Framework for processing operando neutron radiography of energy devices.

Area of Science:

• Materials Science
• Electrochemistry
• Imaging Techniques

Background:

• Neutron radiography is crucial for operando studies of electrochemical devices like fuel cells and batteries.
• Analyzing time-series neutron images is difficult due to resolution needs, low neutron flux, and signal-to-noise issues.
• Current image processing platforms are insufficient, creating bottlenecks in data analysis.

Purpose of the Study:

• To present NeuRED, a novel Python-based framework for processing neutron radiography data from electrochemical devices.
• To provide a user-friendly, transparent, and reproducible workflow for complex image analysis.
• To overcome limitations of existing platforms and accelerate operando imaging research.

Main Methods:

• Development of a Python framework (NeuRED) integrating advanced image processing functions.
• Implementation of a transparent, reproducible, and user-friendly workflow.
• Demonstration of the framework's capabilities using proton exchange membrane fuel cells, Li-ion batteries, and gas-liquid systems.

Main Results:

• NeuRED successfully processes time-series neutron images, mitigating challenges like low signal-to-noise ratios.
• The framework demonstrates versatility across various electrochemical systems and gas-liquid interfaces.
• Provides a robust solution for data analysis bottlenecks in operando neutron radiography.

Conclusions:

• NeuRED is a valuable open-access software tool for the electrochemistry community.
• The framework enhances the efficiency and reliability of operando imaging applications in energy research.
• Facilitates advancements in understanding and developing electrochemical energy devices.