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Nature-inspired solid-state proton diode membrane for high-performance force-electric conversion
Dandan Lei1,2,3, Qixiang Zhang2,4, Yixiang Wang1,2
1School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui, China.
Nature Communications
|February 24, 2026
Summary
Researchers developed a bioinspired solid-state membrane using graphene oxide and bacterial cellulose for ultrafast proton transport. This innovation enables highly rectified proton flow, paving the way for advanced energy and sensing applications.
Area of Science:
- Materials Science
- Nanotechnology
- Bioinspired Engineering
Background:
- Bioinspired ion channel membranes are vital for energy and sensing but face challenges with proton control and liquid electrolytes.
- Developing solid-state alternatives with efficient and directional ion transport is a key research area.
Purpose of the Study:
- To create a bioinspired solid-state diode membrane with ultrafast and highly rectified proton transport.
- To overcome limitations of liquid electrolytes in energy and sensing applications.
Main Methods:
- Hybridization of graphene oxide (GO) and Cu-coupled bacterial cellulose (BC-Cu) to create a gradient in water content.
- Fabrication of a solid-state diode membrane utilizing asymmetric proton transport pathways and potential differences.
- Theoretical calculations to elucidate the mechanism of rectified ionic current.
Main Results:
- Achieved exceptionally high rectification ratios of approximately 125 due to asymmetric transport and interfacial potential barriers.
- Demonstrated enhanced proton transport facilitating efficient force-electric conversion for pressure sensing.
- Successfully created an all-solid-state proton diode membrane.
Conclusions:
- The bioinspired solid-state diode membrane exhibits ultrafast and highly rectified proton transport.
- The material design enables efficient force-electric conversion for pressure sensing applications.
- This approach offers potential for next-generation solid-state rectification systems, including self-powered sensors and neuromorphic computing.
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