RESUMEN
High-performance sensors capable of detecting multidirectional strains are indispensable to understand the complex motions involved in flexible electronics. Conventional isotropic strain sensors can only measure uniaxial deformations or single stimuli, hindering their practical application fields. The answer to such challenge resides in the construction of engineered anisotropic sensing structures. Herein, a hierarchically aligned carbon nanofiber (CNF)/polydimethylsiloxane nanocomposite strain sensor is developed by one-step 3D printing. The precisely controlled printing path and shear flow bring about highly aligned nanocomposite filaments at macroscale and orientated CNF network within each filament at microscale. The periodically orientated nanocomposite filaments along with the inner aligned CNF network successfully control the strain distribution and the appearance of microcracks, giving rise to anisotropic structural response to external deformations. The synergetic effect of the multiscale structural design leads to distinguishable gauge factors of 164 and 0.5 for applied loadings along and transverse to the alignment direction, leading to an exceptional selectivity of 3.77. The real-world applications of the hierarchically aligned sensors in multiaxial movement detector and posture-correction device are further demonstrated. The above findings propose new ideas for manufacturing nanocomposites with engineered anisotropic structure and properties, verifying promising applications in emerging wearable electronics and soft robotics.
RESUMEN
Ultraviolet micro-LEDs show great potential as a light source for maskless photolithography. However, there are few reports on micro-LED based maskless photolithography systems, and the studies on the effects of system parameters on exposure patterns are still lacking. Hence, we developed a maskless photolithography system that employs micro-LEDs with peak wavelength 375â nm to produce micrometer-sized exposure patterns in photoresists. We also systematically explored the effects of exposure time and current density of micro-LED on static direct writing patterns, as well as the effects of stage velocity and current pulse width on dynamic direct writing patterns. Furthermore, reducing the size of micro-LED pixels enables obtaining high-resolution exposure patterns, but this approach will bring technical challenges and high costs. Therefore, this paper proposes an oblique direct writing method that, instead of reducing the micro-LED pixel size, improves the pattern resolution by changing the tilt angle of the sample. The experimental results show that the linewidths of the exposed lines decreased by 4.0% and 15.2%, respectively, as the sample tilt angle increased from 0° to 15° and 30°, which confirms the feasibility of the proposed method to improve the pattern resolution. This method is also expected to correct the exposure pattern error caused by optical distortion of the lens in the photolithography system. The system and method reported can be applied in various fields such as PCBs, photovoltaics, solar cells, and MEMS.
RESUMEN
GaN-based micro-LED is an emerging display and communication device, which can work as well as a photodetector, enabling possible applications in machine vision. In this work, we measured the characteristics of micro-LED based photodetector experimentally and proposed a feasible simulation of a novel artificial neural network (ANN) device for the first time based on a micro-LED based photodetector array, providing ultrafast imaging (â¼133 million bins per second) and a high image recognition rate. The array itself constitutes a neural network, in which the synaptic weights are tunable by the bias voltage. It has the potentials to be integrated with novel machine vision and reconfigurable computing applications, acting as a role of acceleration and similar functionality expansion. Also, the multi-functionality of micro-LED broadens its application potentials of combining ANN with display and communication.