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 and Liangti Qu ()
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Ce Yang: Tsinghua University
Haiyan Wang: Tsinghua University
Jiaxin Bai: Tsinghua University
Tiancheng He: Tsinghua University
Huhu Cheng: Tsinghua University
Tianlei Guang: Tsinghua University
Houze Yao: Tsinghua University
Liangti Qu: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract Harvesting energy from spontaneous water flow within artificial nanochannels is a promising route to meet sustainable power requirements of the fast-growing human society. However, large-scale nanochannel integration and the multi-parameter coupling restrictive influence on electric generation are still big challenges for macroscale applications. In this regard, long-range (1 to 20 cm) ordered graphene oxide assembled framework with integrated 2D nanochannels have been fabricated by a rotational freeze-casting method. The structure can promote spontaneous absorption and directional transmission of water inside the channels to generate considerable electric energy. A transfer learning strategy is implemented to address the complicated multi-parameters coupling problem under limited experimental data, which provides highly accurate performance optimization and efficiently guides the design of 2D water flow enabled generators. A generator unit can produce ~2.9 V voltage or ~16.8 μA current in a controllable manner. High electric output of ~12 V or ~83 μA is realized by connecting several devices in series or parallel. Different water enabled electricity generation systems have been developed to directly power commercial electronics like LED arrays and display screens, demonstrating the material’s potential for development of water enabled clean energy.

Date: 2022
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DOI: 10.1038/s41467-022-34496-y

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