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Chiroptical Synaptic Heterojunction Phototransistors Based on Self‐Assembled Nanohelix of π‐Conjugated Molecules for Direct Noise‐Reduced Detection of Circularly Polarized Light
High‐performance chiroptical synaptic phototransistors are successfully demonstrated using heterojunctions composed of a self‐assembled nanohelix of a π‐conjugated molecule and a metal oxide semiconductor. To impart strong chiroptical activity to the device, a diketopyrrolopyrrole‐based π‐conjugated...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520648/ https://www.ncbi.nlm.nih.gov/pubmed/37501319 http://dx.doi.org/10.1002/advs.202304039 |
Sumario: | High‐performance chiroptical synaptic phototransistors are successfully demonstrated using heterojunctions composed of a self‐assembled nanohelix of a π‐conjugated molecule and a metal oxide semiconductor. To impart strong chiroptical activity to the device, a diketopyrrolopyrrole‐based π‐conjugated molecule decorated with chiral glutamic acid is newly synthesized; this molecule is capable of supramolecular self‐assembly through noncovalent intermolecular interactions. In particular, nanohelix formed by intertwinded fibers with strong and stable chiroptical activity in a solid‐film state are obtained through hydrogen‐bonding‐driven, gelation‐assisted self‐assembly. Phototransistors based on interfacial charge transfer at the heterojunction from the chiroptical nanohelix to the metal oxide semiconductor show excellent chiroptical detection with a high photocurrent dissymmetry factor of 1.97 and a high photoresponsivity of 218 A W(−1). The chiroptical phototransistor demonstrates photonic synapse‐like, time‐dependent photocurrent generation, along with persistent photoconductivity, which is attributed to the interfacial charge trapping. Through the advantage of synaptic functionality, a trained convolutional neural network successfully recognizes noise‐reduced circularly polarized images of handwritten alphabetic characters with better than 89.7% accuracy. |
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