Density functional modeling and total scattering analysis of the atomic structure of a quaternary Ca
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The variation of the friction factor was measured by photon correlation spectroscopy during isothermal growth of chicken egg white lysozyme crystals. It was observed to initially increase to a maximum as crystals formed, and then to decrease to a constant value. The change in the friction factors was measured as a function of temperature at concentrations of lysozyme varying from one sufficiently low that molecular interactions were undetectable to concentrations near those at which crystals grow in solutions, in all cases the ionic strength was below that required for crystallization.
Thomson scattering measurements in high energy density experiments are often recorded using optical streak cameras. In the low-signal regime, noise introduced by the streak camera can become an important and sometimes the dominant source of measurement uncertainty. In this paper, we present a formal method of accounting for the presence of streak camera noise in our measurements. We present a phenomenological description of the noise generation mechanisms and present a statistical model that may be used to construct the covariance matrix associated with a given measurement. This model is benchmarked against simulations of streak camera images. We demonstrate how this covariance may then be used to weight fitting of the data and provide quantitative assessments of the uncertainty in the fitting parameters determined by the best fit to the data and build confidence in the ability to make statistically significant measurements in the low-signal regime, where spatial correlations in the noise become apparent. These methods will have general applicability to other measurements made using optical streak cameras.
A formal analysis of a mapping method known as scatter decomposition (SD) is presented. SD divides an irregular domain into many equal-size pieces and distributes them modularly among processors. It is shown that, if a correlation in workload is a convex function of distance, then scattering a more finely decomposed domain yields a lower average processor workload variance; if the workload process is stationary Gaussian and the correlation function decreases linearly in distance to zero and then remains zero, scattering a more finely decomposed domain yields a lower expected maximum processor workload. Finally, if the correlation function decreases linearly across the entire domain, then (among all mappings that assign an equal number of domain pieces to each processor) SD minimizes the average processor workload variance. The dependence of these results on the assumption of decreasing correlation is illustrated with cases where a coarser granularity actually achieves better load balance.
A formal analysis of a powerful mapping technique known as scatter decomposition is presented. Scatter decomposition divides an irregular computational domain into a large number of equal sized pieces, and distributes them modularly among processors. A probabilistic model of workload in one dimension is used to formally explain why, and when scatter decomposition works. The first result is that if correlation in workload is a convex function of distance, then scattering a more finely decomposed domain yields a lower average processor workload variance. The second result shows that if the workload process is stationary Gaussian and the correlation function decreases linearly in distance until becoming zero and then remains zero, scattering a more finely decomposed domain yields a lower expected maximum processor workload. Finally it is shown that if the correlation function decreases linearly across the entire domain, then among all mappings that assign an equal number of domain pieces to each processor, scatter decomposition minimizes the average processor workload variance. The dependence of these results on the assumption of decreasing correlation is illustrated with situations where a coarser granularity actually achieves better load balance.
Molecular beam scattering analysis by four parameter B-C intermolecular potential, plotting expansion coefficients against reduced curvature
Scattering phenomena are taught in introductory physics classes, and concepts such as conservation of energy and momentum are used at all levels of physics. However, applications of scattering are often not included as part of an education. Rutherford scattering, a topic of any modern physics course, is now used as a tool for materials science applications. In the absence of lab equipment for students to perform such experiments, an exercise in the analysis of scattering data may accent the useful concepts given in lectures. The objective of this exercise is to analyze data from Rutherford backscattering experiments in order to determine elemental composition.
Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.
Rain scatter and surface rain rate data were obtained from October 3, 1970 to October 2, 1971 using a bistatic radar system and tipping bucket rain gauges located in southeastern Virginia. The data showed that interference may occur between earth and terrestrial stations located within 80 km of each other either by mainlobe-to-mainlobe coupling or sidelobe-to-mainlobe coupling. The data further show that the distributions of scattering cross section per unit volume measured at several heights and estimated from surface rain gauge data were different. The density functions for the scattering cross section per unit volume values measured at a 3 km height and estimated from surface rain rate data were identical only for summer thundershowers. The data showed that the CCIR model used to estimate interference due to rain is inadequate because the cell size and change of scattering cross section per unit volume with height estimates of the model predict transmission loss values that are too low (more interference than will occur).
Structure elucidation from small-angle scattering curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods, such as the numerical approximation to the Debye equation or the use of spherical harmonics, do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail (e.g. spacing and orientation of proteins in an assembly) as well as large-scale (hundreds of nanometres) features. Owing to its speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small-angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. Here, the diameter distribution of tubes is extracted from SAXS fits, and this is quantitatively compared with distributions from electron microscopy. SAXS data are also obtained from 2D sheet-like protein assemblies, and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.
Analysis of the visible and near infrared spectra of planetary atmospheres and the multiple scattering of photons within the atmosphere are discussed. Photons detected within the spectral region are solar photons which were scattered by the gas and particles in the planetary atmosphere. An example is given for the incident and emitted fluxes for a hypothetical planet with an effective temperature of 100 K. The absorption spectrum of the planetary atmosphere is discussed in terms of the various scattering processes photons undergo within the atmosphere. Three different physical processes are considered. Rayleigh scattering and Raman scattering by the gas molecules, and scattering by any cloud or dust aerosol particles in the atmosphere. The physics of each of these processes is examined.
The primary purpose of this grant was to support analysis of the limb scattering data taken during the 1997 flight of the SOLSE (Shuttle Ozone Limb Sounding Experiment) and LORE (Limb Ozone Retrieval Experiment) instruments. Work was done to assess the performance of the retrieval algorithms for cloudy and clear sky cases in preparation for a poster presentation. The nadir-viewing data were also compared to coincident GOME (Global Ozone Monitoring Experiment) data and to radiative transfer calculations. An algorithm similar to the TOMS (Total Ozone Mapping Spectrometer) ozone retrieval algorithm was developed to infer the ozone column amount from the SOLSE nadir data. Comparison with coincident TOMS ozone column data confirmed that column ozone could be derived successfully from SOLSE nadir radiances, and also revealed significant changes in the SOLSE wavelength resolution and absolute radiometric calibration that were subsequently confirmed by comparing the post-flight instrument characterization to the pre-flight characterization, indicating a potentially useful method for monitoring instrument behavior in-flight.
Using small-angle neutron scattering, we studied the nanostructure of a prototypical D(H)-bonded network electrolyte, alkaline sodium aluminate bisalt, at concentrations up to solute volume fraction of ~0.5. Analysis of the structure factor prepeak at 0.1 < Q < 1.0 Å-1 showed that its evolution is associated with nanoscopic species distribution in water-anion network., with differences in characteristic distance d between OD- and Al(OD)4- anions related to their distinct sizes and interactions. When the solute volume fraction approached 0.5, a common/maximum value of ~4.5 Å was found for the correlation length ? that characterizes the electrostatic force in concentrated electrolyte solution before precipitation occurred. This implies that the solutions’ morphology and behavior at high concentrations may be governed by geometric factors, rather than the chemistry of the specific anion. Furthermore, the scaling of ? with concentration yielded an exponent of 1.25(1), suggesting that ? is limited to a few Å. By combining the current observations of structural heterogeneity at the nanoscale, with dynamic heterogeneity at the microscopic scale from our previous quasi-elastic neutron scattering study, we have established a structural origin of local “caging” and restricted structural relaxation processes. These local solvent-solute interactions not only control dynamics heterogeneity in concentrated electrolytes but also are responsible for crystallization processes in industrial setting, such as aluminum production and radioactive waste treatment.
Computer program calculates neutron or gamma ray first order scattering from a plane or cylindrical surface to a detector point. The SAP Codes, G-SAP and N-SAP, constitute a multiple scatter albedo model shield analysis.
For analysis of large-scale electromagnetic scattering problems, high-frequency asymptotic methods are fast but approximate, whereas low-frequency numerical methods are accurate but slow. Neither can produce an efficient and accurate solution to scattering by large bodies containing small structures. A promising approach is to combine the best features of both types of methods to produce a hybrid technique that is sufficiently fast, reasonably accurate, and applicable to a class of unsolvable problems such as the scatterers mentioned above. There are two extremes for this type of hybridization. One is simply to superimpose solutions from asymptotic and numerical methods. While this approach is most widely used in practical applications, it neglects the interactions between the two solutions, which can be significant in many problems. The other extreme is to combine an asymptotic and a numerical method in an exact manner. In this approach, the effect of a large body is included by incorporating its diffraction into the Green's function in the integral equation for the small structures, which accounts for all interactions. While this approach is accurate, it is difficult to be implemented in a general-purpose computer code because of its complex nature. A more practical approach is to develop a technique that can include all significant interactions and neglect all trivial interactions. The resulting hybrid technique can produce sufficient accuracy and can be implemented in a general-purpose computer code. In this paper, we develop a technique that combines the shooting and- bouncing-ray (SBR) method and the method of moments (MoM) to solve for the scattering by large conducting bodies with small structures mounted on their surfaces.
Deep, underground repositories are needed to isolate radioactive waste from the biosphere. Because bentonite is an integral component of many multibarrier repository systems, information on the hydraulic behavior of bentonite is crucial for modeling the long-term viability of such systems. In this paper the hydraulic behavior of bentonite samples was analyzed as a function of aggregate size, and samples were subjected to hydrothermal treatments involving contact with NaCl, KCl, and deionized water. Neutron and X-ray imaging were used to quantify water sorption into packed bentonite samples and bentonite swelling into the water column. The distance between the original clay-water interface and the wetting front was determined as a function of time. Average water uptake exhibited a square-root-of-time dependence in freshly prepared samples, but more variable rates were observed for samples previously in contact with water. The radiography was supported by small-angle neutron scattering analysis and ultra-small-angle neutron scattering analysis of aggregate size distributions and by inelastic neutron scattering to understand the physicochemical environment of the sorbed water. Results showed that hydrothermal treatment with KCl had the greatest effect of increased water transport in the bentonite, possibly as a result of the interaction of K + with smectite layers in the clay.
Achievements in advancing the state-of-the-art in the measurement, control, and analysis of electromagnetic scattering from general aerodynamic targets are summarized. The major topics associated with this study include: (1) electromagnetic scattering analysis; (2) indoor scattering measurement systems; (3) RCS control; (4) waveform processing techniques; (5) material scattering and design studies; (6) design and evaluation of standard targets; and (7) antenna studies. Progress in each of these areas is reported and related publications are listed.
As part of a larger effort, this work-in-progress reports the possible advantages of modifying conventional workflows used to generate labelled training samples and train machine learning (ML) models on them. We compare results from three different workflows using neutron scattering data analysis as the motivating application and report about 20% improvement in speedup, with no appreciable loss of model accuracy, over a baseline workflow.