Moisture-driven, self-powered noncontact sensing interfaces via turbulence-tailored hygroelectronic effect.

Daozhi Shen, Haotian Luo, Gangli Zhao, Zechao Han, Zhengsheng Yang, Xinyi Le, Yanjie Su, Runze Ma, Limin Zhu

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Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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5 · Who and what money

Authors and funding

9 authors.

Daozhi ShenSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-5426-4357

Haotian LuoSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0009-0005-2522-9201

Gangli ZhaoSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Zechao HanSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0009-0002-7873-5296

Zhengsheng YangSchool of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China.ORCID 0000-0001-5310-7408

Xinyi LeDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.

Yanjie SuNational Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0003-2193-5473

Runze MaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0002-0337-5065

Limin ZhuSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.ORCID 0000-0003-3194-6731

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Noncontact human-machine interfaces (HMIs) provide a hygienic and intelligent approach for the communication between human and robots. However, they are limited by the interaction distance and bulky power supply. Here, we introduce a self-powered, noncontact intelligent sensing interface based on moisture-driven electricity generation and machine learning technique. We demonstrate that a hydrogel doped with ions exhibits strong hygroelectronic behavior and generates sustainable voltage up to ~0.6 volts from ambient air. The motion of a human hand creates localized air turbulence, resulting in changes to humidity and air pressure that tailor the electrical output. By using machine learning models to decode the motion-dependent voltage, our system achieves high gesture recognition accuracy of up to 99% for Arabic numerals, with an impressive interaction distance up to ~8 centimeters. The proposed system is demonstrated in applications such as encrypted information transmission, virtual reality gaming, and real-time vehicle control.

Identifiers

PMID41996498

DOI10.1126/sciadv.aee7050

PMCPMC13089349

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.