文献类型：期刊文献

英文题名：Graphene quantum dots covalently functionalized with zinc porphyrin for digital-analog dual-mode memristors

作者：Duan, Fengrui[1];Fan, Fei[1];Huang, Tianyue[1];Li, Wei[1];Sun, Sai[2];Zhang, Bin[1,3]

机构：[1]East China Univ Sci & Technol, Sch Chem & Mol Engn, Key Lab Adv Mat, Shanghai 200237, Peoples R China;[2]Sinopec Shanghai Res Inst Petrochem Technol, Shanghai 201208, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect, Shanghai 200237, Peoples R China

年份：2025

卷号：17

期号：40

起止页码：23363

外文期刊名：NANOSCALE

收录：;EI(收录号：20254219336976);WOS:【SCI-EXPANDED(收录号:WOS:001579148100001)】；

基金：The authors acknowledge the financial supports from the National Natural Science Foundation of China (52321002, 52473171), the National Key Research and Development Program of China (2024YFF0505200), and Natural Science Foundation of Shanghai Municipality (23ZR1416900).

语种：英文

外文关键词：Analog computers - Carbon Quantum Dots - Charge transfer - Convolutional neural networks - Graphene quantum dots - Nanocrystals - Redox reactions - Semiconductor quantum dots - Zinc - Zinc compounds

摘要：Memristors based on quantum dots (QDs) exhibit significant potential in the fields of digital memory and analog computing. However, challenges remain in the research focused on modifying the electronic properties of QDs to enhance the performance of memristors. In this study, we report a novel donor-acceptor (D-A) structured nanomaterial utilizing zinc porphyrin (ZnTPP) covalently modified graphene quantum dots (GQDs). Due to the synergistic effects of charge transfer between the electron-donating ZnTPP molecules and the electron-accepting GQDs, along with the distinctive redox activity of ZnTPP, the Al/ZnTPP-g-GQDs:PVP/ITO device achieves precise modulation of 50 non-volatile conductive states, characteristic of an analog memristor. When subjected to a wider voltage scan, this device exhibits typical digital memristive behavior. Furthermore, the convolutional neural network (CNN) constructed using this memristor displays robust performance in recognizing and classifying five types of animal images with high accuracy. This research establishes a novel pathway for the application of QDs in digital-analog dual-mode memristors.