Photoproduct distribution in films of cinnamate polymers was analyzed to reveal the contribution of both photoisomerization and photodimerization to LC alignment photoregulation. A polymethacrylate with o-cinnamate side chains displayed preferential formation of Z-isomer while the dimerization takes place more favorably for other polymers including poly(vinyl cinnamate). On the basis of the relationship between photoproduct distribution and liquid crystal photoalignment and on the reversibility of the photoinduced reorientation of a liquid crystal, it was concluded that the photoalignment results from the polarization photoisomerization of cinnamate residues in the same manner as that of photochromic moieties like azobenzenes, whereas the (2 + 2) photodimerization plays a role in enhancing the thermal stability of the homogeneous photoalignment.

Our study adds to previous work in California showing a relation between traffic-related air pollution and autism, and adds similar findings in an eastern US state, with results consistent with increased susceptibility in the third-trimester.

The health risks of As exposure due to the installation of millions of shallow tubewells in the Bengal Basin are known, but fecal contamination of shallow aquifers has not systematically been examined. This could be a source of concern in densely populated areas with poor sanitation because the hydraulic travel time from surface water bodies to shallow wells that are low in As was previously shown to be considerably shorter than for shallow wells that are high in As. In this study, 125 tubewells 6−36 m deep were sampled in duplicate for 18 months to quantify the presence of the fecal indicator Escherichia coli. On any given month, E. coli was detected at levels exceeding 1 most probable number per 100 mL in 19−64% of all shallow tubewells, with a higher proportion typically following periods of heavy rainfall. The frequency of E. coli detection averaged over a year was found to increase with population surrounding a well and decrease with the As content of a well, most likely because of downward transport of E. coli associated with local recharge. The health implications of higher fecal contamination of shallow tubewells, to which millions of households in Bangladesh have switched in order to reduce their exposure to As, need to be evaluated.

Objective: Exposure to ambient fine particulate matter (PM2.5: PM with aerodynamic diameters < 2.5 μm) has been linked with cognitive deficits in older adults. Using fine-grained voxel-wise analyses, we examined whether PM2.5 exposure also affects brain structure.Methods: Brain MRI data were obtained from 1365 women (aged 71–89) in the Women's Health Initiative Memory Study and local brain volumes were estimated using RAVENS (regional analysis of volumes in normalized space). Based on geocoded residential locations and air monitoring data from the U.S. Environmental Protection Agency, we employed a spatiotemporal model to estimate long-term (3-year average) exposure to ambient PM2.5 preceding MRI scans. Voxel-wise linear regression models were fit separately to gray matter (GM) and white matter (WM) maps to analyze associations between brain structure and PM2.5 exposure, with adjustment for potential confounders.Results: Increased PM2.5 exposure was associated with smaller volumes in both cortical GM and subcortical WM areas. For GM, associations were clustered in the bilateral superior, middle, and medial frontal gyri. For WM, the largest clusters were in the frontal lobe, with smaller clusters in the temporal, parietal, and occipital lobes. No statistically significant associations were observed between PM2.5 exposure and hippocampal volumes.Conclusions: Long-term PM2.5 exposures may accelerate loss of both GM and WM in older women. While our previous work linked smaller WM volumes to PM2.5, this is the first neuroimaging study reporting associations between air pollution exposure and smaller volumes of cortical GM. Our data support the hypothesized synaptic neurotoxicity of airborne particles.

Layer-by-layer bottom-up crystal engineering of metal-organic crystals at the surface of sapphire or glass from organic (rubeanic acid and derivatives) and inorganic (Cu(2+)) components which when mixed in solution form instantly an amorphous solid with high proton conduction.

Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

A special electrolyte combination of non-Grignard magnesium chloride, magnesium imide, and triglyme exhibited an excellent performance regarding the magnesium plating/stripping.

Tetrachloroethylene (PCE) is one of the most frequently detected volatile organic compounds (VOCs) in water systems across the USA. In New Jersey, the Department of Environmental Protection (NJDEP) monitors surface water quality at several sites throughout the state. However due to budget and scientific limitations, the sampling data is insufficient to assess all river streams in New Jersey. To address this problem, the objective of this study is to utilize a framework for the space/time estimation of PCE throughout all river reaches in New Jersey over the 1999 through 2003 time period and to track how this concentration evolves over time. We use the Bayesian maximum entropy (BME) mapping method to take into account the composite spatiotemporal variability of PCE, and we produce maps providing a stochastic description of the distribution of PCE at all times throughout the river network. In addition, we conduct a nonattainment assessment analysis by applying a criterion based on the estimated probability distribution function that allows us to identify the river miles that are highly likely in nonattainment of the standard, those that are highly likely in attainment of the standard, and the remaining labeled as nonassessed. Using this criterion we investigate how the river miles contaminated by PCE vary over space and time, and we identify watershed management areas (WMAs) with contamination problems. Finally, a cross validation comparison with a purely spatial analysis demonstrates that the space/time framework leads to a better estimation and a reduction of the number of nonassessed miles.