A5010504318- CO2 Reduction Techniques and Catalysts
- Ionic liquids properties and applications
- Advanced Thermoelectric Materials and Devices
- Neural dynamics and brain function
- Electrocatalysts for Energy Conversion
- Advanced Memory and Neural Computing
- Neural Networks and Reservoir Computing
- Carbon Dioxide Capture Technologies
- Advanced battery technologies research
- Catalysis and Oxidation Reactions
Polytechnic University of Turin
2022-2025
Center for Sustainable Future Technologies
2023-2025
Italian Institute of Technology
2023-2025
Electrochemical processes have emerged as intriguing strategies for both CO2 capture and valorization, which are needed to combat global warming climate change. Among other advantages over competing technologies, electrochemical systems can be powered by renewable sources, including solar energy. This review aims at collecting analyzing the main works proposed in literature that study coupling of reactors conversion into carbon monoxide with 1) or 2) cells power them. In addition critical...
With the rising levels of atmospheric CO2, electrochemistry shows great promise in decarbonizing industrial processes by converting CO2 into valuable products through scalable and sustainable technologies. In this framework, present study investigates solar-driven reduction toward carbon monoxide, achieved integration between electrochemical reactor dye-sensitized solar cells (DSSCs), both experimental modeling perspectives. COMSOL® Multiphysics 6.3 was used to develop a detailed finite...
Abstract In the research for decarbonization processes, electrochemistry is among most studied routes conversion of carbon dioxide in added-value products, thanks to up-scalability and mild conditions work technology. this framework, modeling electrochemical reactor a powerful tool predict optimize important features electroreduction. study, we propose comprehensive whole reactor, which has been validated through experiments with good agreement. particular, performance cell as function...
The hardware implementation of the reservoir computing paradigm represents a key aspect for taking into advantage neuromorphic data processing. In this context, self-organised nanonetworks represent versatile and scalable computational substrate multiple tasks by exploiting emerging collective behaviour system arising from complexity. allows spatio-temporal processing input signals relies on nonlinear interaction in between multitude nanoscale memristive elements. By means physics-based...