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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Synthesis and properties of anhydrous rare-earth phosphates, monazite and xenotime: a review

The synthesis methods, crystal structures, and properties of anhydrous monazite and xenotime (REPO 4 ) crystalline materials are summarized within this review. For both monazite and xenotime, currently available Inorganic Crystal Structure Database data were used to study the effects of incorporating different RE cations on the unit cell parameters, cell volumes, densities, and bond lengths. Domains of monazite-type and xenotime-type structures and other AXO4 compounds (A = RE; X = P, As, V) are discussed with respect to cation sizes. Reported chemical and radiation durabilities are summarized. Different synthesis conditions and chemicals used for single crystals and polycrystalline powders, as well as first-principles calculations of the structures and thermophysical properties of these minerals are also provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Sensitivity analysis of high-efficiency silicon solar-cell design parameters

Silicon solar cell design parameters were investigated to determine their bearing on cell efficiency. Among the parameters reviewed were: (1) bulk resistivity, (2) minority carrier lifetime cell thickness, (3) front junction depth, (4) front surface doping concentration, (5) front surface recombination velocity, and (6) back surface contact. The following were concluded: (1) there is good agreement between experimental and simulation results; (2) sheet material quality improvement is needed for high efficiency cells; (3) 20% cell of this design is feasible with 10 ms bulk lifetime material; and (4) for achieving efficiencies higher than 20% new cell designs including thin cells with light trapping and back surface field should be considered.

Mokashi, A. R.↗
NASA NTRS2015

Crystal-Chemical Analysis Martian Minerals in Gale Crater

The CheMin instrument on the Mars Science Laboratory rover Curiosity performed X-ray diffraction analyses on scooped soil at Rocknest and on drilled rock fines at Yellowknife Bay (John Klein and Cumberland samples), The Kimberley (Windjana sample), and Pahrump (Confidence Hills sample) in Gale crater, Mars. Samples were analyzed with the Rietveld method to determine the unit-cell parameters and abundance of each observed crystalline phase. Unit-cell parameters were used to estimate compositions of the major crystalline phases using crystal-chemical techniques. These phases include olivine, plagioclase and clinopyroxene minerals. Comparison of the CheMin sample unit-cell parameters with those in the literature provides an estimate of the chemical compositions of the major crystalline phases. Preliminary unit-cell parameters, abundances and compositions of crystalline phases found in Rocknest and Yellowknife Bay samples were reported in. Further instrument calibration, development of 2D-to- 1D pattern conversion corrections, and refinement of corrected data allows presentation of improved compositions for the above samples.

Morrison, S. M.↗
NASA NTRS1977

Epitaxial solar-cell fabrication, phase 2

Dichlorosilane (SiH2Cl2) was used as the silicon source material in all of the epitaxial growths. Both n/p/p(+) and p/n/n(+) structures were studied. Correlations were made between the measured profiles and the solar cell parameters, especially cell open-circuit voltage. It was found that in order to obtain consistently high open-circuit voltage, the epitaxial techniques used to grow the surface layer must be altered to obtain very abrupt doping profiles in the vicinity of the junction. With these techniques, it was possible to grow reproducibly both p/n/n(+) and n/p/p(+) solar cell structures having open-circuit voltages in the 610- to 630-mV range, with fill-factors in excess of 0.80 and AM-1 efficiencies of about 13%. Combinations and comparisons of epitaxial and diffused surface layers were also made. Using such surface layers, we found that the blue response of epitaxial cells could be improved, resulting in AM-1 short-circuit current densities of about 30 mA/cm sq. The best cells fabricated in this manner had AM-1 efficiency of 14.1%.

Daiello, R. V.↗
NASA NTRS1986

Identification of some key parameters limiting the performance of high-efficiency silicon solar cells

This paper presents, for the first time, a detailed sensitivity analysis of key cell parameters on silicon-cell efficiency by incorporating advanced solar cell physics in a sophisticated numerical simulation program. It delineates the true physical barriers to obtaining a high-efficiency silicon solar cell. Specific parameters presently limiting cell efficiency are identified to be the minority carrier lifetime and the recombination velocities at the front and back surfaces. Practical cell efficiencies in the vicinity of 22 percent are estimated to be attainable by using good quality silicon crystal and substantially reducing surface recombination velocities.

Mokashi, Anant R.↗
NASA NTRS2013

Crystal-Chemical Analysis of Soil at Rocknest, Gale Crater

The CheMin instrument on the Mars Science Laboratory rover Curiosity performed X-ray diffraction analysis on Martian soil [1] at Rocknest in Gale Crater. In particular, crystalline phases from scoop 5 were identified and analyzed with the Rietveld method [2]. Refined unit-cell parameters are reported in Table 1. Comparing these unit-cell parameters with those in the literature provides an estimate of the chemical composition of the crystalline phases. For instance, Fig. 1 shows the Mg-content of Fa-Fo olivine as a function of the b unit-cell parameter using literature data. Our refined b parameter is indicated by the black triangle.

Morrison, S. M.↗
DOE OSTI2024

Syntheses and crystal structures of four new d 0 transition metal tellurites

Four new d 0 transition metal tellurites, namely InVTeO 6 (1), β-InVTe 2 O 8 (2), FeVTe 2 O 8 (3), and Fe 2 MoTe 2 O 10 (4), were synthesized under hydrothermal conditions, and their crystal structures were determined using single-crystal X-ray diffraction. Compound 1 crystallizes in the noncentrosymmetric space group P2 1 2 1 2 1 (No. 19), with unit cell parameters of a = 5.0759(2) Å, b = 8.5030(3) Å, c = 11.6376(5) Å, V = 502.28(3) Å 3 , and Z = 4, while the other three compounds crystallize in centrosymmetric structures, with space group Cmca and unit cell parameters of a = 7.0633(7) Å, b = 8.962(1) Å, c = 20.162(2) Å, V = 1276.3(2) Å 3 , and Z = 8 for compound 2, space group P2 1 /n and unit cell parameters of a = 7.8901(2) Å, b = 4.9617(1) Å, c = 16.4290(3) Å, β = 93.6198(8)°, V = 641.88(2) Å 3 , and Z = 4 for compound 3, and space group Pnma and unit cell parameters of a = 8.7878(2) Å, b = 6.1327(2) Å, c = 15.2423(4) Å, V = 821.45(4) Å 3 , and Z = 4 for compound 4. Compound 3 adopts a two-dimensional (2D) sheet structure composed of FeO 6 octahedra, VO 4 tetrahedra, and TeO 4 polyhedra. The other three compounds exhibit three-dimensional (3D) framework structures: Compound 1 is built from InO 6 octahedra, VO 4 tetrahedra, TeO 4 polyhedra; Compound 2 consists of InO 6 octahedra, VO 5 square pyramids, and TeO 3 polyhedra; and Compound 4 is composed of FeO 6 octahedra, MoO 6 octahedra, and TeO 3 polyhedra. The band structures, density of states, and electron localization functions of all four compounds were calculated. The vibrational spectra were examined for compounds 1, 2, and 4. Furthermore, powder X-ray diffraction, element analysis, and thermogravimetric analysis were conducted for compound 4.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Crystal Chemistry and Structural Variations for Zircon Samples from Various Localities

This study investigates the variations of structural parameters and chemistry of a partially metamict and seven detrital zircon samples from different localities using single-crystal X-ray diffraction, synchrotron high-resolution powder X-ray diffraction, and electron-probe micro-analysis techniques. The unit-cell parameters for the eight zircon samples vary linearly with increasing unit-cell volume, V. A zircon sample from the Canadian Arctic Islands has the smallest unit-cell parameters, bond distances, ideal stoichiometric composition, unaffected by α-radiation damage, and is chemically pure. A zircon sample from Jemaa, Nigeria has the largest unit-cell parameters because of the effect of α-radiation doses received over a long time (2384 Ma). All the samples show good correlations between Zr and Si apfu (atom per formula unit) versus unit-cell volume, V. The α-radiation doses in the samples are lower than ~3.5 × 1015 α-decay events/mg. Substitutions of other cations at the Zr and Si sites control the variations of the structural parameters. Relatively large unit-cell parameters and bond distances occur because the Zr site accommodates other cations that have larger ionic radii than the Zr atom. Geological age increases the radiation doses in zircon and it is related to V.

58 GEOSCIENCES↗
DOE OSTI2024

NbZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys niobium-zirconium (Nb-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Nb and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2024

TaZr_BCC_SolidSolution_128atoms_VASP6

We performed density functional theory (DFT) calculations for body-centered-cubic (BCC) structures with 128 lattices sites of solid solution binary alloys tantalum-zirconium (Ta-Zr). The electronic structures of alloys have been calculated using Vienna Ab initio Simulation Package (VASP). Within this package the DFT approach is used to reduce many-body Schrodinger equation to set of single particle Kohn-Sham (KS) equations. The generalized electronic exchange-correlation functional is described by generalized gradient approximation with the Perdew-Burke-Ernzerhof parametrization. The electron-ion interactions is described by pseudopotentials developed within the plane-wave basis projector augmented-wave (PAW) approach \cite{PAW}. These pseudopotentials are available at the VASP portal (http://cms.mpi.univie.ac.at/vasp/). Our calculations have been run with the pseudopotentials treating s and p semi-core states as valence in case for the elements Ta and Zr. The electronic densities and potentials are expanded over plane-waves with energy cutoff of 350 eV. 2x2x2 k-mesh and normal precision were used. The alloys were modeled by supercell containing 128 randomly distributed atoms. At initial step the atoms occupy perfect bcc lattice cites. This initial structure was optimized until energy changes less than 1e-6 eV, while forces acting on atoms don't exceed 1e-2 eV/angstrom. The electron-ion interaction is described by PAW pseudopotentials. The calculations have been collected by sampling chemical compositions across the entire compositional range. The chemical compositions have been sampled by progressively changing the number of atoms per constituent by 4. For each chemical composition of binaries and ternaries, the first-principle calculations have been run for 100 randomized arrangements of the constituents on the BCC lattice sites. We collected data for a total of 3,100 randomized atomic structures over 31 chemical compositions. The calculations have been collected on NERSC-Perlmutter and OLCF-Summit using the VASP 6.3.2. The VASP calculations for every atomic structure have been performed in 2 main steps: 1. Starting from an ideal body-centered-cubic (BCC) structure, geometry optimization with low precision has been executed to perform a preliminary optimization of the atomic structure. The output for this calculations is available in the files 0.CONTCAR, 0.OUTCAR, rlx1.out. 2. Using the atomic structure resulting from the preliminary geometry optimization, a second geometry optimization has been performed using normal precision. The output for this calculations is available in the files CONTCAR, OUTCAR, rlx2.out, vaspout.h5, and vasprun.xml. Cases 1-10 have been run without generating the file 'vaspout.h5'. Every chemical composition sampled across the composition range in the dataset has its own directory. The convention used to name the directories for binary alloys is AXBY, where A and B refer to the constituents, whereas X and Y are positive integers that represent the number of atoms for each constituent and their values still sum up to 128. Each atomic structure associated with a specific chemical composition has its own sub-directory within the directory of the corresponding chemical composition. The sub-directories for each atomic structure for each chemical composition are named 'case-*', where * is a positive integer that spans all the values from 1 through 100, extremes included. The files contained in each sub-directory 'case-*' for each atomic structure are as follows: FILES contained in each subdirectory with name "case-N" where N ranges between 11 and 80, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. 0.POSCAR: input file that defines the atomic structure of a system 4. 0.CONTCAR: output file that provides the atomic positions and cell parameters after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 5. 0.OUTCAR: output file that contains detailed information about the progress of a calculation after the first geometry optimization has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.out: file with diagnostic information about the execution of the first geometry optimization with precision variable set to PREC=Low in the INCAR file 7. POSCAR: input file that defines the atomic structure of a system after the first geometry optimization has been run at low precision. This represents the input for the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. CONTCAR: output file that provides the atomic positions and cell parameters after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 9. OUTCAR: output file that contains detailed information about the progress of a calculation after the second geometry optimization has been run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.out: file with diagnostic information about the execution of the second geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system FILES contained in each subdirectory with name "case-N" where N ranges between 1 and 10 and between 81 and 100, extremes included: 1. INCAR: input file that contains various parameters and settings for controlling the behavior of the electronic structure calculations 2. KPOINTS: input file that specifies the Bloch vectors (k points) used to sample the Brillouin zone 3. {ID}.POSCAR: input file that defines the atomic structure of a system at the beginning of ID execution of geometry optimization with PREC=LOW 4. {ID}.CONTCAR: output file that provides the atomic positions and cell parameters at the end of ID execution of geometry optimization with PREC=LOW in the INCAR file 5. {ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of geometry optimization that has been run with the precision variable set to PREC=Low in the INCAR file 6. rlx1.{ID}.out: file with diagnostic information about the execution of the ID execution of the geometry optimization with precision variable set to PREC=Low in the INCAR file 7. N{ID}.POSCAR: input file that defines the atomic structure of a system after the geometry optimization run at low precision. This represents the input for the ID execution of the second geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 8. N{ID}.CONTCAR: output file that provides the atomic positions and cell parameters after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 9. N{ID}.OUTCAR: output file that contains detailed information about the progress of a calculation after the ID execution of the geometry optimization run with the precision variable set to PREC=Normal in the INCAR file 10. rlx2.{ID}.out: file with diagnostic information about the ID execution of geometry optimization with precision variable set to PREC=Normal in the INCAR file 11. vaspout.h5: hierarchical HDF5 file containing the inputs and outputs of a VASP calculation. To analyze the data in this file we recommend using py4vasp. This file is only produced if the VASP version used is compiled with HDF5 support 12. vasprun.xml: contains similar information to OUTCAR, but in an xml format. 13. CHGCAR: contains the charge density data of a system. This data is crucial for analyzing electronic structures, calculating electrostatic potential, and studying the distribution of charge in a crystal or molecular system This research is sponsored by the Artificial Intelligence Initiative as part of the Laboratory Directed Research and Development (LDRD) Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. This work used resources of the Oak Ridge Leadership Computing Facility, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725, under Directorate Discretionary awards MAT025 (Materials Science) and LRN026 (Machine Learning), and INCITE award MAT201. This work also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231, under award ERCAP0025216. REFERENCES (1) Kresse, G. & Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. review B 47, 558 (1993). (2) Kresse, G. & Hafner, J. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium. Phys. Rev. B 49, 14251 (1994) (3) Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. materials science 6, 15–50 (1996) (4) Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. review B 54, 11169 (1996) (5) Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. review b 59, 1758 (1999)

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reaction heterogeneity in practical high-energy lithium–sulfur pouch cells

The lithium–sulfur (Li–S) battery is a promising next-generation energy storage technology because of its high theoretical energy and low cost. Extensive research efforts have been made on new materials and advanced characterization techniques for mechanistic studies. However, it is uncertain how discoveries made on the material level apply to realistic batteries due to limited analysis and characterization of real high-energy cells, such as pouch cells. Evaluation of pouch cells (>1 A h) (instead of coin cells) that are scalable to practical cells provides a critical understanding of current limitations which enables the proposal of strategies and solutions for further performance improvement. Herein, we design and fabricate pouch cells over 300 W h kg -1 , compare the cell parameters required for high-energy pouch cells, and investigate the reaction processes and their correlation to cell cycling behavior and failure mechanisms. Spatially resolved characterization techniques and fluid-flow simulation reveal the impacts of the liquid electrolyte diffusion within the pouch cells. We found that catastrophic failure of high-energy Li–S pouch cells results from uneven sulfur/polysulfide reactions and electrolyte depletion for the first tens of cycles, rather than sulfur dissolution as commonly reported in the literature. The uneven reaction stems from limited electrolyte diffusion through the porous channels into the central part of thick cathodes during cycling, which is amplified both across the sulfur electrodes and within the same electrode plane. A combination of strategies is suggested to increase sulfur utilization, improve nanoarchitectures for electrolyte diffusion and reduce consumption of the electrolytes and additives.

25 ENERGY STORAGE↗
DOE OSTI2022

Serial crystallography with multi-stage merging of thousands of images

KAMO and BLEND provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending BLEND to support additional clustering options such as the use of more accurate cell distance metrics and the use of reflection-intensity correlation coefficients to infer `distances' among sets of reflections. This increases the sensitivity to differences in unit-cell parameters and allows clustering to assemble nearly complete data sets on the basis of intensity or amplitude differences. If the data sets are already sufficiently complete to permit it, one applies KAMO once and clusters the data using intensities only. When starting from incomplete data sets, one applies KAMO twice, first using unit-cell parameters. In this step, either the simple cell vector distance of the original BLEND or the more sensitive NCDist is used. This step tends to find clusters of sufficient size such that, when merged, each cluster is sufficiently complete to allow reflection intensities or amplitudes to be compared. One then uses KAMO again using the correlation between reflections with a common hkl to merge clusters in a way that is sensitive to structural differences that may not have perturbed the unit-cell parameters sufficiently to make meaningful clusters. Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which then can be merged, one hopes, to yield the electron density from a single protein form. Since these physical parameters are often largely independent of one another, it should be possible to greatly improve the efficacy of data-clustering software by using a multi-stage partitioning strategy. Here, one possible approach to multi-stage data clustering is demonstrated. The strategy is to use unit-cell clustering until the merged data are sufficiently complete and then to use intensity-based clustering. Using this strategy, it is demonstrated that it is possible to accurately cluster data sets from crystals that have subtle differences.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Possible Radiation-Induced Transition from Monazite-(Ce) to Xenotime-(Y)

This study examines two pegmatitic monazite samples (2a and 4b, these numbers are related to a previous study) to determine their crystal chemistry and effects of internal radiation damage using synchrotron high-resolution powder X-ray diffraction and electron-probe micro-analysis. Both the huttonite and cheralite substitutions are discussed. Rietveld structure refinement of sample 2a shows three different phases [2a = monazite-(Ce), 2b = monazite-(Ce), and 2c = xenotime-(Y)] with distinct structural parameters. The changes among the unit-cell parameters between the two monazite-(Ce) phases is more pronounced in the a followed by the b and c unit-cell parameters. Sample 4a is a single-phase monazite-(Sm) that contains 0.164 apfu Th. Phase 2c with space group I41/amd arises from redistribution of La, Ce, Pr, Nd, Sm, Gd, Dy, Si, and Y atoms from those in monazite (space group P21/n). A possible cause for the phase transition from monazite-(Ce) to xenotime-(Y) is α-radiation events over a long geological time. However, other chemical processes cannot be ruled out as a cause for the transition.

structural variations↗
DOE OSTI2021

Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed

The objective of the work presented is to perform a preliminary sensitivity analysis of particle-in-cell (PIC) model parameters when applied to settling bed, bubbling fluidized bed, and circulating fluidized bed simulations. These examples correspond to widely different flow conditions commonly seen in chemical engineering applications. Simulations were performed using the PIC method in the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). As part of the non-intrusive uncertainty quantification (UQ) analysis, simulation campaigns were generated using Nodeworks. Sampling locations or settings for PIC model parameters were determined using the Latin Hypercube method. Response surfaces were created using radial basis functions (RBF), and Sobol’ indices were estimated to quantify the influence of model parameters on the quantities of interest (QoI). This study marks a first step towards systematically determining optimal ranges for model parameters used in MFiX-PIC. Based on limited experience, it is expected that these values would depend strongly on flow conditions. Given the complexity of the multiphase flow systems under analysis, a non-intrusive UQ based approach is used to identify the most influential parameters in each case. This prior knowledge will help in proposing an effective design of experiments (DoE) and determine optimal parameters through techniques such as deterministic or Bayesian calibration, which will be pursued in the future.

42 ENGINEERING↗
DOE OSTI2021

Crystal Structure of an Anisotropic Pyrope Garnet That Contains Two Cubic Phases

The crystal structure of two different samples of pyrope garnet, ideally Mg 3 Al 2 Si 3 O 12 , from South Africa was refined using the Rietveld method, space group Ia3¯d, and monochromatic synchrotron high-resolution powder X-ray diffraction (HRPXRD) data. Sample 1 from Wesselton Mine is a single cubic phase and is optically isotropic. Electron-probe microanalysis (EPMA) provided an average composition {Mg 2.30 Fe 2+ 0.26 Ca 0.42 Mn 2+ 0.02 } Σ3 [Al 1.53 Fe 3+ 0.06 Cr 3+ 0.40 Ti 4+ 0.01 Fe 2+ 0.01 ] Σ2 Si 3 O 12 , which contains a significant amount of Cr cations. The unit-cell parameter (Å) and bond distances (Å) are a = 11.56197(1) Å, average = 2.2985, Al-O = 1.9101(4), and Si-O = 1.6343(3) Å. Sample 2 from De Beers Diamond Mine has an average composition {Mg 2.33 Fe 2+ 0.33 Ca 0.33 Mn 2+ 0.01 } Σ3 [Al 1.73 Fe 3+ 0.12 Cr 3+ 0.06 Ti 4+ 0.05 Fe 2+ 0.05 ] Σ2 Si 3 O 12 and is a fine-scale intergrowth of two cubic phases. The weight percentage, unit-cell parameter (Å), and bond distances (Å) for phase 2a are 62.2(1)%, a = 11.56185(1) Å, average = 2.3006, Al-O = 1.9080(4), Si-O = 1.6334(4) Å. The corresponding values for phase 2b are 37.8(1)%, a = 11.53896(1) Å, average = 2.2954, Al-O = 1.9020(6), Si-O = 1.6334(6) Å. The two cubic phases in sample 2 cause the crystal to be optically anisotropic because of strain induce birefringence. The unit-cell parameter and bond distances for sample 1 are similar to those in phase 2a.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Crystal structures and comparisons of potassium rare-earth molybdates KRE(MoO 4 ) 2 (RE = Tb, Dy, Ho, Er, Yb, and Lu)

Six potassium rare-earth molybdates KRE(MoO 4 ) 2 (RE = Tb, Dy, Ho, Er, Yb, and Lu) were synthesized by flux-assisted growth in K 2 Mo 3 O 10 . The crystal structures were determined using single-crystal X-ray diffraction data. The synthesized molybdates crystallize with the orthorhombic Pbcn space group (No. 60). Trendlines for unit-cell parameters were calculated using data from the current study. The unit-cell parameters a and c increase linearly whereas b decreases with larger RE cations, based on crystal radii. The unit-cell volumes increase linearly and the densities decrease linearly with larger RE cations. The average distances between the RE cations and the nearest O atoms increase with larger cations whereas the average distances of Mo—O and K—O do not show specific trends.

36 MATERIALS SCIENCE↗