Dye-sensitized solar cells under ambient light powering machine learning: towards autonomous smart sensors for the internet of things-Reference-Cited by-同舟云学术

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

Published:2020 Issue:11 Volume:11 Page:2895-2906
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Michaels Hannes¹²³⁴⁵^ORCID,Rinderle Michael⁶⁷⁸⁹^ORCID,Freitag Richard¹⁰³¹¹⁵^ORCID,Benesperi Iacopo¹²³⁴⁵^ORCID,Edvinsson Tomas¹²²³¹³⁵^ORCID,Socher Richard¹⁴¹⁵¹⁶,Gagliardi Alessio⁶⁷⁸⁹,Freitag Marina¹²³⁴⁵^ORCID

Affiliation:

1. Department of Chemistry

2. Ångström Laboratory

3. Uppsala University

4. SE-75120 Uppsala

5. Sweden

6. Department of Electrical and Computer Engineering

7. Technical University of Munich

8. 80333 Munich

9. Germany

10. IT-Division

11. SE-75105 Uppsala

12. Department of Solid-state Physics

13. SE-75121 Uppsala

14. Salesforce Research

15. Palo Alto

16. USA

Abstract

Indoor light harvesters enable machine learning on fully autonomous IoT devices at 2.72 × 10¹⁵ photons per inference.

Funder

Energimyndigheten

Göran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning

Deutsche Forschungsgemeinschaft

Stiftelsen Åforsk

Vetenskapsrådet

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/SC/C9SC06145B

Reference66 articles.

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3. An improved extreme learning machine model for the prediction of human scenarios in smart homes

4. A. Jukan and M.Chamania , in Advanced Photonics 2017 (IPR, NOMA, Sensors, Networks, SPPCom, PS) , OSA , Washington, D.C. , 2017 , p. NeM2B.4

5. A. Nicosia , D.Pau , D.Giacalone , E.Plebani , A.Bosco and A.Iacchetti , 2018 IEEE Int. Conf. Consum. Electron. , 2018-Jan , pp. 1–4

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