Classification of device structures and applications of self-powered ultraviolet photodetectors.

The authors report a hybrid organic/inorganic optical upconversion device that converts $1.5\mu \mathrm{m}$ infrared light to $\sim 520\mathrm{nm}$ visible light. The device was made by direct tandem integration of an inorganic $\mathrm{In}\mathrm{Ga}\mathrm{As}∕\mathrm{In}\mathrm{P}$ photodetector with an organic light-emitting diode (OLED). Optical upconversion with an external efficiency of 0.7% W/W at room temperature has been achieved. Interfacial structure at the inorganic-organic interface was found to play a vital role in enabling the integration of the hybrid tandem upconverter. Both sulfur-terminated InP surface and nanocarbon fullerene interlayer were found crucial to form a good interface contact, permitting continuous flow of photocarriers from the inorganic detector into the OLED.

According to the International Diabetes Federation, 530 million people worldwide have diabetes, with more than 6.7 million reported deaths in 2021. Monitoring blood glucose levels is essential for individuals with diabetes, and developing noninvasive monitors has been a long-standing aspiration in diabetes management. The ideal method for monitoring diabetes is to obtain the glucose concentration level with a fast, accurate, and pain-free measurement that does not require blood drawing or a surgical operation. Multiple noninvasive glucose detection techniques have been developed, including bio-impedance spectroscopy, electromagnetic sensing, and metabolic heat conformation. Nevertheless, reliability and consistency challenges were reported for these methods due to ambient temperature and environmental condition sensitivity. Among all the noninvasive glucose detection techniques, optical spectroscopy has rapidly advanced. A photoacoustic system has been developed using a single wavelength quantum cascade laser, lasing at a glucose fingerprint of 1080 cm${}^{-1}$ for noninvasive glucose monitoring. The system has been examined using artificial skin phantoms, covering the normal and hyperglycemia blood glucose ranges. The detection sensitivity of the system has been improved to $\pm 25$ mg/dL using a single wavelength for the entire range of blood glucose. Machine learning has been employed to detect glucose levels using photoacoustic spectroscopy in skin samples. Ensemble machine learning models have been developed to measure glucose concentration using classification techniques. The model has achieved a 90.4% prediction accuracy with 100% of the predicted data located in zones A and B of Clarke’s error grid analysis. This finding fulfills the US Food and Drug Administration requirements for glucose monitors.