Graphene quantum dots covalently functionalized with zinc porphyrin for digital-analog dual-mode memristors

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.