Journal: Chemical Science
Affiliations: 7Go to article
Light option for machine learning
Highly efficient solar cells optimized for harvesting ambient light in indoor environments could power a smart Internet of Things (IoT) network.
A team that included Technical University of Munich scientists has developed cells that capture energy from fluorescence lights with efficiencies of up to 34%. They achieved this by developing a dye-sensitized photovoltaic cell tailor made for harvesting the wavelengths of light available indoors and by suppressing energy losses from electron-back transfer in the cell.
The cells could capture enough energy to power autonomous IoT devices with artificial-intelligence capabilities. A wireless network of these IoT devices could use intermittently available ambient light to power an artificial neural network that demonstrated machine learning, a type of computation usually carried out on large servers.
Photovoltaic cells attuned to capturing ambient light could power a new generation of smart IoT devices, the researchers conclude.
- Chemical Science 11, 2895–2906 (2020). doi: 10.1039/c9sc06145b
|Uppsala University (UU), Sweden||0.56|
|Technical University of Munich (TUM), Germany||0.25|
|Salesforce.com, Inc., United States of America (USA)||0.13|
|Newcastle University, United Kingdom (UK)||0.06|