Published By : 15 Sep 2017 | Published By : QYRESEARCH
As industry players and governments in various parts of the world are gearing toward making transition from fossil fuels to low-carbon and green infrastructure, they are looking to leverage the potential of renewable fuels. Over past few decades, this has led to several technological developments in harnessing the power of hydrogen fuels, currently being produced using photoelectrolysis of water molecules. Despite vast advances made, producing commercially cost-effective renewable fuels with reasonable efficiencies remains far-fetched dream, unarguably. A recent research done by scientists at Lancaster University, U.K., hopes to solve this fundamental problem. Physicists at the University have discovered a novel method based on nanotechnology to significantly improve photoelectrolysis to produce commercially viable renewable fuel from water.
The research that forms part of a larger study on methods of solar production of hydrogen as a renewable fuel is published in open access journal Scientific Reports.
Quantum Technology to Markedly Increase Maximum Photovoltage in Electrochemical Cells
Current methods of producing hydrogen fuel relies on utilizing solar power to split water molecules into oxygen and the fuel hydrogen. However the photo-voltage generated in photo electrochemical cells (PECs) employed for the process is too low for it to be commercially useful. The researchers harnessed the power of quantum technology to increase the maximum photovoltage generated in a PEC. To this end, the investigators proposed a novel method based on semiconductor nanostructures. Theoretically, this is achieved by making type-II heterojunction at the semiconductor–electrolyte interface. This was found to improve the efficiency of PECs significantly.
The researchers contend that such a study has never been performed experimentally before; neither, the scope of such an experiment has been investigate theoretically. They hope that the results of their study should form the basis for further similar experiments in the foreseeable future, with an aim to consistently improve the implementation of the model.