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Gold nanoclusters (Au NCs) are promising luminescent nanomaterials due to their outstanding optical properties. However, their relatively low quantum yields and environment-dependent photoluminescence properties have limited their biological applications. To address these problems, we developed a novel strategy to prepare chitosan oligosaccharide lactate (Chi)-functionalized Au NCs (Au NCs@Chi), which exhibited emission with enhanced quantum yield and elongated emission lifetime as compared to the Au NCs, as well as exhibited environment-independent photoluminescence properties. In addition, utilizing the free amino groups of Chi onto Au NCs@Chi, we designed a FRET-based sensing platform for the detection of hydrogen sulfide (H2S). The Au NCs and the specific H2S-sensitive merocyanine compound were respectively employed as an energy donor and acceptor in the platform. The addition of H2S induced changes in the emission profile and luminescence lifetime of the platform with high sensitivity and selectivity. Utilization of the platform was demonstrated to detect exogenous and endogenous H2S in vitro and in vivo through wavelength-ratiometric and time-resolved luminescence imaging (TLI). Compared to previously reported luminescent molecules, the platform was less affected by experimental conditions and showed minimized autofluorescence interference and improved accuracy of detection.

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Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones for navigation, smartphone-based road condition assessment has emerged as a promising new approach. In this paper, we propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focuses on classification of three main class labels-smooth road, potholes, and deep transverse cracks. We hypothesize that using features from all three axes of the sensors provides more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results indicate that models trained with features from all axes of the smartphone sensors outperform models that use only one axis. We also observe that the use of neural networks provides a significantly improved data classification. The machine learning approach discussed here can be implemented on a larger scale to monitor roads for defects that present a safety risk to commuters as well as to provide maintenance information to relevant authorities.

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