Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things
- Journal:
- Chemical Science
- Published:
- DOI:
- 10.1039/c9sc06145b
- Affiliations:
- 7
- Authors:
- 8
Research Highlight
Light option for machine learning
© Berkah/Getty
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.
References
- Chemical Science 11, 2895–2906 (2020). doi: 10.1039/c9sc06145b
Institutions | Authors | Share |
---|---|---|
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 |