Supercapacitively Liquid‐Solid Dual‐State Optoelectronics
Photo-transduction of solid-state optoelectronics occurs in semiconductors or their interfaces. Considering the confined active area and interfacial capacitance of solid-state materials, solid-state optoelectronics faces inherent limitations in photo-transduction, especially for bionic vision, and the performance is lower than that of living systems. For example, a photoreceptor generates pA-level photocurrent when absorbing a single photon. Here, a liquid-solid dual-state phototransistor is demonstrated, in which photo-transduction and modulation take place at the microporous interface between semiconductors and water, mimicking principles of the photoreceptor. When operating in the water, an orderly stacked photo-harvesting covalent organic framework layer generates supercapacitively photogating modulation of the channel conductivity via a dual-state interface, achieving responsivity of 4.6 × 10<sup>10</sup> A W<sup>-1</sup> and detectivity of 1.62 × 10<sup>16</sup> Jones at room temperature, several orders of magnitude higher than other photodetectors. Such bio-inspired dual-state optoelectronics enables high-contrast scotopic neuromorphic imaging with responsivity greater than photoreceptors, holding promise for constructing optoelectronic systems with performance beyond conventional solid-state optoelectronics.
Authors
- Gang Chen (ORCID: https://orcid.org/0000-0003-2979-6033)
- Dapeng Wei (ORCID: https://orcid.org/0000-0002-3575-617X)
- Yungen Wu (ORCID: https://orcid.org/0000-0002-0601-0221)
- Daizong Ji (ORCID: https://orcid.org/0000-0003-2331-5219)
- Lan Peng (ORCID: https://orcid.org/0000-0002-2426-8365)
- Qianying Guo (ORCID: https://orcid.org/0000-0002-4929-6127)
- Cong Zhang (ORCID: https://orcid.org/0000-0003-1569-3084)
- Yunqi Liu (ORCID: https://orcid.org/0000-0001-5521-2316)
- Qiankun Wang (ORCID: https://orcid.org/0000-0001-5551-9940)
- Shi Luo
- Wentao Liu (ORCID: https://orcid.org/0000-0001-8402-7486)
- Derong Kong
Institutions
- Tongji University (CN)
- Fudan University (CN)
- Chongqing Institute of Green and Intelligent Technology (CN)
- Shanghai Institute of Technical Physics (CN)
Publication Details
- Journal
- Advanced Materials
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1002/adma.202406345
- Citations
- 15
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 3.13
Funders
- National Natural Science Foundation of China
- Fudan University
- National Key Research and Development Program of China