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This work presents the design, analysis, and experimental validation of a novel chip-based 3-D printed ultra-high frequency (UHF) radio frequency identification (RFID) sensor designed for leaf moisture detection for smart farming applications. The presented sensor is designed to operate at a frequency of 915 MHz and utilizes an integrated circuit (IC) chip having a specified impedance of 18.06−j164 Ω at 915 MHz. The proposed sensor is fabricated by the state-of-the-art Nano Dimension DragonFly IV 3-D printer. The 3-D printer uses both dielectric and conductive inks in a single printer for producing additive manufactured electronics (AME). The performance of the sensor is validated by experiments conducted on Valley Oak, Japanese tree lilac, and Crabapple leaf samples. The sensor’s functionality is based on its ability to detect variations in the dielectric properties of leaves, which are caused by changes in moisture content. This is achieved by analyzing the radio frequency (RF) backscattered signal, measured in terms of the received signal strength indicator (RSSI) levels, using a standard RFID reader. Experimental results demonstrate a consistent linear relationship between RSSI levels and leaf moisture content that is used to obtain a calibration curve that can accurately determine unknown moisture levels. By integrating advanced fabrication techniques with reliable RF sensing mechanisms, this work offers a sustainable, and scalable solution for monitoring plant health and optimizing agricultural productivity. 

In this paper, a broadband achromatic focusing metasurface design scheme based on the equivalent circuit theory and optimized by a deep learning method is proposed. The designed metasurface element consists of multilayer metal rings and a grounding layer, and the phase modulation effect of achromatic aberration in a wide frequency range is realized by precisely controlling the distance between the layers. The preparation of this complex structure is realized by using additive manufacturing technology, which effectively overcomes the limitations of traditional printed circuit board technology in manufacturing complex structures. To further improve the design efficiency, deep conditional generative adversarial network is introduced in this paper to quickly determine the structural parameters and realize the inverse design, which significantly improves the efficiency and accuracy of the metasurface structure design. The experimental results show that the metasurface possesses good focusing performance in the 17 to 35 GHz band with an effective bandwidth utilization of 69.2 %. The design method proposed in this study combines artificial intelligence and additive manufacturing technology, which provides new design ideas for applications in the fields of communication, optics and wireless energy transmission. 

This paper presents the development and characterization of a miniaturized RF sensor designed for temperature sensing applications, leveraging advanced additive manufacturing techniques. The sensor utilizes NiTiNOL, a superelastic alloy, as the temperature-sensing material, integrated into a split-box resonator structure. The resonator operates at a frequency of 38.125 GHz, and the design benefits from the flexibility and precision offered by 3D printing technology. This approach allows for a compact form factor and robust performance in harsh environments. The sensor's performance was evaluated through a series of simulations, demonstrating high sensitivity and reliability in temperature measurement. The results highlight the potential of additively manufactured RF sensors in various industrial, medical, and environmental monitoring applications, offering advantages such as reduced size, weight, and power consumption, along with enhanced mechanical robustness and thermal stability. This work underscores the significance of additive manufacturing in advancing next-generation sensor technologies. 

This letter proposes an angle-multiplexed multifocal method for designing a 2-D beam-scanning transmitarray (TA). The angle-multiplexed method is commonly used to generate orbital angular momentum (OAM) multiplexing. The multifocal phase distribution is calculated using the angle-multiplexed and bifocal methods to achieve higher gain enhancement and 2-D beam-scanning with a low scan loss without optimization. The additive manufacturing technology is used to fabricate a 3-D transmission line component. In the terahertz band, the transmitting and receiving (Tx-Rx) unit cell impedance matching can be affected by the Pancharatnam–Berry (P-B) method, and a 3-D coaxial line is introduced to tackle this problem. For proof of concept, the prototype was fabricated. Feeded by a WR-06 waveguide probe, the proposed TA achieves a gain enhancement of 16.3 dB and ±30° 2-D beam-scanning, simulated and measured 3 dB gain bandwidths of 19.6% and 16.3%, and the measured axial ratio bandwidth of 30.7%.