Superhydrophobic surfaces are biomimetic structures with potential applications in several key technological areas. In the past decade, several top-down and bottom-up fabrication methods have been developed to create such surfaces. These typically combine a hierarchical structure and low surface energy coatings to increase the contact angle and decrease the rolling angles. Silicon-based superhydrophobic surfaces are particularly attractive since they can be integrated with active electronics in order to protect them from the detrimental effects of environmental water and moisture. In this work, we introduce a simple and inexpensive process incorporating electrochemical surface modification (to create a fractal shape micro-nano topography) in combination with a final wet etching step to fabricate a superhydrophobic silicon surface with a contact angle of 160 degrees and a sliding angle of less than 1 degree.

In this paper, the first of a series of papers on optimal battery charging, we present a closed-form solution to the problem of optimally charging a Li-ion battery. A combination of three cost functions is considered as the objective function: time-to-charge (TTC), energy losses (EL), and a temperature rise index (TRI). First, we consider the cost function of the optimization problem as a weighted sum of TTC and EL. We show that the optimal charging strategy in this case is the well-known Constant Current-Constant Voltage (CC-CV) policy with the value of the current in the CC stage being a function of the ratio of weighting on TTC and EL and of the resistance of the battery. Then, we extend the cost function to a weighted sum of TTC, EL and TRI and derive an analytical solution for the problem. It is shown that the analytical solution can be approximated by a CC-CV with the value of current in the CC stage being a function of ratio of weighting on TTC and EL, resistance of the battery and the effective thermal resistance. Index Terms battery charging, optimal charging, time to charge, open circuit voltage, state of charge.

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Precision Agriculture (PA) is an integral component of the contemporary agricultural revolution that focuses on enhancing food productivity in proportion to the increasing global population while minimizing resource waste. While the recent advancements in PA, such as the integration of IoT (Internet of Things) sensors, have significantly improved the surveillance of field conditions to achieve high yields, the presence of batteries and electronic chips makes them expensive and non-biodegradable. To address these limitations, for the first time, we have developed a fully Degradable Intelligent Radio Transmitting Sensor (DIRTS) that allows remote sensing of subsoil volumetric water using drone-assisted wireless monitoring. The device consists of a simple miniaturized resonating antenna encapsulated in a biodegradable polymer material such that the resonant frequency of the device is dependent on the dielectric properties of the soil surrounding the encapsulated structure. The simple structure of DIRTS enables scalable additive manufacturing processes using cost-effective, biodegradable materials to fabricate them in a miniaturized size, thereby facilitating their automated distribution in the soil. As a proof-of-concept, we present the use of DIRTS in lab and field conditions where the sensors demonstrate the capability to detect volumetric water content within the range of 3.7–23.5% with a minimum sensitivity of 9.07 MHz/%. Remote sensing of DIRTS can be achieved from an elevation of 40 cm using drones to provide comparable performance to lab measurements. A systematic biodegradation study reveals that DIRTS can provide stable readings within the expected duration of 1 year with less than 4% change in sensitivity before signs of degradation. DIRTS provides a new steppingstone toward advancing precision agriculture while minimizing the environmental footprint.

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