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Framework combining microkinetic modelling with reactor physics to characterise heterogeneous catalyst surfaces during reaction, reducing reliance on expensive analytical tools
About
Our innovative computational framework revolutionises how we understand catalyst behaviour under real operating conditions. Current methods for studying catalyst surfaces during reactions often require expensive, specialised analytical equipment and facilities, creating a significant barrier for many researchers and organisations. Our solution combines cutting-edge microkinetic modelling with reactor multiphysics in an accessible Python library, enabling researchers to predict surface species abundances and reaction pathways without relying on costly operando analytical techniques. This framework not only reduces the need for expensive experimental measurements but also accelerates catalyst development by enabling rapid screening of materials and reaction conditions. Perfect for both academic and industrial researchers, our tool democratises advanced catalyst characterisation whilst minimising resource consumption and environmental impact. The framework's modular design and open-source nature ensure it can be adapted for different reaction systems and scales, making sophisticated catalyst analysis accessible to the broader research community.
Key Benefits
* Significant Cost Reduction: Minimises the need for expensive operando analytical equipment and specialised facilities, making advanced catalyst characterisation accessible to more research groups and companies
* Accelerated Development Cycle: Enables rapid screening of materials and reaction conditions through computational predictions, dramatically reducing the time and resources needed for catalyst optimisation
* Enhanced Understanding: Provides detailed insights into surface species abundances and reaction pathways under realistic operating conditions, offering information that may be challenging or impossible to measure directly
* Broad Applicability: The modular, open-source framework can be adapted to different reaction systems and scales, making it valuable for both academic research and industrial applications
* Efficient Resource Use: Intelligent design of experiments feature minimises the number of experimental measurements needed, reducing material consumption and environmental impact
* Future-Ready Platform: Built to enable integration with machine learning approaches, laying the groundwork for next-generation catalyst and process optimisation
Applications
Primary Applications:
* Academic Research Laboratories: Particularly those focused on heterogeneous catalysis but lacking access to expensive operando analytical facilities
* Industrial R&D Departments: Companies developing or optimising catalytic processes who want to reduce development costs and time-to-market
* Catalyst Manufacturing Companies: Organisations seeking to understand and optimise their catalyst performance under real operating conditions
Specific Sectors:
* Chemical Manufacturing: For process optimisation and catalyst development
* Sustainable Energy: Particularly relevant for hydrogen production (demonstrated through ammonia decomposition case study)
* Environmental Technology: Companies working on catalytic solutions for emissions control and green chemistry
* Materials Science: Research groups studying surface chemistry and materials characterisation
Target Users:
* Catalysis Researchers
* Process Engineers
* Material Scientists
* Chemical Engineers
* Industrial R&D Teams
* Academic Research Groups
* Environmental Technology Developers