Advances in short-wavelength x-ray multilayer optics: toward high-throughput multimirror systems for the wavelengths <10 nm

Author(s):  
Igor A. Artyukov ◽  
Yegor A. Bugayev ◽  
Oleksandr Yu. Devizenko ◽  
Ruslan M. Feshchenko ◽  
Tadashi Hatano ◽  
...  
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yiming Chen ◽  
Chi Chen ◽  
Chen Zheng ◽  
Shyam Dwaraknath ◽  
Matthew K. Horton ◽  
...  

AbstractThe L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.


Nano Research ◽  
2021 ◽  
Author(s):  
Olga A. Krysiak ◽  
Simon Schumacher ◽  
Alan Savan ◽  
Wolfgang Schuhmann ◽  
Alfred Ludwig ◽  
...  

AbstractDespite outstanding accomplishments in catalyst discovery, finding new, more efficient, environmentally neutral, and noble metal-free catalysts remains challenging and unsolved. Recently, complex solid solutions consisting of at least five different elements and often named as high-entropy alloys have emerged as a new class of electrocatalysts for a variety of reactions. The multicomponent combinations of elements facilitate tuning of active sites and catalytic properties. Predicting optimal catalyst composition remains difficult, making testing of a very high number of them indispensable. We present the high-throughput screening of the electrochemical activity of thin film material libraries prepared by combinatorial co-sputtering of metals which are commonly used in catalysis (Pd, Cu, Ni) combined with metals which are not commonly used in catalysis (Ti, Hf, Zr). Introducing unusual elements in the search space allows discovery of catalytic activity for hitherto unknown compositions. Material libraries with very similar composition spreads can show different activities vs. composition trends for different reactions. In order to address the inherent challenge of the huge combinatorial material space and the inability to predict active electrocatalyst compositions, we developed a high-throughput process based on co-sputtered material libraries, and performed high-throughput characterization using energy dispersive X-ray spectroscopy (EDS), scanning transmission electron microscopy (SEM), X-ray diffraction (XRD) and conductivity measurements followed by electrochemical screening by means of a scanning droplet cell. The results show surprising material compositions with increased activity for the oxygen reduction reaction and the hydrogen evolution reaction. Such data are important input data for future data-driven materials prediction.


1992 ◽  
Vol 4 (7) ◽  
pp. 2326-2337 ◽  
Author(s):  
B. J. MacGowan ◽  
L. B. Da Silva ◽  
D. J. Fields ◽  
C. J. Keane ◽  
J. A. Koch ◽  
...  

2018 ◽  
Vol 89 (11) ◽  
pp. 113111 ◽  
Author(s):  
Wolfgang Malzer ◽  
Daniel Grötzsch ◽  
Richard Gnewkow ◽  
Christopher Schlesiger ◽  
Fabian Kowalewski ◽  
...  

2018 ◽  
Vol 24 (S1) ◽  
pp. 1008-1009
Author(s):  
Wenbing Yun ◽  
Srivatsan Seshadri ◽  
Sylvia Lewis ◽  
Jeff Gelb ◽  
SH Lau ◽  
...  

Plant Methods ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Francisco E. Gomez ◽  
Geraldo Carvalho ◽  
Fuhao Shi ◽  
Anastasia H. Muliana ◽  
William L. Rooney

2013 ◽  
Vol 21 (1) ◽  
pp. 203-208 ◽  
Author(s):  
Yannick G. Spill ◽  
Seung Joong Kim ◽  
Dina Schneidman-Duhovny ◽  
Daniel Russel ◽  
Ben Webb ◽  
...  

Small-angle X-ray scattering (SAXS) is an experimental technique that allows structural information on biomolecules in solution to be gathered. High-quality SAXS profiles have typically been obtained by manual merging of scattering profiles from different concentrations and exposure times. This procedure is very subjective and results vary from user to user. Up to now, no robust automatic procedure has been published to perform this step, preventing the application of SAXS to high-throughput projects. Here,SAXS Merge, a fully automated statistical method for merging SAXS profiles using Gaussian processes, is presented. This method requires only the buffer-subtracted SAXS profiles in a specific order. At the heart of its formulation is non-linear interpolation using Gaussian processes, which provides a statement of the problem that accounts for correlation in the data.


Author(s):  
M. Herrero-Huerta ◽  
V. Meline ◽  
A. S. Iyer-Pascuzzi ◽  
A. M. Souza ◽  
M. R. Tuinstra ◽  
...  

Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.


Author(s):  
Youngchang Kim ◽  
Lance Bigelow ◽  
Maria Borovilos ◽  
Irina Dementieva ◽  
Erika Duggan ◽  
...  

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