Electronic Materials

Recent studies show that titanium (Ti)-based alloys combine established mechanical strength, corrosion resistance, and biocompatibility with emerging electrical and electrochemical properties relevant to bioelectronics. The main goal of the present manuscript is to give a wide-ranging overview on the use of Ti-alloys in electronics and biomedicine, focusing on a comprehensive analysis and synthesis of the existing literature to identify gaps and future directions. Concurrently, the identification of possible correlations between the effects of the manufacturing process, alloying elements, and other degrees of freedom influencing the material characteristics are put in evidence, aiming to establish a global view on efficient interdisciplinary efforts to realize high-added-value smart devices useful in the field of biomedicine, such as, for example, implantable apparatuses. This review mostly summarizes advances in surface modification approaches—including anodization, conductive coatings, and nanostructuring that improve conductivity while maintaining biological compatibility. Trends in applications demonstrate how these alloys support smart implants, biosensors, and neural interfaces by enabling reliable signal transmission and long-term integration with tissue. Key challenges remain in balancing electrical performance with biological response and in scaling laboratory modifications for clinical use. Perspectives for future work include optimizing alloy composition, refining surface treatments, and developing multifunctional designs that integrate mechanical, biological, and electronic requirements. Together, these directions highlight the potential of titanium alloys to serve as foundational materials for next-generation bioelectronic medical technologies.

To address the issues of insufficient responsivity and low imaging contrast of carbon-based HGFET high-sensitivity short-wave infrared (SWIR) detectors under low-light conditions, this paper proposes a high-sensitivity and high-contrast image enhancement algorithm for low-light detection, with FPGA-based hardware verification. The proposed algorithm establishes a multi-stage cooperative enhancement framework targeting key challenges such as low signal-to-noise ratio (SNR), high dark-state noise, and weak target extraction. Unlike traditional direct enhancement methods, the proposed approach first performs defective row-column correction and background noise separation based on dark-state data, which provides a clean foundation for signal reconstruction. Furthermore, an adaptive gamma correction mechanism based on image maximum value is introduced to avoid unnecessary nonlinear transformations in high-contrast regions. During the contrast enhancement stage, an exposure-constrained adaptive histogram equalization strategy is adopted to effectively suppress noise amplification and saturation in low-light scenes. Finally, an innovative dual-mode threshold selection method based on image variance is proposed, which can dynamically integrate the OTSU algorithm with statistical moment analysis to ensure robust background noise separation across both high- and low-contrast scenarios. Experimental results demonstrate that the proposed algorithm significantly improves target contrast in infrared images while preventing detail loss due to overexposure. Under microwatt-level laser power, background noise is effectively suppressed, and both imaging quality and weak target detection capability are substantially enhanced.

In this study, we explore the integration of a cost-effective triboelectric nanogenerator (TENG) with an large silicon PIN detector (diameter: 12 mm) for intelligent wireless recognition applications. Wireless communication eliminates the need for physical connections, enabling greater flexibility and scalability in deployment. It allows for seamless integration of AI systems into a wide range of environments without the constraints of wiring, reducing installation complexity and enhancing mobility. Additionally, we demonstrate the TENG’s functionality as an autonomous communication unit. The TENG is employed to convert various environmentally triggered signals into digital formats and to autonomously power optoelectronic devices, thus eliminating the need for an external power supply. By integrating optoelectronic components within the self-powered sensing system, the TENG can identify specific trigger information and reduce extraneous noise, thereby improving the accuracy of information transmission. Moreover wireless technology facilitates real-time data transmission and processing. This setup not only enhances the overall efficiency and adaptability of the system but also supports continuous operation in diverse and dynamic settings. This paper introduces a novel convolutional neural network-long short-term memory (CNN-LSTM) fusion neural network model. Utilizing the sensing system in combination with the CNN-LSTM neural network enables the collection and identification of variations in the flicker frequency and luminosity of optoelectronic devices. This capability allows for the recognition of environmental trigger signals generated by the TENG. The classification and recognition results of human body trigger signals indicate a recognition accuracy of 92.94%.