Constant Optical Power Operation of an Ultraviolet LED Controlled by a Smartphone.

Constant light power operation of an ultraviolet (UV) LED based on portable low-cost instrumentation and a monolithically integrated monitoring photodiode (MPD) has been reported for the first time. UV light irradiation has become one of the essential measures for disinfection and sterilization. Monitoring and maintaining a specified light power level is important to meet the criteria of sterilization. We built a module composed of a monolithically integrated UV LED and MPD, a transimpedance amplifier, an Arduino Uno card, a digital-to-analog converter and a Bluetooth transceiver. An Android App that we wrote remotely controlled the UV LED module via Bluetooth. The Arduino Uno card was programmed to receive demands from the smartphone, sent a driving voltage to the LED and returned the present MPD voltage to the smartphone. A feedback loop was used to adjust the LED voltage for maintaining a constant light output. We successfully demonstrated the functioning of remote control of the App, and the resultant UV LED measured power remained the same as the setting power. This setup can also be applied to visible or white LEDs for controlling/maintaining mixed light's chromaticity coordinates or color temperature. With such controlling and internet capability, custom profiling and maintenance of precision lighting remotely would be possible.

Molecular Chirality Detection with Periodic Arrays of Three-Dimensional Twisted Metamaterials.

Rapid detection of the handiness of chiral molecules is an important topic for pharmaceutical industries because chiral drugs with opposing handiness sometimes exhibit unwanted side effects. In this research, a rapid optical method is proposed to determine the handiness of the chiral drug "Thalidomide". The platform is a large array of three-dimensional (3D) twisted metamaterials fabricated with a novel method by combining nanospherical-lens lithography (NLL) and hole-mask lithography (HML). The fabrication is high-throughput and the twisted metamaterials cover a large area. Strong circular dichroism (CD) response is observed in the near-infrared (NIR) region, which enables the chiral detection to be performed by a low-cost and portable spectroscope system. The proposed nanofabrication method significantly improves the capabilities of NLL and HML, which can be quickly adapted to fabricate various periodic 3D metamaterials. In addition, the results of this research pave the road for the rapid penetration of nanophotonics into the pharmaceutical industries.

Polarization-Selecting III-Nitride Elliptical Nanorod Light-Emitting Diodes Fabricated with Nanospherical-Lens Lithography.

Current-injected elliptical nanorod light-emitting diodes (LEDs) are demonstrated to emit polarized light with a bottom-emitting configuration. The polarization ratio of the electroluminescence reaches 3.17 when the length of the minor axis for the elliptical nanorods is as small as 150 nm. Electromagnetic simulation confirms the occurrence of the polarization selectivity especially when the length of the minor axis is down to 150 nm. Light with different polarization travels at different speeds in these asymmetric elliptical nanorods. Only one polarization experiences destructive interference between the light directly from the source and the reflected light by the top metal interface. A thin light-blocking layer is incorporated to increase the polarization selectivity. It is also not recommended to infill the gap with SiO2 since the polarization selectivity will be reduced. The proposed nanorod LEDs are fabricated using top-down nanofabrication approaches by combining nanospherical-lens lithography and two-step etch processes, which are both fully compatible with current semiconductor manufacturing processes. Results in this study will help to develop a chip-level polarization-selecting LED, which will be very useful for applications that require polarized light. It is especially beneficial for applications that are not suitable for using an external polarizer or require polarized light at the individual chip level.

Large-Scale Nanofabrication of Designed Nanostructures Using Angled Nanospherical-Lens Lithography for Surface Enhanced Infrared Absorption Spectroscopy.

Nanophotonics has been a focused research discipline for the past decade and has resulted in many novel concepts that promise to change human life. However, the actual penetration of this research into real products is severely limited mostly due to the slow development of economic nanofabrication. In this study, we have demonstrated a versatile and low-cost nanofabrication method referred to as "angled nanospherical-lens lithography (A-NLL)", which is able to produce large-scale and periodic nanopatterns. The nanopatterns designed within a two-dimensional polar chart can be carefully fabricated on the substrate. The fabricated patterns easily cover an area larger than 1 cm2, which is beneficial as platforms for surface enhanced infrared absorption (SEIRA) where an expensive and bulky IR microscope is not required. We believe that the proposed A-NLL method is ideal for industrialization of future nanophotonic applications.

A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks.

Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.