Electronic Materials

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%.

This work examines a cylindrical FE-DE heterostructure and compares its performance with that of a cylindrical FE-FE-DE heterostructure. It aims to maximize voltage amplification, increase capacitance, and attain a constant negative capacitance. First, the existence of negative capacitance is shown by analyzing isolated cylindrical ferroelectric capacitors. A cylindrical dielectric capacitor and a cylindrical ferroelectric capacitor are integrated in series to stabilize negative capacitance. Our results indicate that the capacitance of the FE-FE-DE stack, consisting of ${Si:HfO}_2$ and ${Zr:HfO}_2$, closely aligns with the dielectric capacitance. Consequently, enhanced performance is anticipated in comparison with the FE-DE arrangement. Additionally, the dynamic response of two distinct configurations was analyzed, yielding a comprehensive understanding of these heterostructures’ behavior.

Multiferroic composites of xNi_0.8Zn_0.2Fe₂O₄/(1 − x)BaTiO₃ (x = 0, 0.1, 0.3, 0.5, labeled NZFO/BTO) with ~100 nm particle size were synthesized via high-energy ball milling and thermal annealing. The X-ray diffraction shows a co-existence of the ferromagnetic phase of NZFO and the ferroelectric phase of BTO. Our observations indicate that saturation, remanence, and coercivity progressively increase with increasing NFO content, specifically from x = 0 to x = 0.5. At x = 0.1, the maximum electric polarization, remanent electric polarization, coercivity and electric power loss density reach their maximum values of ~0.057 µC/cm², 0.018 µC/cm², 3.25 kV/cm and 0.222 mJ/cm³, respectively, for an applied electric field less than 10 kV/cm. These multiferroic composites demonstrate excellent electromagnetic wave absorption capabilities from 2 to 18 GHz. With BTNF1 (x = 0.1) sample thickness of 2.5–3.5 mm, a minimum reflection loss of −41.51, −37, −28.72 dB corresponds to frequencies of 12.52 GHz, 11 GHz and 9.32 GHz. The effective absorption bandwidth for this sample is 11.5–16 GHz, indicating optimal impedance and attenuation matching and effective absorption of electromagnetic waves throughout the K_u bands. These outcomes reveal the capability for wideband absorption uses in radar invisibility technology and electromagnetic insulation.