Harvesting energy from natural water evaporation has been proposed as a promising alternative to supply power for self‐powered and low‐power devices and systems, owing to its spontaneous, ubiquitous, and sustainability. Herein, an approach is presented for harvesting water‐evaporation‐induced electricity based on liquid–solid triboelectric nanogenerators (LS‐TENGs), which has various advantages of easy preparation, substrate needless, and robustness. This developed harvester with porous Al₂O₃ ceramic sheet can generate a continuous and stable direct current of ≈0.3 µA and voltage of ≈0.7 V by optimizing the sheet physical dimensions and ambient parameters such as relative humidity, temperature, wind velocity, and ion concentration. The output power also can be improved significantly by series or parallel connection the harvesters, which has superior electrical compatibility and environmental suitability. The development of the water‐evaporation‐induced electricity harvesting shows many application prospects including power supply for digital calculator and charging capacitor. This research provides an in‐depth experimental study on water‐evaporation‐induced electricity harvesting based on LS‐TENGs and an efficient approach to supply electricity for low‐power devices.

This paper presents a novel technique to develop combined neural network and transfer function models for parametric modeling of passive components. In this technique, the neural network is trained to map geometrical variables onto coefficients of transfer functions. A major advance is achieved in resolving the discontinuity problem of numerical solutions of the coefficients with respect to the geometrical variables. Minimum orders of transfer functions for different regions of geometrical parameter space are identified. Our investigations show that varied orders used for different regions result in the discontinuity of coefficients. The gaps between orders are bridged by a new order-changing module, which guarantees the continuity of coefficients and simultaneously maintains the modeling accuracy through a neural network optimization process. This technique is also expanded to include bilinear transfer functions. Once trained, the model provides accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to conventional training methods, the proposed method allows better accuracy in challenging applications involving high-order transfer functions, wide frequency range, and large geometrical variations. Three examples including parametric modeling of slotted patch antennas, bandstop microstrip filters, and bandpass coupled-line filters are examined to demonstrate the validity of this technique.

The paper proposes an ultrahigh sensitivity microwave sensor for retrieving the complex permittivity of the liquid sample. The proposed sensor originally evolves from the complementary split-ring resonator-based sensor, which confines the electric field in the slot of the resonator. A complementary electric-LC resonator structure is applied to substitute the CSRR due to its excellent ability of concentrating the electric field. To further improve the field confinement, a meander slot is embedded in the central arm of the resonator. Then, two etched complementary split-ring resonators are added on both sides of the central meander arm of the modified complementary electric-LC resonator. A polydimethylsiloxane microfluidic channel substrate is placed on the meander arm, and a mixture of water-ethanol is injected. The variations in the resonant frequency and peak attenuation are used to retrieve the complex permittivity of the injected liquid sample. The developed sensors are fabricated and tested, and a good agreement is observed between measured data and reference values. The peak sensitivities in retrieving the relative permittivity achieve 2.5% and 2.71% with the proposed sensors, which are much higher than previously reported designs.