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Modeling vapor pressure is crucial for studying the moisture reliability of microelectronics, as high vapor pressure can cause device failures in environments with high temperature and humidity. To minimize the impact of vapor pressure, a super-hydrophobic (SH) coating can be applied on the exterior surface of devices in order to prevent moisture penetration. The underlying mechanism of SH coating for enhancing device reliability, however, is still not fully understood. In this paper, we present several existing theories for predicting vapor pressure within microelectronic materials. In addition, we discuss the mechanism and effectiveness of SH coating in preventing water vapor from entering a device, based on experimental results. Two theoretical models, a micro-mechanics-based whole-field vapor pressure model and a convection-diffusion model, are described for predicting vapor pressure. Both methods have been successfully used to explain experimental results on uncoated samples. However, when a device was coated with an SH nanocomposite, weight gain was still observed, likely due to vapor penetration through the SH surface. This phenomenon may cast doubt on the effectiveness of SH coatings in microelectronic devices. Based on current theories and the available experimental results, we conclude that it is necessary to develop a new theory to understand how water vapor penetrates through SH coatings and impacts the materials underneath. Such a theory could greatly improve microelectronics reliability.

The impact of changing emission inventory scenarios is assessed based on combinations of point, mobile, area-road and biogenic sources, on the tropospheric ozone concentration in two southeast Texas urban areas, during the rapid ozone formation event (ROFE) on August 25, 2000. The results revealed that removing Texas point source volatile organic compounds (VOC) emission gives the greatest reduction in Peak O3 concentration (128 ppbv) in the Houston area.

The spatial and diurnal emission characteristics of the gridded emission inventories are assessed and compared with those of the National Emission Trend 1996 (NET96). It was found that National Emission Inventory 1999 (NEI99) data exhibit greater emissions with respect to point and mobile sources but smaller emissions with respect to area sources when compared to the corresponding gridded NET96 data in the same study domain.

Linear regression models are a common approach to analyzing data. This study explores constructing a generic text document to describe linear regression models using Natural Language Generation. A series of functions were developed in this research to explain a regression model. Each function describes a fact about the linear regression model including model parameters, model fit, and outlier analysis. A simulated annealing heuristic is used to join the results of the functions to a single document. The heuristic optimizes the information displayed to the users subject to a page length restriction. The proof of concept software in this study was developed in R. [ABSTRACT FROM AUTHOR]

Speciated samples of PM2.5 were collected at a site in Jefferson County, Texas by US EPA (Environmental Protection Agency) from July of 2003 to August of 2005. A total of 269 samples with 52 species were measured; however, 22 species were excluded in this study because of too many below-detection-limit data. The data set was analyzed by positive matrix factorization (PMF) to infer the sources of PM observed at the site. The analysis identified ten sources: sulfate-rich secondary aerosol I (35.9%), sulfate-rich secondary aerosol II (21.0%), cement/carbon-rich (11.7%), wood smoke (8.8%), metal processing (6.3%), motor vehicle/road dust (5.7%), nitrate-rich secondary aerosol (3.3%), soil (3.2%), sea salt (2.6%), and chloride depleted marine aerosol (1.6%). Sulfate and nitrate mainly exist as salts. The two sulfate-rich secondary aerosols account for almost 57% of the PM2.5 mass concentration. The factor containing highest concentrations of Cl and Na was attributed to sea salt due to the proximity of the monitoring site to the Gulf of Mexico. The chloride depleted marine aerosol was related to the sea salt aerosol. Cement/carbon-rich, wood smoke, metal processing, and motor vehicle/road dust factor were likely to be the local sources. [ABSTRACT FROM AUTHOR]

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