Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things

Journal: Chemical Science

Published: 2020-03-18

DOI: 10.1039/c9sc06145b

Affiliations: 7

Authors: 8

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Research Highlight

Light option for machine learning

© Berkah/Getty

© 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.

Supported content

  1. Chemical Science 11, 2895–2906 (2020). doi: 10.1039/c9sc06145b
Institutions 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