Room-temperature operation of light-assisted NO2gas sensor based on GaN nanowires and graphene

We report the successful demonstration of a light-assisted NO₂gas sensor that operates at room temperature with high response. The gas sensor was fabricated with high-crystalline undoped-GaN nanowires (NWs) and graphene functioning as the light-absorbing medium and carrier channel, respectively. Exposure of the gas sensor to the NO₂concentration of 100 ppm at a light intensity of 1 mW cm^-2of a xenon lamp delivered a response of 16% at room temperature, which increased to 23% when the light intensity increased to 100 mW cm^-2. This value is higher than those previously reported for GaN-based NO₂gas sensors operating at room temperature. The room-temperature response of the gas sensor measured after six months was calculated to be 21.9%, which corresponds to 95% compared to the value obtained immediately after fabricating the devices. The response of the gas sensor after independently injecting NO₂, H₂S, H₂, CO, and CH₃CHO gases were measured to be 23, 5, 2.6, 2.2, and 1.7%, respectively. These results indicate that the gas sensor using GaN NWs and graphene provides high response, long-term stability, and good selectivity to NO₂gas at room temperature. In addition, the use of undoped-GaN NWs without using additional catalysts makes it possible to fabricate gas sensors that operate at room temperature simpler and better than conventional technologies.

Disentangling topographic contributions to near-field scanning microwave microscopy images

We develop empirical models to predict the contribution of topographic variations in a sample to near-field scanning probe microwave microscopy (NSMM) images. In particular, we focus on |S₁₁| images of a thin Perovskite photovoltaic material and a GaN nanowire. The difference between the measured NSMM image and this prediction is our estimate of the contribution of material property variations to the measured image. Prediction model parameters are determined from either a reference sample that is nearly free of material property variations or directly from the sample of interest. The parameters of the prediction model are determined by robust linear regression so as to minimize the effect of material property variations on results. For the case where the parameters are determined from the reference sample, the prediction is adjusted to account for instrument drift effects. Our statistical approach black is fully empirical black and thus complementary to current approaches based on physical models that are often overly simplistic.

Photorechargeable High Voltage Redox Battery Enabled by Ta3 N5 and GaN/Si Dual-Photoelectrode

Solar rechargeable battery combines the advantages of photoelectrochemical devices and batteries and has emerged as an attractive alternative to artificial photosynthesis for large-scale solar energy harvesting and storage. Due to the low photovoltages by the photoelectrodes, however, most previous demonstrations of unassisted photocharge have been realized on systems with low open circuit potentials (<0.8 V). In response to this critical challenge, here it is shown that the combined photovoltages exceeding 1.4 V can be obtained using a Ta₃ N₅ nanotube photoanode and a GaN nanowire/Si photocathode with high photocurrents (>5 mA cm^-2 ). The photoelectrode system makes it possible to operate a 1.2 V alkaline anthraquinone/ferrocyanide redox battery with a high ideal solar-to-chemical conversion efficiency of 3.0% without externally applied potentials. Importantly, the photocharged battery is successfully discharged with a high voltage output.

Facile Formation of High-Quality InGaN/GaN Quantum-Disks-in-Nanowires on Bulk-Metal Substrates for High-Power Light-Emitters

High-quality nitride materials grown on scalable and low-cost metallic substrates are considerably attractive for high-power light-emitters. We demonstrate here, for the first time, the high-power red (705 nm) InGaN/GaN quantum-disks (Qdisks)-in-nanowire light-emitting diodes (LEDs) self-assembled directly on metal-substrates. The LEDs exhibited a low turn-on voltage of ∼2 V without efficiency droop up to injection current of 500 mA (1.6 kA/cm(2)) at ∼5 V. This is achieved through the direct growth and optimization of high-quality nanowires on titanium (Ti) coated bulk polycrystalline-molybdenum (Mo) substrates. We performed extensive studies on the growth mechanisms, obtained high-crystal-quality nanowires, and confirmed the epitaxial relationship between the cubic titanium nitride (TiN) transition layer and the hexagonal nanowires. The growth of nanowires on all-metal stack of TiN/Ti/Mo enables simultaneous implementation of n-metal contact, reflector, and heat sink, which greatly simplifies the fabrication process of high-power light-emitters. Our work ushers in a practical platform for high-power nanowires light-emitters, providing versatile solutions for multiple cross-disciplinary applications that are greatly enhanced by leveraging on the chemical stability of nitride materials, large specific surface of nanowires, chemical lift-off ready layer structures, and reusable Mo substrates.

Origin of 3.45 eV Emission Line and Yellow Luminescence Band in GaN Nanowires: Surface Microwire and Defect

The physical origin of the strong emission line at 3.45 eV and broadening yellow luminescence (YL) band centered at 2.2 eV in GaN nanowire (NW) has been debated for many years. Here, we solve these two notable issues by using state-of-the-art first-principles calculations based on many-body perturbation theory combined with polarization-resolved experiments. We demonstrate that the ubiquitous surface "microwires" with amazing characteristics, i.e., the outgrowth nanocrystal along the NW side wall, are vital and offer a new perspective to provide insight into some puzzles in epitaxy materials. Furthermore, inversion of the top valence bands, in the decreasing order of crystal-field split-off hole (CH) and heavy/light hole, results in the optical transition polarized along the NW axis due to quantum confinement. The optical emission from bound excitons localized around the surface microwire to CH band is responsible for the 3.45 eV line with E∥c polarization. Both gallium vacancy and carbon-related defects tend to assemble at the NW surface layer, determining the broadening YL band.

Formation and nature of InGaN quantum dots in GaN nanowires

InGaN/GaN disk-in-nanowire heterostructures on silicon substrates have emerged as important gain media for the realization of visible light sources. The nature of quantum confinement in the disks is largely unknown. From the unique nature of the measured temperature dependence of the radiative lifetime and direct transmission electron microscopy, it is evident that such self-organized islands (disks) behave as quantum dots. This is confirmed by the observation of single photon emission from a single disk-in-nanowire and the presence of a sharp minimum in the line width enhancement factor of edge emitting lasers having the InGaN disks as the gain media.

Adaptive and robust statistical methods for processing near-field scanning microwave microscopy images

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical.