These solar cells could revolutionise the way IoT devices are powered
20 April 2023
New high-efficiency solar cells, developed by researchers at Newcastle University, can power the Internet of Things devices with ambient light.
Led by Dr Marina Freitag, the research group from the School of Natural and Environmental Sciences created dye-sensitised photovoltaic cells based on a copper(II/I) electrolyte, achieving an unprecedented power conversion efficiency of 38 percent and 1.0V open-circuit voltage at 1,000 lux (fluorescent lamp).
The cells are non-toxic and environmentally friendly, setting a new standard for sustainable energy sources in ambient environments.
Published in the journal Chemical Science, the research has the potential to revolutionise the way IoT devices are powered, making them more sustainable and efficient, and opening up new opportunities in industries such as healthcare, manufacturing, and smart city development.
Dr Marina Freitag, Principal Investigator at the School of Natural and Environmental Sciences, Newcastle University, said: "Our research marks an important step towards making IoT devices more sustainable and energy-efficient.
“By combining innovative photovoltaic cells with intelligent energy management techniques, we are paving the way for a multitude of new device implementations that will have far-reaching applications in various industries.”
The team also introduced a pioneering energy management technique, employing long short-term memory (LSTM) artificial neural networks to predict changing deployment environments and adapt the computational load of IoT sensors accordingly.
This dynamic energy management system enables the energy-harvesting circuit to operate at optimal efficiency, minimising power losses or brownouts.
This breakthrough study demonstrates how the synergy of artificial intelligence and ambient light as a power source can enable the next generation of IoT devices.
The energy-efficient IoT sensors, powered by high-efficiency ambient photovoltaic cells, can dynamically adapt their energy usage based on LSTM predictions, resulting in significant energy savings and reduced network communication requirements.