A workflow for modeling radiolysis in chemically, physically, and geometrically complex scenarios

Area of Science:

• Materials Science
• Computational Chemistry
• Radiation Physics

Background:

• Radiation-based techniques are vital for nanoscale materials research.
• Radiation-induced damage, especially radiolysis in liquids, often hinders these characterizations.
• Accurate modeling of radiation chemistry is essential but often overlooked.

Purpose of the Study:

• To introduce a comprehensive workflow for numerically modeling radiolysis reaction kinetics.
• To address chemically, physically, and geometrically complex scenarios.
• To improve the interpretation of experimental observations in radiation-based characterization.

Main Methods:

• Developed a Python-based environment (AuRaCh tool) for automatic reaction network composition.
• Integrated reaction networks into finite element software (COMSOL Multiphysics) for expansion.
• Analyzed model complexity and explored simplifications using characteristic numbers.

Main Results:

• Demonstrated workflow applicability in liquid-phase electron microscopy.
• Extended applicability to other fields with complex reaction networks.
• Scrutinized model complexity for experimentally relevant parameter regimes.

Conclusions:

• The workflow enhances computational modeling of radiation chemistry.
• Improves correlative experimental methods in nanoscale research.
• Promotes cross-community approaches for better understanding of radiation effects.