Male reproductive problems have become an important factor affecting the harmonious development of society. However, there is still a lack of fast and convenient diagnostic techniques for male sperm DNA fragmentation. In this work, we prepared a photosensitive memristor based on a C 15 H 8 O 6 /ZnO heterojunction as the functional layer of the device, which exhibits bipolar resistance switching (RS) behavior with stable cycling, high durability, and good uniformity. Through the synergistic regulation of light intensity and applied voltage, the device can achieve paired-pulse facilitation (PPF) and long-term potentiation/depression (LTP/LTD) plasticity to accurately simulate biosynaptic functions. Further, it was applied to monitor DNA strand breaks in male sperm based on the performance advantages of the as-prepared memristive device. Moreover, this method overcomes the limitations of traditional diagnostic indicators (DNA fragmentation index and high DNA stainability), which establishes a novel diagnostic indicator by converting biological signals into electrical signals, and verifies the responsiveness of memristors to the characteristics of biological samples. Therefore, this method not only significantly reduces detection costs and simplifies diagnostic procedures but also expands the application scenarios of artificial intelligence (AI) technology in the reproductive field, providing experimental support for the integration of bioelectronic interfaces, brain-inspired devices, and biological systems. • A photosensitive memristor based on a C 15 H 8 O 6 /ZnO heterojunction as the functional layer of the device was prepared, which exhibits bipolar resistance switching (RS) behavior. • The as-prepared memristor can be achieved paired-pulse facilitation (PPF) and long-term potentiation/depression (LTP/LTD) plasticity, accurately simulating biosynaptic functions. • The device is further applied to monitor DNA strand breaks in male sperm based on the performance advantages of the as-prepared memristive device. • This work not only significantly reduces detection costs and simplifies diagnostic procedures but also expands the application scenarios of AI technology in the reproductive field.
Cui et al. (Fri,) studied this question.