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In the development of catalytic materials, a set of standard conditions is needed where the kinetic performance of many samples can be compared. This can be challenging when a sample set covers a broad range of activity. Precise kinetic characterization requires uniformity in the gas and catalyst bed composition. This limits the range of convecting devices to low conversion (generally <20%). While steady-state kinetics offer a snapshot of conversion, yield and apparent rates of the slow reaction steps, transient techniques offer much greater detail of rate processes and hence more information as to why certain catalyst compositions offer better performance. In this work, transient experiments in two transport regimes are compared: an advecting differential plug flow reactor (PFR) and a pure-diffusion temporal analysis of products (TAP) reactor. The decomposition of ammonia was used as a model reaction to test three simple materials: polycrystalline iron, cobalt and a bimetallic preparation of the two. These materials presented a wide range of activity and it was not possible to capture transient information in the advecting device for all samples at the same conditions while ensuring uniformity. We push the boundary for the theoretical estimates of uniformity in the TAP device and find reliable kinetic measurement up to 90% conversion. However, what is more advantageous from this technique is the ability to observe the time-dependence of the reaction rate rather than just singular points of conversion and yield. For example, on the iron sample we observed reversible adsorption of ammonia and on cobalt materials we identify two routes for hydrogen production. From the time-dependence of reactants and product, the dynamic accumulation was calculated. This was used to understand the atomic distribution of H and N species regulated by the surface of different materials. When ammonia was pulsed at 550 °C, the surface hydrogen/nitrogen, (H/N), ratios that evolved for Fe, CoFe and Co were 2.4, 0.25 and 0.3 respectively. This indicates that iron will store a mixture of hydrogenated species while materials with cobalt will predominantly store NH and N. While much is already known about iron, cobalt and ammonia decomposition, the goal of this work was to demonstrate new tools for comparing materials over a wider window of conversion and with much greater kinetic detail. As such, this provides an approach for detailed kinetic discrimination of more complex industrial samples beyond conversion and yield.

Within-field spatial variability reduces growers’ return on investment and overall productivity while potentially increasing negative environmental impacts through increased soil erosion, nutrient runoff, and leaching. The hypothesis that integrating energy crops into non-profitable segments of agricultural fields could potentially increase grain yield and biomass feedstock production was tested in this study using a statewide analysis of predominantly corn- and soy-producing counties in Iowa. Basic and rigorous controls on permissible soil and soil-carbon losses were imposed on harvest of crop residues to enhance year-to-year sustainability of crop and residue production. Additional criteria limiting harvesting costs and focus on large-area subfields for biomass production were imposed to reduce the impacts of energy crop integration on grain production. Model simulations were conducted using 4 years (2013–2016) of soil, weather, crop yield, and management practice data on all counties in Iowa. Miscanthus (Miscanthus x giganteus), switchgrass (Panicum virgatum), and crop-residue-based bioenergy feedstock systems were evaluated as biomass. Average energy crop and plant residue harvesting efficiencies were estimated at 50 and 60%, respectively. Because of higher potential yields, average logistics costs for miscanthus-based biomass production were 15 and 23% lower than switchgrass-based and crop residue-based biomass productions, respectively, under basic sustainability controls, and 17 and 26% lower under rigorous sustainability controls. Subfield shape, size, area, and harvest equipment size were the dominant factors influencing harvesting cost and efficiency suggesting that in areas where subfields are predominantly profitable or harvesting efficiencies low, other options such as prairie strips, buffer zones around fields, and riparian areas should be investigated for more profitable biomass production and sustainable farming systems.

Multi-principal element, high entropy alloys (HEAs) are an emerging class of materials that have found applications across the board. Owing to the multitude of possible candidate alloys, exploration and compositional design of HEAs for targeted applications is challenging since it necessitates a rational approach to identify compositions exhibiting enriched performance. Here, we report an innovative framework that integrates molecular dynamics and machine learning to explore a large chemical-configurational space for evaluating elastic modulus of equiatomic and non-equiatomic HEAs along primary crystallographic directions. Vital thermodynamic properties and machine learning features have been incorporated to establish fundamental relationships correlating Young’s modulus with Gibbs free energy, valence electron concentration, and atomic size difference. In HEAs, as the number of elements increases, interactions between the elastic modulus values and features become increasingly nested, but tractable as long as non-linearity is accounted. Basic design principles are offered to predict HEAs with enhanced mechanical attributes. Graphical abstract

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With physical and mechanistic complexity of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood mechanism driving the reaction. As oxygen accumulated on the catalyst, a transition in the mechanism was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

The temporal analysis of products reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including the residence time distribution, kinetic coefficients, number of active sites, and the reaction mechanism. However, as with any measurement device, the TAP reactor signal is convoluted with noise. To reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced analysis. This preprocessing consists of baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The current methodology of preprocessing requires significant user discretion and reliance on previous experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to convert the raw instrument signal to chemical information. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing in the calibration of the inert and feed mixture products without need of prior calibration experiments or heuristic input from the user.

An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sensitivity analyses and PDE-constrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate parameters and assist in mechanism discrimination. The mathematical and structural details of TAPsolver are outlined, as well as validation of the forward and inverse problems against well-studied prototype problems. Benchmarks of the code are presented, and a case study for extracting thermodynamically-consistent kinetic parameters from experimental TAP measurements of CO oxidation on supported platinum particles is presented. TAPsolver will act as a foundation for future development and dissemination of TAP data processing techniques.

Dendritic cells are key players of the immune system as they efficiently induce primary immune responses by activating naive T cells. We generated human dendritic cells from CD14+ blood precursors and investigated expression of the actin-bundling protein fascin during maturation by western blotting, immunofluorescence, and cytofluorometry. Cells obtained by culture of CD14+ blood precursors in the presence of granulocyte-macrophage colony-stimulating factor and interleukin-4, which were only weakly positive for the maturation marker CD83, expressed low amounts of fascin. Addition of a cytokine cocktail including tumor necrosis factor α, interleukin-1β, interleukin-6, and prostaglandin E2 induced maturation of the cells and enhanced fascin expression in parallel with CD83 expression. Isolated mature CD83+ cells displayed especially high fascin levels on western blots, as did gated CD83+ dendritic cells in cytofluorometry. Dendritic cells generated from CD34+ blood precursors expressed high levels of fascin as well. Confocal microscopy revealed that location of fascin within the cell was restricted to the area of the submembranous actin cytoskeleton and to the dendritic processes. Suppression experiments using antisense constructs of fascin hint at a retarded morphologic maturation of dendritic cells, supporting the view that fascin expression is pivotal for dendrite formation. Our data suggest that fascin could serve as a marker molecule to monitor the maturation state of in vitro generated dendritic cells for use in clinical trials.

Daily administration of vitamin D is important for maintaining bone homeostasis. The orthopedic community has shown increased interest in vitamin D supplementation and patient outcomes after fracture. The current study used data from a large hip fracture trial to determine the proportion of patients who consistently used vitamin D after hip fracture surgery and to determine whether supplementation was associated with improved health-related quality of life and reduced reoperation rates. The FAITH study is a multicenter trial of elderly patients with femoral neck fracture treated with internal fixation. The current study asked a subset of patients included in the FAITH study about vitamin D supplementation and categorized them as consistent users, inconsistent users, or nonusers. This study also evaluated whether supplementation was associated with improved quality of life and reduced reoperation rates. The final analysis included 573 patients (mean age, 74.1 years; female, 66.3%; nondis-placed fractures, 72.4%). A total of 18.7% of participants reported no use of vitamin D, 35.6% reported inconsistent use, and 45.7% reported consistent use. Adjusted analysis found that consistent supplementation was associated with a 2.42 increase of the Short Form-12 physical component score 12 months postoperatively ( P =.033). However, supplementation was not associated with reduced reoperation rates ( P =.386). Despite guidelines recommending vitamin D supplementation, a low proportion of elderly patients with hip fracture use vitamin D consistently, suggesting a need for additional strategies to promote compliance. This study found that the use of vitamin D was associated with a statistically significant but not clinically significant improvement in health-related quality of life after hip fracture. Further research is needed to confirm these findings. [ Orthopedics. 2017; 40(5):e868–e875.]

It has recently been demonstrated experimentally that a turbulent plasma created by the collision of two inhomogeneous, asymmetric, weakly magnetised laser-produced plasma jets can generate strong stochastic magnetic fields via the small-scale turbulent dynamo mechanism, provided the magnetic Reynolds number of the plasma is sufficiently large. In this paper, we compare such a plasma with one arising from two pre-magnetised plasma jets whose creation is identical save for the addition of a strong external magnetic field imposed by a pulsed magnetic field generator (`MIFEDS'). We investigate the differences between the two turbulent systems using a Thomson-scattering diagnostic, X-ray self-emission imaging and proton radiography. The Thomson-scattering spectra and X-ray images suggest that the presence of the external magnetic field has a limited effect on the plasma dynamics in the experiment. While the presence of the external magnetic field induces collimation of the flows in the colliding plasma jets and the initial strengths of the magnetic fields arising from the interaction between the colliding jets are significantly larger as a result of the external field, the energy and morphology of the stochastic magnetic fields post-amplification are indistinguishable. We conclude that, for turbulent laser-plasmas with super-critical magnetic Reynolds numbers, the dynamo-amplified magnetic fields are determined by the turbulent dynamics rather than the seed fields and modest changes in the initial flow dynamics of the plasma, a finding consistent with theoretical expectations and simulations of turbulent dynamos.

Understanding magnetic-field generation and amplification in turbulent plasma is essential to account for observations of magnetic fields in the universe. A theoretical framework attributing the origin and sustainment of these fields to the so-called fluctuation dynamo was recently validated by experiments on laser facilities in low-magnetic-Prandtl-number plasmas (\\(\\mathrm{Pm} < 1\\)). However, the same framework proposes that the fluctuation dynamo should operate differently when \\(\\mathrm{Pm} \\gtrsim 1\\), the regime relevant to many astrophysical environments such as the intracluster medium of galaxy clusters. This paper reports a new experiment that creates a laboratory \\(\\mathrm{Pm} \\gtrsim 1\\) plasma dynamo for the first time. We provide a time-resolved characterization of the plasma's evolution, measuring temperatures, densities, flow velocities and magnetic fields, which allows us to explore various stages of the fluctuation dynamo's operation. The magnetic energy in structures with characteristic scales close to the driving scale of the stochastic motions is found to increase by almost three orders of magnitude from its initial value and saturate dynamically. It is shown that the growth of these fields occurs exponentially at a rate that is much greater than the turnover rate of the driving-scale stochastic motions. Our results point to the possibility that plasma turbulence produced by strong shear can generate fields more efficiently at the driving scale than anticipated by idealized MHD simulations of the nonhelical fluctuation dynamo; this finding could help explain the large-scale fields inferred from observations of astrophysical systems.