Photodetector based on bis-2,6-[2-(2-oxoindolin-3-ylidene)malononitrile]naphthalene derivatives/zinc oxide nanorod heterostructures with machine vision observation and artificial intelligence pattern recognition

Abstract

Zinc oxide (ZnO)-based photodetectors have made significant progress in the field of broadband photodetection in recent years due to their ease of integration with low-bandgap semiconductors. Although the intrinsic bandgap of 3.2 eV for pure ZnO limits its response to wavelengths outside the ultraviolet (UV) light region, researchers have successfully extended its detection range into the visible light region through material modifications and heterojunction designs. This study investigates the use of the bis-2,6-[2-(2-oxoindolin-3-ylidene)malononitrile]naphthalene derivative (teven-518)/ZnO nanocomposite as a photodetector. The molecular structure of teven-518 possesses a high electron affinity that is further enhanced by the strong electron-withdrawing nature of the malononitrile groups, which improves electron transport capability. Additionally, the introduction of the naphthalene unit enhances molecular coplanarity, resulting in a rigid and planar structure that promotes π–π stacking between molecules, thereby improving the charge carrier mobility. On the other hand, the C2C6 side chains in the molecule provide moderate intermolecular interactions, enabling uniform film formation during solution processing. Through the integration of code development and model training, along with material characterization and photodetection results, this study confirms that the addition of teven-518 with ZnO nanorods contributes to the enhancement of ZnO-based photodetector performance, showing significant improvements in both machine learning-based recognition and model evaluation.