Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70 °C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

Highly Efficient, Dual-Functional Self-Assembled Electrospun Nanofiber Filters for Simultaneous PM Removal and On-Site Eye-Readable Formaldehyde Sensing.

Air pollution containing particulate matter (PM) and volatile organic compounds has caused magnificent burdens on individual health and global economy. Although advances in highly efficient or multifunctional nanofiber filters have been achieved, many existing filters can only deal with one type of air pollutant, such as capturing PM or absorbing and detecting toxic gas. Here, highly efficient, dual-functional, self-assembled electrospun nanofiber (SAEN) filters were developed for simultaneous PM removal and onsite eye-readable formaldehyde sensing fabricated on a commercial fabric mask. With the use of an electrolyte solution containing a formaldehyde-sensitive colorimetric agent as a collector during electrospinning, the one-step fabrication of the dual-functional SAEN filter on commercial masks, such as a fabric mask and a daily disposable mask, was achieved. The electrolyte solution also allowed the uniform deposition of electrospun nanofibers, thereby achieving the high efficiency of PM filtration with an increased quality factor up to twice that of commercial masks. The SAEN filter enabled onsite and eye-readable formaldehyde gas detection by changing its color from yellow to red under a 5 ppm concentrated formaldehyde gas atmosphere. The repetitive fabrication and detachment of the SAEN filter on a fabric mask minimized the waste of the mask while maintaining high filtration efficiency by replenishing the SAEN filters and reusing the fabric mask. Given the dual functionality of SAEN filters, this process could provide new insights into designing and developing high performance and dual-functional electrospun nanofiber filters for various applications, including individual protection and indoor purification applications. Supplementary Information: The online version contains supplementary material available at 10.1007/s42765-023-00279-3.

Fabrication Procedure for a 3D Hollow Nanofibrous Bifurcated-Tubular Scaffold by Conformal Electrospinning.

Electrospinning has shown great potential for the fabrication of 3D nanofibrous tubular scaffolds for bifurcated vascular grafts. However, fabrication of complex 3D nanofibrous tubular scaffolds with bifurcated or patient-specific shapes remains limited. In this study, a 3D hollow nanofibrous bifurcated-tubular scaffold was fabricated by the uniform and conformal deposition of electrospun nanofibers via conformal electrospinning. By conformal electrospinning, electrospun nanofibers are conformally deposited onto a complex shape, such as the bifurcated region, without large pores or defects. Owing to conformal electrospinning, a corner profile fidelity (FC), a measure of conformal deposition of electrospun nanofibers at the bifurcated region, was increased 4 times at the bifurcation angle (θB) of 60°, and all FC values of the scaffolds reached 100%, regardless of the θB. Furthermore, the thickness of the scaffolds could be controlled by varying the electrospinning time. Leakage-free liquid transfer was successfully achieved owing to the uniform and conformal deposition of electrospun nanofibers. Finally, the cytocompatibility and 3D mesh-based modeling of the scaffolds were demonstrated. Thus, conformal electrospinning can be used to fabricate leakage-free and complex 3D nanofibrous scaffolds for bifurcated vascular grafts.

Electrospray mode discrimination with current signal using deep convolutional neural network and class activation map.

The electrospray process has been extensively applied in various fields, including energy, display, sensor, and biomedical engineering owing to its ability to generate of functional micro/nanoparticles. Although the mode of the electrospray process has a significant impact on the quality of micro/nano particles, observing and discriminating the mode of electrospray during the process has not received adequate attention. This study develops a simple automated method to discriminate the mode of the electrospray process based on the current signal using a deep convolutional neural network (CNN) and class activation map (CAM). The solution flow rate and applied voltage are selected as experimental variables, and the electrospray process is classified into three modes: dripping, pulsating, and cone-jet. The current signal through the collector is measured to detect the deposition of electrospray droplets on the collector. The 1D CNN model is trained using frequency data converted from the current data. The model exhibits excellent performance with an accuracy of 96.30%. Adoption of the CAM configuration enables the model to provide a discriminative cue for each mode and elucidate the decision-making process of the CNN model.

Highly efficient patterning technique for silver nanowire electrodes by electrospray deposition and its application to self-powered triboelectric tactile sensor.

A patterned transparent electrode is a crucial component of state-of-the-art wearable devices and optoelectronic devices. However, most of the patterning methods using silver nanowires (AgNWs), which is one of the outstanding candidate materials for the transparent electrode, wasted a large amount of unused AgNWs during the patterning process. Here, we report a highly efficient patterning of AgNWs using electrospray deposition with grounded electrolyte solution (EDGE). During electrospray deposition, a patterned electrolyte solution collector attracted AgNWs by strong electrostatic attraction and selectively deposited them only on the patterned collector, minimizing AgNW deposited elsewhere. The enhanced patterning efficiency was verified through a comparison between the EDGE and conventional process by numerical simulation and experimental validation. As a result, despite the same electrospray deposition conditions for both cases except for the existence of the electrolyte solution collector, the coverage ratio of AgNWs fabricated by the EDGE process was at least six times higher than that of AgNWs produced by the conventional process. Furthermore, the EDGE process provided high design flexibility in terms of not only the material of the substrate, including a polymer and a ceramic but also the shape of the substrate, including a 2D flat and 3D curved surface. As an application of the EDGE process, a self-powered touch sensor exploiting the triboelectric effect was demonstrated. Thus, the EDGE process would be utilized in further application in wearable or implantable devices in the field of biomedicine, intelligent robots, and human-machine interface.

Conformal Fabrication of an Electrospun Nanofiber Mat on a 3D Ear Cartilage-Shaped Hydrogel Collector Based on Hydrogel-Assisted Electrospinning.

Electrospinning is a common and versatile process to produce nanofibers and deposit them on a collector as a two-dimensional nanofiber mat or a three-dimensional (3D) macroscopic arrangement. However, 3D electroconductive collectors with complex geometries, including protruded, curved, and recessed regions, generally caused hampering of a conformal deposition and incomplete covering of electrospun nanofibers. In this study, we suggested a conformal fabrication of an electrospun nanofiber mat on a 3D ear cartilage-shaped hydrogel collector based on hydrogel-assisted electrospinning. To relieve the influence of the complex geometries, we flattened the protruded parts of the 3D ear cartilage-shaped hydrogel collector by exploiting the flexibility of the hydrogel. We found that the suggested fabrication technique could significantly decrease an unevenly focused electric field, caused by the complex geometries of the 3D collector, by alleviating the standard deviation by more than 70% through numerical simulation. Furthermore, it was experimentally confirmed that an electrospun nanofiber mat conformally covered the flattened hydrogel collector with a uniform thickness, which was not achieved with the original hydrogel collector. Given that this study established the conformal electrospinning technique on 3D electroconductive collectors, it will contribute to various studies related to electrospinning, including tissue engineering, drug/cell delivery, environmental filter, and clothing.