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The motivation for this work is to better characterize the controls on the tropical upper tropospheric/lower stratospheric (UTLS) temperature and humidity.
Many structural materials rely on the formation of chromia, silica or alumina as a protective layer when exposed in high temperature oxidizing environments. The presence of these oxide layers provides a protective diffusion barrier which slows down further oxidation. In atmospheres containing water vapor, however, reactions to form volatile hydroxide species occur which remove the surface oxide, thus, lowering the protective capability of the oxide scale. This paper summarizes the current understanding of volatility of chromia, silica and alumina in water vapor containing combustion environments. In addition unanswered questions in each system are discussed. Th current paper represents an update on the considerable information learned in the past five years for these systems.
A wideband passive synthetic-aperture multichannel receiver with an antenna is mounted on a satellite which travels in an orbit above the Earth passing over large bodies of water, e.g., the Atlantic Ocean. The antenna is scanned to receive signals over a wide frequency band from each incremental surface area (pixel) of the water which are related to the pixel's sea temperature. The received signals are fed to several channels which are tuned to separate selected frequencies. Their outputs are fed to a processor with a memory for storage. As the antenna points to pixels within a calibration area around a buoy of known coordinates, signals are likewise received and stored. Exactly measured sea temperature is received from the buoy. After passing over several calibration areas, a forward stepwise regression analysis is performed to produce an expression which selects the significant from the insignificant channels and assigns weights (coefficients) to them. The expression is used to determine the sea temperature at each pixel based on the signals received therefrom. Wind temperature, pressure, and wind speed at each pixel can also be calculated.
The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
The tunability and spatial precision of paramagnetic molecules makes them attractive for quantum sensing. However, usual microwave-based detection methods have poor temporal and spatial resolution, and optical methods compatible with room-temperature solutions have remained elusive. In this study, we utilized pump-probe polarization spectroscopy to initialize and track electron spin coherence in a molecule. Designed to efficiently couple spins to light, aqueous potassium hexachloroiridate(IV) enabled detection of few-picosecond free-induction decay at room temperature and micromolar concentrations. Viscosity was found to strongly vary decoherence lifetimes. This approach has improved the experimental time resolution by up to five orders of magnitude, making it possible to observe molecular electron spin coherence in a system that only exhibits coherence below 25 kelvin with traditional techniques.
Hydration numbers of metal complexes in low-density aqueous solutions are required for developing geochemical models for ore-forming metals and for designing supercritical desalination processes. In this study, we investigate the temperature and density dependence of the hydration numbers of silver chloride at 623 K, 673 K, and 713 K and densities of 10–100 kg/m 3 . Experimental estimates of the hydration number in the literature for AgCl at these conditions are inconclusive and possibly contradictory as to the temperature and density dependence. First-principles molecular simulation presents an attractive alternative to experimental measurements. Specifically, recent work shows that machine-learning-accelerated nested Monte Carlo simulations provide reliable estimates for the hydration numbers of CuCl at 623 K from 10 to 100 kg/m 3 . Using the same technique, we find a monotonic temperature dependence, with the hydration number decreasing slightly with increasing temperature. In addition, the simulation-predicted hydration numbers steadily increase with increasing density. These temperature and density trends are in agreement with certain experimental data sets. Therefore, this work demonstrates how first-principles Monte Carlo simulations assist in resolving discrepancies between experimental data sets. Our simulation results also correctly predict that the hydration number, and thus also the solubility, of AgCl is lower than that of CuCl under the same conditions. Furthermore, the bond length and angle formed between the water complex and AgCl differ from those for CuCl, consistent with the lower solubility of AgCl.
While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.
Mainly due to their global nature, satellite observations can provide a very useful basis for GCM validations. In particular, satellite sounders such as AIRS provide 3-D spatial information (most useful for GCMs), so the question arises: can we use AIRS datasets for climate variability assessments? We show that the recent (September 2002 February 2010) CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. The correspondence can be seen even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics, for example. This essentially perfect agreement of OLR anomalies and trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate, and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by EI-Nino-La Nina cycles . We have created climate parameter anomaly datasets using AIRS retrievals which can be compared directly with coupled GCM climate variability assessments. Moreover, interrelationships of these anomalies and trends should also be similar between the observed and GCM-generated datasets, and, in cases of discrepancies, GCM parameterizations could be improved based on the relationships observed in the data. First, we assess spatial "trends" of variability of climatic parameter anomalies [since anomalies relative to the seasonal cycle are good proxies of climate variability] at the common 1x1 degree GCM grid-scale by creating spatial anomaly "trends" based on the first 7+ years of AIRS Version 5 Leve13 data. We suggest that modelers should compare these with their (coupled) GCM's performance covering the same period. We evaluate temporal variability and interrelations of climatic anomalies on global to regional e.g., deep Tropical Hovmoller diagrams, El-Nino-related variability scales, and show the effects of El-Nino-La Nina activity on tropical anomalies and trends of water vapor cloud cover and OLR. For GCMs to be trusted highly for long-term climate change predictions, they should be able to reproduce findings similar to these. In summary, the AIRS-based climate variability analyses provide high quality, informative and physically plausible interrelationships among OLR, temperature, humidity and cloud cover both on the spatial and temporal scales. GCM validations can use these results even directly, e. g., by creating 1x1 degree trendmaps for the same period in coupled climate simulations.
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Driven by environmental and health concerns related to per- and polyfluoroalkyl substances (PFAS), there has been growing interest in developing fluorine-free proton (H + ) exchange membrane (PEM) materials for fuel cells and water electrolyzers. In this study, we present a side-by-side comparison of the key transport properties of submicron thick, PFAS-free amorphous silicon dioxide (SiO 2 ) membranes to Nafion, a fluorinated polymer electrolyte membrane that represents the industry standard for PEM fuel cells and electrolyzers. Here, measurements of proton (H + ) conductivity (σ H + ), hydrogen (H 2 ) permeability (P H 2 ), and electrical resistivity (ρ e – ) were conducted using model thin films comprised of SiO 2 membranes deposited by atomic layer deposition (ALD). Although the H + conductivity of the SiO 2 membranes is 2–3 orders of magnitude lower than Nafion, the addition of phosphorus dopants (PO x ) improves H + conductivity such that the area specific membrane resistance of thin (<50 nm) PO x -doped SiO 2 membranes is more than an order of magnitude lower than Nafion-117. Importantly, the safe operation of such nanoscale membranes within a PEM electrolyzer is feasible thanks to the low H 2 permeability of dense SiO 2 -based membranes, which are predicted to limit H 2 crossover rates to acceptable levels for pressures up to ≈ 100 bar. As a proof-of-principle demonstration, a chip-scale water electrolyzer based on 100 nm thick PO x -SiO 2 membrane is shown to achieve a current density of 2 A cm –2 at a potential of 2.5 V. If this technology can be successfully scaled up, H + conducting oxide membranes offer an attractive PFAS-free alternative to Nafion for efficient and durable water electrolysis and fuel cell technologies.
The Hybrid Sulfur (HyS) process is a promising thermochemical water-splitting cycle with global scale hydrogen production potential. The SO 2 -depolarized electrolyzer (SDE) is a critical component of the cycle. At the core of the SDE is the membrane-electrode assembly (MEA), which consists of a polymer electrolyte membrane (PEM) sandwiched between two electrocatalyst layers. New electrocatalyst and membrane materials are being developed with the goals of improving the electrolyzer performance and extending the lifetime of the MEA. In this work, we evaluated the performance of three different membranes and optimized operating conditions, resulting is one of the highest performances in the literature.
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