(Nanowerk News) Newcastle University researchers have created an environmentally friendly, high-efficiency photovoltaic cell that harnesses ambient light to power Internet of Things (IoT) devices.
Led by Dr Marina Freitag, a research group from the School of Natural and Environmental Sciences (SNES) created a dye-sensitive photovoltaic cell based on a copper(II/I) electrolyte, achieving an unprecedented power conversion efficiency of 38% and 1.0V of circuit voltage. open at 1,000 lux (fluorescent lamp). The cells are non-toxic and environmentally friendly, setting a new standard for sustainable energy sources in the environment.
Published in a journal Chemistry (“Emerging Indoor Photovoltaics for Self-Employed and Self-aware IoT towards Sustainable Energy Management”), this research has the potential to revolutionize the way IoT devices are enabled, 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 SNES, University of Newcastle, 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 many new device implementations that will have wide-reaching applications in various industries.”
The team also introduced a pioneering energy management technique, using long-term memory (LSTM) neural networks to predict changes in the deployment environment and adapt the computing load of IoT sensors. This dynamic energy management system allows energy harvesting circuits to operate at optimal efficiency, minimizing power loss or power outages.
This groundbreaking study shows how the synergy of artificial intelligence and ambient light as a power source can enable the next generation of IoT devices. Energy-efficient IoT sensors, powered by high-efficiency ambient photovoltaic cells, can dynamically adjust their energy usage based on LSTM predictions, resulting in significant energy savings and reduced network communication requirements.