As the first invention to efficiently harvest electricity from ambient mechanical energy by using contact electrification, the triboelectric nanogenerator has elicited worldwide attention because of its cost-effectiveness and sustainability. This study exploits a superhydrophobic nanostructured aluminum tube to estimate electrical output for solid-water contact electrification inside a tubular system. The linearly proportional relationship of short-circuit current and open-circuit voltage to the detaching speed of water was determined by using a theoretical energy harvesting model and experimentation. A pioneering stick-type solid-water interacting triboelectric nanogenerator, called a SWING stick, was developed to harvest mechanical energy through solid-water contact electrification generated when the device is shaken by hand. The electrical output generated by various kinds of water from the environment was also measured to demonstrate the concept of the SWING stick as a compact triboelectric nanogenerator. Several SWING sticks were connected to show the feasibility of the device as a portable and compact source of direct power. The developed energy harvesting model and the SWING stick can provide a guideline for the design parameters to attain a desired electrical output; therefore, this study can significantly increase the applicability of a water-driven triboelectric nanogenerator.

Electrospinning is a simple versatile process used to produce nanofibers and collect them as a nanofiber mat. However, due to bending instability, electrospinning often produces a nanofiber mat with non-uniform mat thickness. In this study, we developed a uniform-thickness electrospun nanofiber mat (UTEN) production system with a movable collector based on real-time thickness measurement and thickness feedback control. This system is compatible with a collector with void regions such as a mesh-type collector, two-parallel-metal-plate collector, and ring-type collector, which facilitates the measurement of light transmittance across the produced nanofiber mat during electrospinning. A real-time measurement system was developed to measure light transmittance and convert it to the thickness of the nanofiber mat in real time using the Beer–Lambert law. Thickness feedback control was achieved by repeating the following sequences: (1) finding an optimal position of the movable collector based on the measured thickness of the nanofiber mat, (2) shifting the collector to an optimal position, and (3) performing electrospinning for a given time step. We found that the suggested thickness feedback control algorithm could significantly decrease the non-uniformity of the nanofiber mat by reducing the standard deviation by more than 8 and 3 times for the numerical simulation and experiments, respectively, when compared with the conventional electrospinning. As a pioneering research, this study will contribute to the development of an electrospinning system to produce robust and reliable nanofiber mats in many research and industrial fields such as biomedicine, environment, and energy.

Controlling stress and deformation induced by thermo-mechanical stimulation in high-precision mechanical systems can be achieved by mechanical metamaterials (MM) exhibiting negative thermal expansion (NTE) and negative Poisson's ratio (NPR). However, the inverse design of MM exhibiting a wide range of arbitrary target NTEs and NPRs is a challenging task due to the low design flexibility of analytical methods and parametric studies based on numerical simulation. In this study, we propose Bézier curve-based programmable chiral mechanical metamaterials (BPCMs) and a deep autoencoder-based inverse design model (DAIM) for the inverse design of BPCMs. Through iterative transfer learning with data augmentation, DAIM can generate BPCMs with a curved rib shape inaccessible with the Bézier curve, which improves the inverse design performance of the DAIM in the data sparse domain. This approach decreases the mean absolute error of NTE and NPR between the inverse design target and the numerical simulation results of inverse designed BPCMs on the data-sparse domain by 79.25% and 83.33% on average, respectively. A 3D-printed BPCM is validated experimentally and exhibits good coincidence with the target NTE and NPR. Our proposed BPCM and the corresponding inverse design framework enable the inverse design of BPCMs with NTE in the range of −1100 to 0 ppm K⁻¹ and NPR in the range of −0.6 to −0.1. Furthermore, programmable thermal deformation modes with a fixed Poisson's ratio are realized by combining various inverse designed BPCM unit cells. BPCMs and the DAIM for their inverse design are expected to improve the mechanical robustness of high-precision mechanical systems through tunable modulation of thermo-mechanical stimulation.