2D Conjugated Metal–Organic Framework‐Based Composite Membranes for Nanofluidic Ionic Photoelectric Conversion

Nanofluidic photoelectric conversion system based on photo-excitable 2D materials can directly transduce light stimuli into an ion-transport-mediated electric signal, showing potential for mimicking the retina's function with a more favorable human-robot interactions. However, the current membranes suffer from low generation efficiency of charge carriers due to the mixed microstructure and limited charge transport ability caused by the large interlayer spacing and monotonous pathway. Here, a fully conjugated 2D hexaimino-substituted triphenylene-based metal-organic framework (2D-HATP-cMOF) based composite membrane with high conductivity for photoelectric conversion is presented. The extended π-d conjugation within the ab plane and the favorable transport pathway through π-π stacking of the c-MOF maximize the generation and transfer of charge carrier and greatly accelerate the ion transport. As a result, the 2D-HATP-cMOF-based composite membrane possesses ultrafast photoelectric response, superior to other reported 2D systems like graphene oxide (GO), transition metal carbides, carbonitrides and nitrides (MXene), and MoS<sub>2</sub>, which require at least 10 s. A successful ion pump phenomenon, that is active transport from low concentration to high concentration as an important way of information transmission in organisms, is realized based on the efficient photoelectric conversion capability. This work demonstrates the great promise of 2D c-MOF in ionic photoelectric conversion.

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Publication Details

Journal
Advanced Materials
Published
2025-02-10
DOI
https://doi.org/10.1002/adma.202416093
Citations
11
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
8.03
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article

2D Conjugated Metal–Organic Framework‐Based Composite Membranes for Nanofluidic Ionic Photoelectric Conversion

Xinyan Jiang, Zhe Li, Lei Jiang, Naijia Zhao et al.
11 citations
Advanced Materials
Advanced Memory and Neural Computing
8.03
article

2D Conjugated Metal–Organic Framework‐Based Composite Membranes for Nanofluidic Ionic Photoelectric Conversion

Xinyan Jiang, Zhe Li, Lei Jiang, Naijia Zhao, Zhen Zhang, Qixiang Zhang
article en
11 citations

Abstract

Nanofluidic photoelectric conversion system based on photo-excitable 2D materials can directly transduce light stimuli into an ion-transport-mediated electric signal, showing potential for mimicking the retina's function with a more favorable human-robot interactions. However, the current membranes suffer from low generation efficiency of charge carriers due to the mixed microstructure and limited charge transport ability caused by the large interlayer spacing and monotonous pathway. Here, a fully conjugated 2D hexaimino-substituted triphenylene-based metal-organic framework (2D-HATP-cMOF) based composite membrane with high conductivity for photoelectric conversion is presented. The extended π-d conjugation within the ab plane and the favorable transport pathway through π-π stacking of the c-MOF maximize the generation and transfer of charge carrier and greatly accelerate the ion transport. As a result, the 2D-HATP-cMOF-based composite membrane possesses ultrafast photoelectric response, superior to other reported 2D systems like graphene oxide (GO), transition metal carbides, carbonitrides and nitrides (MXene), and MoS<sub>2</sub>, which require at least 10 s. A successful ion pump phenomenon, that is active transport from low concentration to high concentration as an important way of information transmission in organisms, is realized based on the efficient photoelectric conversion capability. This work demonstrates the great promise of 2D c-MOF in ionic photoelectric conversion.

Advanced MaterialsVol. 37(12)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Suzhou University of Science and Technology (CN), Technical Institute of Physics and Chemistry (CN)
Affordable and clean energy
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
8.03
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