Direct laser printing of thin-film polyaniline devices

We report the fabrication of electrically functional polyaniline thin-film microdevices. Polyaniline films were printed in the solid phase by Laser Induced Forward Transfer directly between Au electrodes on a Si/SiO2 substrate. To apply solid-phase deposition, aniline was in situ polymerized on quartz substrates. Laser deposition preserves the morphology of the films and delivers sharp features with controllable dimensions. The electrical characteristics of printed polyaniline present ohmic behavior, allowing for electroactive applications. Results on gas sensing of ammonia are presented.Comment: In Pres

Changes in PM2.5 concentrations and their sources in the US from 1990 to 2010

Significant reductions in emissions of SO2, NOx, volatile organic compounds (VOCs), and primary particulate matter (PM) took place in the US from 1990 to 2010. We evaluate here our understanding of the links between these emissions changes and corresponding changes in concentrations and health outcomes using a chemical transport model, the Particulate Matter Comprehensive Air Quality Model with Extensions (PMCAMx), for 1990, 2001, and 2010. The use of the Particle Source Apportionment Algorithm (PSAT) allows us to link the concentration reductions to the sources of the corresponding primary and secondary PM. The reductions in SO2 emissions (64 %, mainly from electric-generating units) during these 20 years have dominated the reductions in PM2.5, leading to a 45 % reduction in sulfate levels. The predicted sulfate reductions are in excellent agreement with the available measurements. Also, the reductions in elemental carbon (EC) emissions (mainly from transportation) have led to a 30 % reduction in EC concentrations. The most important source of organic aerosol (OA) through the years according to PMCAMx is biomass burning, followed by biogenic secondary organic aerosol (SOA). OA from on-road transport has been reduced by more than a factor of 3. On the other hand, changes in biomass burning OA and biogenic SOA have been modest. In 1990, about half of the US population was exposed to annual average PM2.5 concentrations above 20 µg m−3, but by 2010 this fraction had dropped to practically zero. The predicted changes in concentrations are evaluated against the observed changes for 1990, 2001, and 2010 in order to understand whether the model represents reasonably well the corresponding processes caused by the changes in emissions.This work was supported by the Center for Air, Climate, and Energy Solutions (CACES), which was supported under assistance agreement no. R835873 awarded by the U.S. Environmental Protection Agency and the Horizon-2020 Project REMEDIA of the European Union under grant agreement no. 874753.Peer ReviewedPostprint (published version

Bioinformatic analysis of Entamoeba histolytica SINE1 elements

BACKGROUND: Invasive amoebiasis, caused by infection with the human parasite Entamoeba histolytica remains a major cause of morbidity and mortality in some less-developed countries. Genetically E. histolytica exhibits a number of unusual features including having approximately 20% of its genome comprised of repetitive elements. These include a number of families of SINEs - non-autonomous elements which can, however, move with the help of partner LINEs. In many eukaryotes SINE mobility has had a profound effect on gene expression; in this study we concentrated on one such element - EhSINE1, looking in particular for evidence of recent transposition. RESULTS: EhSINE1s were detected in the newly reassembled E. histolytica genome by searching with a Hidden Markov Model developed to encapsulate the key features of this element; 393 were detected. Examination of their sequences revealed that some had an internal structure showing one to four 26-27 nt repeats. Members of the different classes differ in a number of ways and in particular those with two internal repeats show the properties expected of fairly recently transposed SINEs - they are the most homogeneous in length and sequence, they have the longest (i.e. the least decayed) target site duplications and are the most likely to show evidence (in a cDNA library) of active transcription. Furthermore we were able to identify 15 EhSINE1s (6 pairs and one triplet) which appeared to be identical or very nearly so but inserted into different sites in the genome; these provide good evidence that if mobility has now ceased it has only done so very recently. CONCLUSIONS: Of the many families of repetitive elements present in the genome of E. histolytica we have examined in detail just one - EhSINE1. We have shown that there is evidence for waves of transposition at different points in the past and no evidence that mobility has entirely ceased. There are many aspects of the biology of this parasite which are not understood, in particular why it is pathogenic while the closely related species E. dispar is not, the great genetic diversity found amongst patient isolates and the fact, which may be related, that only a small proportion of those infected develop clinical invasive amoebiasis. Mobile genetic elements, with their ability to alter gene expression may well be important in unravelling these puzzles

Development of layered anode structures supported over Apatite-type Solid Electrolytes

Apatite-type lanthanum silicates (ATLS) materials have attracted interest in recent literature as solid electrolytes for SOFCs. The fabrication of an ATLS based fuel cell with the state-of-art electrodes (NiO/YSZ as anode and LSCF or LSM as cathode) can show degradation after long operation hours due to Si diffusion mainly towards the anode. In this work, we report a “layer-by-layer anodic electrodes” fabrication by means of spin coating and physical spraying. The overall aim of this work is the successful fabrication of such a layered structure including suitable blocking layers towards the inhibition of Si interdiffusion from the apatite electrolyte to the anode. The results showed that the deposition of 3 layers of LFSO/GDC (3μm), NiO/GDC (4μm) and the final NiO/YSZ anode layer provided a stable half-cell, with no solid state reaction occurring among the electrodes and no Si diffusion observed towards the anode after thermal treatment at 800°C for 120h

Estimation of secondary organic aerosol formation parameters for the volatility basis set combining thermodenuder, isothermal dilution, and yield measurements

Secondary organic aerosol (SOA) is a major fraction of the total organic aerosol (OA) in the atmosphere. SOA is formed by the partitioning onto pre-existent particles of low-vapor-pressure products of the oxidation of volatile, intermediate-volatility, and semivolatile organic compounds. Oxidation of the precursor molecules results in a myriad of organic products, making the detailed analysis of smog chamber experiments difficult and the incorporation of the corresponding results into chemical transport models (CTMs) challenging. The volatility basis set (VBS) is a framework that has been designed to help bridge the gap between laboratory measurements and CTMs. The parametrization of SOA formation for the VBS has been traditionally based on fitting yield measurements of smog chamber experiments. To reduce the uncertainty in this approach, we developed an algorithm to estimate the SOA product volatility distribution, effective vaporization enthalpy, and effective accommodation coefficient combining SOA yield measurements with thermograms (from thermodenuders) and areograms (from isothermal dilution chambers) from different experiments and laboratories. The algorithm is evaluated with “pseudo-data” produced from the simulation of the corresponding processes, assuming SOA with known properties and introducing experimental error. One of the novel features of our approach is that the proposed algorithm estimates the uncertainty in the predicted yields for different atmospheric conditions (temperature, SOA concentration levels, etc.). The uncertainty in these predicted yields is significantly smaller than that of the estimated volatility distributions for all conditions tested.</p

Formation of semivolatile inorganic aerosols in the Mexico City Metropolitan Area during the MILAGRO campaign

One of the most challenging tasks for chemical transport models (CTMs) is the prediction of the formation and partitioning of the major semi-volatile inorganic aerosol components (nitrate, chloride, ammonium) between the gas and particulate phases. In this work the PMCAMx-2008 CTM, which includes the recently developed aerosol thermodynamic model ISORROPIA-II, is applied in the Mexico City Metropolitan Area in order to simulate the formation of the major inorganic aerosol components. The main sources of SO2 (such as the Miguel Hidalgo Refinery and the Francisco Perez Rios Power Plant) in the Mexico City Metropolitan Area (MCMA) are located in Tula, resulting in high predicted PM1 (particulate matter with diameter less than 1 µm) sulfate concentrations (over 25 µg m-3) in that area. The average predicted PM1 nitrate concentrations are up to 3 µg m-3 (with maxima up to 11 µg m-3) in and around the urban center, mostly produced from local photochemistry. The presence of calcium coming from the Tolteca area (7 µg m-3) as well as the rest of the mineral cations (1 µg m-3 potassium, 1 µg m-3 magnesium, 2 µg m-3 sodium, and 3 µg m-3 calcium) from the Texcoco Lake resulted in the formation of a significant amount of aerosol nitrate in the coarse mode with concentrations up to 3 µg m-3 over these areas. PM1-10 (particulate matter with diameter between 1 and 10 µm) chloride is also high and its concentration exceeds 2 µg m-3 in Texcoco Lake. PM1 ammonium concentrations peak at the center of Mexico City (2 µg m-3) and the Tula vicinity (2.5 µg m-3). The performance of the model for the major inorganic PM components (sulfate, ammonium, nitrate, chloride, sodium, calcium, and magnesium) is encouraging. At the T0 measurement site, located in the Mexico City urban center, the average measured values of PM1 sulfate, nitrate, ammonium, and chloride are 3.5 µg m-3, 3.5 µg m-3, 2.1 µg m-3, and 0.36 µg m-3, respectively. The corresponding predicted values are 3.7 µg m-3, 2.7 µg m-3, 1.7 µg m-3, and 0.25 µg m-3. High sulfate concentrations are associated with the transport of sulfate from the Tula vicinity, while in periods where southerly winds are dominant; the concentrations of sulfate are low. The underprediction of nitrate can be attributed to the underestimation of OH levels by the model during the early morning. Ammonium is sensitive to the predicted sulfate concentrations and the nitrate levels. The performance of the model is also evaluated against measurements taken from a suburban background site (T1) located north of Mexico City. The average predicted PM2.5 (particulate matter with diameter less than 2.5 µm) sulfate, nitrate, ammonium, chloride, sodium, calcium, and magnesium are 3.3, 3.2, 1.4, 0.5, 0.3, 1.2, and 0.15 µg m-3, respectively. The corresponding measured concentrations are 3.7, 2.9, 1.5, 0.3, 0.4, 0.6, and 0.15 µg m-3. The overprediction of calcium indicates a possible overestimation of its emissions and affects the partitioning of nitric acid to the aerosol phase resulting occasionally in an overprediction of nitrate. Additional improvements are possible by improving the performance of the model regarding the oxidant levels, and revising the emissions and the chemical composition of the fugitive dust. The hybrid approach in which the mass transfer to the fine aerosol is simulated using the bulk equilibrium assumption and to the remaining aerosol sections using a dynamic approach, is needed in order to accurately simulate the size distribution of the inorganic aerosols. The bulk equilibrium approach fails to reproduce the observed coarse nitrate and overpredicts the fine nitrate. Sensitivity tests indicate that sulfate concentration in Tula decreases by up to 0.5 µg m-3 after a 50% reduction of SO2 emissions while it can increase by up to 0.3 µg m-3 when NOx emissions are reduced by 50%. Nitrate concentration decreases by up to 1 µg m-3 after the 50% reduction of NOx or NH3 emissions. Ammonium concentration decreases by up to 1 µg m-3, 0.3 µg m-3, and 0.1 µg m-3 after the 50% reduction of NH3, NOx, and SO2 emissions, respectively.Seventh Framework Programme (European Commission) (MEGAPOLI Grant agreement no.: 212520)National Institutes of Health (U.S.) (NSF (ATM-0528227

Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0

Accurately predicting urban PM2.5 concentrations and composition has proved challenging in the past, partially due to the resolution limitations of computationally intensive chemical transport models (CTMs). Increasing the resolution of PM2.5 predictions is desired to support emissions control policy development and address issues related to environmental justice. A nested grid approach using the CTM PMCAMx-v2.0 was used to predict PM2.5 at increasing resolutions of 36 km × 36 km, 12 km × 12 km, 4 km × 4 km, and 1 km × 1 km for a domain largely consisting of Allegheny County and the city of Pittsburgh in southwestern Pennsylvania, US, during February and July 2017. Performance of the model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Novel surrogates were developed to allocate emissions from cooking and on-road traffic sources to the 1 km × 1 km resolution grid. Total PM2.5 mass is reproduced well by the model during the winter period with low fractional error (0.3) and fractional bias (+0.05) when compared to regulatory measurements. Comparison with speciated measurements during this period identified small underpredictions of PM2.5 sulfate, elemental carbon (EC), and organic aerosol (OA) offset by a larger overprediction of PM2.5 nitrate. In the summer period, total PM2.5 mass is underpredicted due to a large underprediction of OA (bias = −1.9 µg m−3, fractional bias = −0.41). In the winter period, the model performs well in reproducing the variability between urban measurements and rural measurements of local pollutants such as EC and OA. This effect is less consistent in the summer period due to a larger fraction of long-range-transported OA. Comparison with total PM2.5 concentration measurements from low-cost sensors showed improvements in performance with increasing resolution. Inconsistencies in PM2.5 nitrate predictions in both periods are believed to be due to errors in partitioning between PM2.5 and PM10 modes and motivate improvements to the treatment of dust particles within the model. The underprediction of summer OA would likely be improved by updates to biogenic secondary organic aerosol (SOA) chemistry within the model, which would result in an increase of long-range transport SOA seen in the inner modeling domain. These improvements are obvious topics for future work towards model improvement. Comparison with regulatory monitors showed that increasing resolution from 36 to 1 km improved both fractional error and fractional bias in both modeling periods. Improvements at all types of measurement locations indicated an improved ability of the model to reproduce urban–rural PM2.5 gradients at higher resolutions.</p

Sources and production of organic aerosol in Mexico City: insights from the combination of a chemical transport model (PMCAMx-2008) and measurements

Urban areas are large sources of organic aerosols and their precursors. Nevertheless, the contributions of primary (POA) and secondary organic aerosol (SOA) to the observed particulate matter levels have been difficult to quantify. In this study the three-dimensional chemical transport model PMCAMx-2008 is used to investigate the temporal and geographic variability of organic aerosol in the Mexico City Metropolitan Area (MCMA) during the MILAGRO campaign that took place in the spring of 2006. The organic module of PMCAMx-2008 includes the recently developed volatility basis-set framework in which both primary and secondary organic components are assumed to be semi-volatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The MCMA emission inventory is modified and the POA emissions are distributed by volatility based on dilution experiments. The model predictions are compared with observations from four different types of sites, an urban (T0), a suburban (T1), a rural (T2), and an elevated site in Pico de Tres Padres (PTP). The performance of the model in reproducing organic mass concentrations in these sites is encouraging. The average predicted PM[subscript 1] organic aerosol (OA) concentration in T0, T1, and T2 is 18 μg m[superscript −3], 11.7 μg m[superscript −3], and 10.5 μg m[superscript −3] respectively, while the corresponding measured values are 17.2 μg m[superscript −3], 11 μg m[superscript −3], and 9 μg m[superscript −3]. The average predicted locally-emitted primary OA concentrations, 4.4 μg m[superscript −3] at T0, 1.2 μg m[superscript −3] at T1 and 1.7 μg m[superscript −3] at PTP, are in reasonably good agreement with the corresponding PMF analysis estimates based on the Aerosol Mass Spectrometer (AMS) observations of 4.5, 1.3, and 2.9 μg m[superscript −3] respectively. The model reproduces reasonably well the average oxygenated OA (OOA) levels in T0 (7.5 μg m[superscript −3] predicted versus 7.5 μg m[superscript −3] measured), in T1 (6.3 μg m[superscript −3] predicted versus 4.6 μg m[superscript −3] measured) and in PTP (6.6 μg m[superscript −3] predicted versus 5.9 μg m[superscript −3] measured). The rest of the OA mass (6.1 μg m[superscript −3] and 4.2 μg m[superscript −3] in T0 and T1 respectively) is assumed to originate from biomass burning activities and is introduced to the model as part of the boundary conditions. Inside Mexico City (at T0), the locally-produced OA is predicted to be on average 60 % locally-emitted primary (POA), 6 % semi-volatile (S-SOA) and intermediate volatile (I-SOA) organic aerosol, and 34 % traditional SOA from the oxidation of VOCs (V-SOA). The average contributions of the OA components to the locally-produced OA for the entire modelling domain are predicted to be 32 % POA, 10 % S-SOA and I-SOA, and 58 % V-SOA. The long range transport from biomass burning activities and other sources in Mexico is predicted to contribute on average almost as much as the local sources during the MILAGRO period.European UnionSeventh Framework Programme (European Commission) (Grant agreement no.: 212520)National Science Foundation (U.S.) (ATM 0732598)Molina Center for Energy and the EnvironmentNational Science Foundation (U.S.) (ATM 0528227)National Science Foundation (U.S.) (ATM 0810931

Simulations of organic aerosol concentrations in Mexico City using the WRF-CHEM model during the MCMA-2006/MILAGRO campaign

Organic aerosol concentrations are simulated using the WRF-CHEM model in Mexico City during the period from 24 to 29 March in association with the MILAGRO-2006 campaign. Two approaches are employed to predict the variation and spatial distribution of the organic aerosol concentrations: (1) a traditional 2-product secondary organic aerosol (SOA) model with non-volatile primary organic aerosols (POA); (2) a non-traditional SOA model including the volatility basis-set modeling method in which primary organic components are assumed to be semi-volatile and photochemically reactive and are distributed in logarithmically spaced volatility bins. The MCMA (Mexico City Metropolitan Area) 2006 official emission inventory is used in simulations and the POA emissions are modified and distributed by volatility based on dilution experiments for the non-traditional SOA model. The model results are compared to the Aerosol Mass Spectrometry (AMS) observations analyzed using the Positive Matrix Factorization (PMF) technique at an urban background site (T0) and a suburban background site (T1) in Mexico City. The traditional SOA model frequently underestimates the observed POA concentrations during rush hours and overestimates the observations in the rest of the time in the city. The model also substantially underestimates the observed SOA concentrations, particularly during daytime, and only produces 21% and 25% of the observed SOA mass in the suburban and urban area, respectively. The non-traditional SOA model performs well in simulating the POA variation, but still overestimates during daytime in the urban area. The SOA simulations are significantly improved in the non-traditional SOA model compared to the traditional SOA model and the SOA production is increased by more than 100% in the city. However, the underestimation during daytime is still salient in the urban area and the non-traditional model also fails to reproduce the high level of SOA concentrations in the suburban area. In the non-traditional SOA model, the aging process of primary organic components considerably decreases the OH levels in simulations and further impacts the SOA formation. If the aging process in the non-traditional model does not have feedback on the OH in the gas-phase chemistry, the SOA production is enhanced by more than 10% compared to the simulations with the OH feedback during daytime, and the gap between the simulations and observations in the urban area is around 3 μg m[superscript −3] or 20% on average during late morning and early afternoon, within the uncertainty from the AMS measurements and PMF analysis. In addition, glyoxal and methylglyoxal can contribute up to approximately 10% of the observed SOA mass in the urban area and 4% in the suburban area. Including the non-OH feedback and the contribution of glyoxal and methylglyoxal, the non-traditional SOA model can explain up to 83% of the observed SOA in the urban area, and the underestimation during late morning and early afternoon is reduced to 0.9 μg m[superscript −3] or 6% on average. Considering the uncertainties from measurements, emissions, meteorological conditions, aging of semi-volatile and intermediate volatile organic compounds, and contributions from background transport, the non-traditional SOA model is capable of closing the gap in SOA mass between measurements and models.National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0528227)National Science Foundation (U.S.). Atmospheric Chemistry Program (ATM-0810931)Molina Center for Energy and the Environmen