Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels

Ce Yang; Haiyan Wang; Jiaxin Bai; Tiancheng He; Huhu Cheng; Tianlei Guang; Houze Yao; Liangti Qu

doi:10.1038/s41467-022-34496-y

Nature Communications (Nov 2022)

Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels

Ce Yang,
Haiyan Wang,
Jiaxin Bai,
Tiancheng He,
Huhu Cheng,
Tianlei Guang,
Houze Yao,
Liangti Qu

Affiliations

Ce Yang: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Haiyan Wang: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Jiaxin Bai: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Tiancheng He: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Huhu Cheng: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Tianlei Guang: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Houze Yao: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University
Liangti Qu: Key Laboratory of Organic Optoelectronics & Molecular Engineering, Ministry of Education, Department of Chemistry, State Key Laboratory of Tribology in Advanced Equipment (SKLT), Laboratory of Flexible Electronics Technology, Tsinghua University

DOI: https://doi.org/10.1038/s41467-022-34496-y
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

Read online

Large-scale nanochannel integration and the multi-parameter coupling restrictive influence on electric generation are big challenges for effective energy harvesting from spontaneous water flow within artificial nanochannels. Here, authors apply transfer learning to overcome these and design optimized water-enabled generators.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

About the journal