment Videos

Last Updated: May 13, 2026

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO
08:14

Improved Heterojunction Quality in Cu2O-based Solar Cells Through the Optimization of Atmospheric Pressure Spatial Atomic Layer Deposited Zn1-xMgxO

Published on: July 31, 2016

12.4K
In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells
09:19

In Situ Monitoring of the Accelerated Performance Degradation of Solar Cells and Modules: A Case Study for CuIn,GaSe2 Solar Cells

Published on: October 3, 2018

8.5K
Close-Space Sublimation-Deposited Ultra-Thin CdSeTe/CdTe Solar Cells for Enhanced Short-Circuit Current Density and Photoluminescence
12:21

Close-Space Sublimation-Deposited Ultra-Thin CdSeTe/CdTe Solar Cells for Enhanced Short-Circuit Current Density and Photoluminescence

Published on: March 6, 2020

8.4K

Area of Science:

  • Materials Science
  • Renewable Energy
  • Semiconductor Physics

Background:

  • Kesterite materials, specifically copper zinc tin sulfide selenide (CZTSSe), show promise for optoelectronic applications.
  • Defects in CZTSSe hinder solar cell performance by increasing carrier recombination.
  • Optimizing CZTSSe requires strategies to mitigate bulk and interface defects.

Purpose of the Study:

  • To enhance copper zinc tin sulfide selenide (CZTSSe) solar cell performance using a machine learning-guided approach.
  • To optimize germanium (Ge) incorporation for improved device efficiency and reduced open-circuit voltage loss.
  • To investigate the role of defect engineering in achieving high-quality CZTSSe devices.

Main Methods:

  • A machine learning (ML) model was developed to predict optimal germanium (Ge) concentrations for CZTSSe.
  • Germanium (Ge) was incorporated into CZTSSe absorber layers to reduce defects.
  • Silver (Ag) was introduced to passivate copper (Cu)-related defects.

Main Results:

  • The ML model identified <5% Ge concentration as optimal for CZTSSe performance.
  • Ge incorporation suppressed deep-level defects, improving carrier separation and minority carrier lifetime.
  • The champion CZTSSe solar cell with Ag and Ge incorporation achieved a 11.32% power conversion efficiency, a significant increase from 9.11%.

Conclusions:

  • Machine learning-guided Ge incorporation is an effective strategy for enhancing CZTSSe solar cell performance.
  • Defect passivation through Ge and Ag incorporation significantly reduces nonradiative recombination losses.
  • This work demonstrates a pathway to improved kesterite solar cell efficiency through targeted material engineering.