Investigation on the use of soap films for hydrogen production

The world today is facing one of the greatest challenges in human history. Climate change and its far-reaching impacts demand urgent sustainable solutions to restore balance between human development and the natural environment. Since the early years of the third millennium, the green revolution has gained momentum, aiming to transform the way energy is produced and consumed. One widely promoted approach is the use of renewable energy sources for power generation. While this transition is fundamental in mitigating climate change, certain sectors, such as heavy industry, aviation, and construction, have energy demands that cannot be fully met by renewable sources alone. A promising complementary solution is green hydrogen, produced through water electrolysis powered by renewable energy. Its potential as a key component in de-carbonization has driven extensive research into understanding electrolysis processes under varying operating conditions. This thesis focuses on green hydrogen production in pure water–surfactant solutions, investigating how different experimental setups influence the resulting physical and chemical phenomena. The aim is to classify these phenomena to guide future experimental design, improve process efficiency, and contribute to a deeper understanding of hydrogen production through electrolysis. This work also builds on concepts from the EU-funded SoFiA (Soap Film based Artificial Photosynthesis) project, which explored soap films as functional platforms for solar fuel production. The project demonstrated that the nanometric thickness and high surface-to-volume ratio of soap films, combined with functional surfactants, can be exploited to control photo-electrochemical reactions for CO2 reduction and water oxidation. In conclusion, the primary aim of this work is not to present groundbreaking results, but rather to provide a detailed analysis and a starting point for future studies and experimental developments. Follow-up investigations are already underway, including the use of machine learning algorithms, to better understand the phenomena observed.