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The 3rd-Gen OLED materials employing thermally-activated delayed fluorescence (TADF) combine advantages of first two for high-efficiency and low-cost devices. Though urgently needed, blue TADF emitters have not met stability requirement for applications. It is essential to elucidate the degradation mechanism and identify the tailored descriptor for material stability and device lifetime. Here, via in-material chemistry, we demonstrate chemical degradation of TADF materials involves critical role of bond cleavage at triplet state rather than singlet, and disclose the difference between bond dissociation energy of fragile bonds and first triplet state energy (BDE-ET1) is linearly correlated with logarithm of reported device lifetime for various blue TADF emitters. This significant quantitative correlation strongly reveals the degradation mechanism of TADF materials have general characteristic in essence and BDE-ET1 could be the shared "longevity gene". Our findings provide a critical molecular descriptor for high-throughput-virtual-screening and rational design to unlock the full potential of TADF materials and devices. High throughput virtual screening of robust blue thermally activated delayed fluorescent emitters has yet been implemented. Here, authors reveal a linear relationship of the difference between bond dissociate energy and first triplet state energy with the logarithm of device operational lifetime. [ABSTRACT FROM AUTHOR]

A critical aspect of air pollution exposure assessments is estimation of the air exchange rate (AER) for various buildings where people spend their time. The AER, which is the rate of exchange of indoor air with outdoor air, is an important determinant for entry of outdoor air pollutants and for removal of indoor-emitted air pollutants. This paper presents an overview and critical analysis of the scientific literature on empirical and physically based AER models for residential and commercial buildings; the models highlighted here are feasible for exposure assessments as extensive inputs are not required. Models are included for the three types of airflows that can occur across building envelopes: leakage, natural ventilation, and mechanical ventilation. Guidance is provided to select the preferable AER model based on available data, desired temporal resolution, types of airflows, and types of buildings included in the exposure assessment. For exposure assessments with some limited building leakage or AER measurements, strategies are described to reduce AER model uncertainty. This review will facilitate the selection of AER models in support of air pollution exposure assessments. [ABSTRACT FROM AUTHOR]

The Relationship of Indoor, Outdoor and Personal Air (RIOPA) study was designed to investigate residential indoor, outdoor and personal exposures to several classes of air pollutants, including volatile organic compounds, carbonyls and fine particles (PM2.5). Samples were collected from summer, 1999 to spring, 2001 in Houston (TX), Los Angeles (CA) and Elizabeth (NJ). Indoor, outdoor and personal PM2.5 samples were collected at 212 nonsmoking residences, 162 of which were sampled twice. Some homes were chosen due to close proximity to ambient sources of one or more target analytes, while others were farther from sources. Median indoor, outdoor and personal PM2.5 mass concentrations for these three sites were 14.4, 15.5 and 31.4?µg/m3, respectively. The contributions of ambient (outdoor) and nonambient sources to indoor and personal concentrations were quantified using a single compartment box model with measured air exchange rate and a random component superposition (RCS) statistical model. The median contribution of ambient sources to indoor PM2.5 concentrations using the mass balance approach was estimated to be 56%for all study homes (63%, 52%and 33%for California, New Jersey and Texas study homes, respectively). Reasonable variations in model assumptions alter median ambient contributions by less than 20%. The mean of the distribution of ambient contributions across study homes agreed well for the mass balance and RCS models, but the distribution was somewhat broader when calculated using the mass balance model with measured air exchange rates.Journal of Exposure Analysis and Environmental Epidemiology (2005) 15, 17-28. doi:10.1038/sj.jea.7500378 Published online 12 May 2004 [ABSTRACT FROM AUTHOR]