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This report serves as a compendium of the time dependent populations of every isotope produced from the fissioning of the uranium-235 nucleus. All isotope populations, ground state and isomeric state, are plotted as a function of time from microseconds to a week after creation.
The Neumann iteration method presently used for solving the electron transport equation in which energy, attitude, and pitch angle are independent variables is fast, and can compute numerical point-response-function solutions of the electron transport equation. Because both the inelastic cross sections and angular elastic cross sections of the model are empirically based, the solutions obtained represent a test of compatibility between various sets of cross sections and energy deposition measurements. The use of a numerical quadrature based on analytic phase function forms yields accurate phase function integrals at low computational cost.
A new collaboration between the New Mexico Institute of Mining and Technology, Idaho State University, and Idaho National Laboratory builds the domestic workforce in isotope production and related science and technologies by conducting research and education programs related to photo-nuclear reactions. Workforce development was identified by the Department of Energy’s Office of Science in the Reaching a New Energy Sciences Workforce (RENEW) program that seeks to engage underrepresented university students in isotope production activities. This collaboration includes coursework development, student training, and isotope production research utilizing photo-nuclear reactions at electron linear accelerators. Bremsstrahlung-weighted excitation functions and cross-sections will be measured and the use of nanomaterials to exploit kinematic recoil reactions will be investigated. Students will be hosted at the Idaho National Laboratory where mentors will provide opportunities to explore careers in nuclear science and technology. This presentation regards the ongoing progress of the research and education objectives of the collaboration that was performed under the Office of Science Isotopes Program under award number D000-22-2765.
Summary It is well understood that agricultural management influences arbuscular mycorrhizal (AM) fungi, but there is controversy about whether farmers should manage for AM symbiosis. We assessed AM fungal communities colonizing wheat roots for three consecutive years in a long‐term (> 14 yr) tillage and fertilization experiment. Relationships among mycorrhizas, crop performance, and soil ecosystem functions were quantified. Tillage, fertilizers and continuous monoculture all reduced AM fungal richness and shifted community composition toward dominance of a few ruderal taxa. Rhizophagus and Dominikia were depressed by tillage and/or fertilization, and their abundances as well as AM fungal richness correlated positively with soil aggregate stability and nutrient cycling functions across all or no‐tilled samples. In the field, wheat yield was unrelated to AM fungal abundance and correlated negatively with AM fungal richness. In a complementary glasshouse study, wheat biomass was enhanced by soil inoculum from unfertilized, no‐till plots while neutral to depressed growth was observed in wheat inoculated with soils from fertilized and conventionally tilled plots. This study demonstrates contrasting impacts of low‐input and conventional agricultural practices on AM symbiosis and highlights the importance of considering both crop yield and soil ecosystem functions when managing mycorrhizas for more sustainable agroecosystems.
Tradeoffs in microbial functional traits have been a focus of recently described ecological frameworks and of mathematical models of microbial community functioning. Tradeoffs in key traits such as growth rate, growth yield, resource acquisition, and stress tolerance may have either a genetic basis or a physiological basis, and the type of tradeoff can inform how traits are modeled and measured. Here we provide evidence that growth rate/decomposition and growth rate/stress tolerance tradeoffs have a primarily genetic basis in a phylogenetically diverse suite of ten leaf litter-inhabiting fungi. In contrast, growth yield tradeoffs with functional traits are more likely to have a physiological basis. Consideration of the type of tradeoff, genetic or physiological, should help to inform efforts to model microbial contributions to ecosystem processes, especially when considering different scales. Consideration of physiological tradeoffs may be important for understanding short-term variability (e.g., pulse events) and fine spatial scales, whereas genetic tradeoffs are likely to be useful for understanding regional- to continental-scale and medium- to long-term contributions of microbes to ecosystem processes.
Three energy dependent quantities examined for gases, describing relation between ionization yields, cross section and loss functions
We report tests of various density functionals for ferromagnetic Fe, Co, and Ni with a focus on characterizing the behavior of the so-called strongly constrained and appropriately normed (SCAN) functional. It is found that SCAN is closer in behavior to functionals that yield localized behavior, such as hybrid functionals, than other semilocal functionals that are tested. The results are understood in terms of a tendency to differentiate orbitals, favoring integer occupation, which is necessary for a correct description of atomic systems, but inappropriate for the open shell metallic ferromagnetic metals studied here. Not only is the exchange splitting for open shells enhanced with SCAN, but also as seen in Ni, there is much more band dependence with significantly more overestimation for bands corresponding to the partially filled orbitals.
In laser-driven implosions for laboratory fusion, the comparison of hot-spot x-ray yield to neutron production can serve to infer hot-spot mix. For high-performance direct-drive implosions, this ratio depends sensitively on the degree of equilibration between the ion and electron fluids. A scaling for x-ray yield as a function of neutron yield and characteristic ion and electron hot-spot temperatures is developed on the basis of simulations with varying degrees of equilibration. We apply this model to hot-spot x-ray measurements of direct-drive cryogenic implosions typical of the direct-drive designs with best ignition metrics. Furthermore, the comparison of the measured x-ray and neutron yields indicates that hot-spot mix, if present, is below a sensitivity estimated as ~ 2% by-atom mix of ablator plastic into the hot spot.
A number of advantages result from using B-splines as basis functions in a Galerkin method for solving partial differential equations. Among them are arbitrary order of accuracy and high resolution similar to that of compact schemes but without the aliasing error. This work develops another property, namely, the ability to treat semi-structured embedded or zonal meshes for two-dimensional geometries. This can drastically reduce the number of grid points in many applications. Both integer and non-integer refinement ratios are allowed. The report begins by developing an algorithm for choosing basis functions that yield the desired mesh resolution. These functions are suitable products of one-dimensional B-splines. Finally, test cases for linear scalar equations such as the Poisson and advection equation are presented. The scheme is conservative and has uniformly high order of accuracy throughout the domain.
Chitosan (Chi) beads were conjugated with three different amino acids [namely, glutamic acid (GLU), methionine (MET), and taurine (TAU)] aiming to increase the divalent copper ions uptake in aqueous media. Scanning Electron Microscopy evidenced the development of a large porous structure after amino acid functionalization, associated with the increase in a number of amino groups in the polymer backbone. X-Ray Photoelectron Spectroscopy and Fourier-Transform Infrared Spectra analyses were also employed to assess the conjugation of these three different amino acids in chitosan backbone. Adsorption experiments were conducted in a batch process, at 298 K, and kinetic data indicated a slightly better fitting for the pseudo-first-order model when compared to pseudo-second order. Intraparticle diffusion model suggested a three-step mechanism for Cu(II) adsorption kinetics, limited by the third step, the intraparticle diffusion. The isotherm data fitting to the traditional Langmuir and Freundlich models indicated a better fit for the former case. The amino acid conjugation resulted in the increase of the maximum adsorption capacity for Cu(II) from 1.30 mmol g{sup −1} prior to amino acid conjugation to values as high as 2.31 mmol g{sup −1}, 2.40 mmol g{sup −1} and 2.68 mmol g{sup −1} for Chi–TAU, Chi–GLU, and Chi–MET, respectively. These results are attributed to the introduction of additional amino groups and new carboxylate and amino acid residues into the chitosan backbone, which might also be explored for amino acid demanding applications.
We review progress in designing and transforming multi-functional yield-stress fluids and give a perspective on the current state of knowledge that supports each step in the design process. We focus mainly on the rheological properties that make yield-stress fluids so useful and the trade-offs which need to be considered when working with these materials. Thinking in terms of "design with" and "design of' yield-stress fluids motivates how we can organize our scientific understanding of this field. "Design with" involves identification of rheological property requirements independent of the chemical formulation, e.g. for 3D direct-write printing which needs to accommodate a wide range of chemistry and material structures. "Design of includes microstructural considerations: conceptual models relating formulation to properties, quantitative models of formulation-structureproperty relations, and chemical transformation strategies for converting effective yield-stress fluids to be more useful solid engineering materials. Finally, future research directions are suggested at the intersection of chemistry, soft-matter physics, and material science in the context of our desire to design useful Theologically-complex functional materials.
The thrust of the present work is to explore the utility of using a sum of sinusoids (seven or more) to obtain an evoked response and, furthermore, to see if the response is sensitive to changes in cognitive processing. Within the field of automatic control system technology, a mathematical input/output relationship for a sinusoidally stimulated nonlinear system is defined as describing function. Applying this technology, sum of sines inputs to yield describing functions for the visual-cortical response have been designed. What follows is a description of the method used to obtain visual-cortical describing functions. A number of measurement system redesigns were necessary to achieve the desired frequency resolution. Results that guided and came out of the redesigns are presented. Preliminary results of stimulus parameter effects (average intensity and depth of modulation) are also shown.
Significance The ability to scrutinize the correlation of dimension, composition, and dopant to electrocatalytic performance renders the development of highly active electrocatalysts. This work reports a general and robust strategy for crafting uniform perovskite oxide nanoparticles (i.e., BaTiO 3 and La- and Co-doped BaTiO 3 ) with controlled sizes and dopant compositions by employing amphiphilic star-like diblock copolymers as nanoreactors. Their size- and dopant-dependent oxygen reduction reaction (ORR) performances are unveiled. Particularly, La- and Co-doped BaTiO 3 nanoparticles exhibit markedly improved ORR activities over the pristine counterparts due to reduced free energy barrier of ORR and increased conductivity as substantiated by density functional theory. The nanoreactor strategy could be conveniently extended to yield various functional nanomaterials of interest with tailored sizes and compositions for electrocatalysis.
Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.
Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.
Spin-echo small-angle neutron scattering (SESANS) is a unique method to measure structures of materials in real space with length scales from ∼ 30 μm to ∼20 µm. As shown in Figure 1, the accessible length scale of SESANS is given by its ability to encode the momentum transfer into the Larmor phase, namely Φ = $\vec{𝛿}$ ⋅ $\vec{𝑄}$, where $\vec{𝑄}$ is the momentum transfer and $\vec{𝛿}$ is the encoding vector of the setup and its projection along Q (δQ) is called spin-echo length (SEL). The spin echo length, which is synonymous with the spatial correlation distance probed, is defined as the following 𝛿𝑄 ∝ 𝜆 2 𝐵𝐿cot𝜃 where 𝜆 is neutron wavelength, B is magnetic field, L is length of the parallelogram magnetic field region, and 𝜃 is the angle between the inclined magnetic field boundary and the beam direction, as shown in Figure 1. The result of the SESANS experiment is a Hankel transformation of the SANS scattering function I(Q), which yields the correlation function of the sample in real space.
Excitation of neutral atoms by inelastic scattering of incident electrons in gaseous nebulae were investigated using Slater Wave functions to describe the initial and final states of the atom. Total cross sections using the Born Approximation are calculated for: Li(2s yields 2p), Na(3s yields 4p), k(4s yields 4p). The intensity of emitted radiation from gaseous nebulae is also calculated, and Maxwell distribution is employed to average the kinetic energy of electrons.
Generalized questions about shield materials are common and yet difficult to answer analytically. Most forms of quick analysis use simple attenuation. The drawback to such methods is the use of mono-energetic averaged cross sections. Averaging a cross section requires knowledge of the flux a priori. If the flux was known, then some more in-depth calculation was performed negating the need for quick scoping calculations. Source independent methods for assessing the transport medium provide a means of creating response functions that yield general statements about the effectiveness of a shielding material. The following paper uses MCNP 6.2 to generate source independent, energy dependent, response functions for the transport medium that allow for quick scoping of effectiveness of various neutron shields. The source independent method here leveraged FT SCX tally treatment, to assess the performance of various shields such that energy response is preserved. (authors)