A powerful platform for engineering sustainability in catalytic and electrochemical energy conversion processes can be offered by atomically designed materials, in specific by single/dual atom catalysts [SAC]. Such materials are well known for maximized atomic utilization and precise control of active sites, which reduces the dependence on scarce or critical metals but still deliver with high activity, selectivity, and stability under operating conditions relevant to sustainable technologies. The focus of the symposium will be on the design, synthesis, and evaluation (using imaging and/or spectroscopic techniques) of SAC for catalytic and electrochemical applications, including electrocatalytic transformations. Strategies that link atomic-scale engineering to sustainability outcomes, such as earth-abundant metal systems, low-energy and scalable synthesis routes, stability under realistic electrochemical environments, and catalyst recyclability will be emphasized here. The emerging efforts to integrate life-cycle assessment, techno-economic analysis, and systems-level considerations into the development of SACs that bridges laboratory-scale advances with real-world deployment will also be highlighted in the symposium. This symposium will aim to define pathways for translating atomic precision into measurable and scalable sustainability impact, by bringing together researchers across catalysis, electrochemistry, and sustainable materials engineering
- In situ / operando studies to understand material behaviour in real-time operating conditions
- Study of synthesis mechanisms, material selectivity and stability
- In-depth investigations on metal support interactions
- Life cycle assessment and/or Lab-to-Fab realization
- New methodologies/strategies in single atom material synthesis
- Advances in characterization techniques to confirm presence of single atom materials
- Single atom catalysts in photocatalysis, photoelectrocatalysis, photothermal catalysis, CO2 reduction, energy storag
- DFT studies on single atom materials
- Machine learning approaches and accelerated single atom materials discovery

