ITO/Ag/ITO (IAI) multilayer-based transparent conductive electrodes for ultraviolet light-emitting diodes are fabricated by reactive sputtering, optimized by annealing, and characterized with respect to electrical and optical properties. Increasing the annealing temperature from 300°C to 500°C decreased the sheet resistance and increased the transmittance. This may result from an observed improvement in the crystallinity of the IAI multilayer and a reduction in the near-UV absorption coefficient of Ag. We observed the lowest sheet resistance (9.21 Ω/sq) and the highest optical transmittance (88%) at 380 nm for the IAI multilayer samples annealed in N₂ gas at 500°C.
Warm and natural white light (i.e., with a correlated colour temperature <5000 K) with good colour rendition (i.e., a colour rendering index >75) is in demand as an indoor lighting source of comfortable interior lighting and mood lighting. However, for warm white light, phosphor-converted white light-emitting diodes (WLEDs) require a red phosphor instead of a commercial yellow phosphor (YAG:Ce), and suffer from limitations such as unavoidable energy conversion losses, degraded phosphors and high manufacturing costs. Phosphor-free WLEDs based on three-dimensional (3D) indium gallium nitride (InGaN)/gallium nitride (GaN) structures are promising alternatives. Here, we propose a new concept for highly efficient phosphor-free warm WLEDs using 3D core-shell InGaN/GaN dodecagonal ring structures, fabricated by selective area growth and the KOH wet etching method. Electrically driven, phosphor-free warm WLEDs were successfully demonstrated with a low correlated colour temperature (4500 K) and high colour rendering index (R = 81). From our findings, we believe that WLEDs based on dodecagonal ring structures become a platform enabling a high-efficiency warm white light-emitting source without the use of phosphors.
As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (μLED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In 2 O 3 film coated with plasmonic metal nanoparticles (NPs) directly on the μLED. The e-nose system consists of two different μLED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-μLED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO 2 , acetone, methanol) was developed. The μLED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications.
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