Search for: All records

Medium viscosity strongly affects the dynamics of solvated species and can drastically alter the deactivation pathways of their excited states. This study demonstrates the utility of poly(dimethylsiloxane) (PDMS) as a room-temperature solid-state medium for optical spectroscopy. As a thermoset elastic polymer, PDMS is transparent in the near ultraviolet, visible, and near infrared spectral regions. It is easy to mould into any shape, forming surfaces with a pronounced smoothness. While PDMS is broadly used for the fabrication of microfluidic devices, it swells in organic solvents, presenting severe limitations for the utility of such devices for applications employing non-aqueous fluids. Nevertheless, this swelling is reversible, which proves immensely beneficial for loading samples into the PDMS solid matrix. Transferring molecular-rotor dyes (used for staining prokaryotic cells and amyloid proteins) from non-viscous solvents into PDMS induces orders-of-magnitude enhancement of their fluorescence quantum yield and excited-state lifetimes, providing mechanistic insights about their deactivation pathways. These findings demonstrate the unexplored potential of PDMS as a solid solvent for optical applications. 

Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events.