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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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At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Vertically oriented ZrO 2 –TiO 2 –Nb 2 O 5 –Al 2 O 3 mixed nanopatterned bioceramics on Ti6Al7Nb implant assessed by laser spallation technique

Recently, reports on the fabrication of nonstructural configuration have generated scientific interest due to increased awareness of the oxide nanotubes in the biomedical implant field and other industrial approaches. In this paper, highly ordered ZrO 2 –TiO 2 –Nb 2 O 5 –Al 2 O 3 nanotube arrays were grown by physical vapor deposition (PVD) of a zirconium layer on titanium-niobium alloy implant (IMPTi67) and succeeding anodic oxidation at a constant voltage (60 V) with exposure times ranging from 30 to 300 min. To crystallize the resulting mixed oxide nanotubes, heat treatment was also applied. The nanotubes were placed in an atmospheric furnace at temperatures ranging from 450 to 800 °C for 2 h. This treatment provided a morphological evolution was strongly influenced by the anodization time, where highly aligned bamboo-shaped oxide nanotubes (161 ± 44 nm in inside diameter and ~1 μm in length) were formed after 300 min of anodization. After annealing at temperature below 500 °C, the nano-array configuration remained stable and the average inner diameter decreased to around 40 nm. The effects of anodically fabrication time on the adhesion strength of four-phase multilayered mixed oxide nanotubes on anodized Zr/IMPTi67 surfaces assed by microscratch analysis. From the results, increasing the annealing temperature further to 800 °C fully destroyed the nanotubes and consequently the tubular arrangement was converted to a coarse grain structure. It was revealed that annealing at 450 °C for 2 h was the optimum conditions for stable nanotubular array generation, where the highest adhesion strength (shifted from 2595.59 to 2640.12 mN), microhardness (372 HV) and hydrophilicity (15 ± 1°) were recorded. It is notify that the adhesion strength of highly ordered mixed nanotubes was remarkably decreased after thermal treatment at 800 °C (1817.51 mN). Through the laser spallation analysis, the intrinsic adhesion strength of the Zr/IMPTi67 interface was found to be at 63 MPa, while the first stages of failure occurred at a stress state of 35 MPa. The adhesion of the film with the tubular structure proved to have a significantly larger adhesion strength of 246 MPa for the complete failure and 203 MPa for incipient stages of spallation. Finally, the considered surface modification can be an effective step in determining the interface strength between biomedical implants and the nanostructured coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Depicting the roles of CuO secondary phase and heat treatment in driving the magnetic and magnetocaloric features of Pr 2/3 Sr 1/3 MnO 3 manganite

In this work, we report a detailed experimental study regarding the impact of copper oxide (CuO) secondary phase and heat treatments on the structural, magnetic, and magnetocaloric properties of the near-room temperature Pr 2/3 Sr 1/3 MnO 3 (PSMO) magnetic refrigerant. Our investigations are carried out by using structural and microstructural analyses, alongside magnetization measurements. Here, the analysis of X-ray diffraction data of PSMO(95%)-CuO(5%) (PSMO-CuO) samples shows the coexistence of both CuO and PSMO phases. Further, the microstructural analysis of PSMO-CuO reveals that the addition of CuO significantly enhances the grains size. On the other hand, the added secondary phase markedly reduces the Curie temperature (T c ) from 293 K for PSMO to about 273 K for PSMO-CuO composite while increasing the magnetocaloric effect. This decrease in T c is associated with a significant change from 162° to about 156° in the Mn-OII-Mn bond angle respectively. Moreover, performed investigations regarding the role of heat treatments unveil that the observed changes in the structural and magnetic features are mainly driven by the secondary phase that modify grains size and double-exchange interactions in the PSMO compound. Interestingly, our findings demonstrate that the Curie temperature of the PSMO and accordingly its magnetocaloric effect can be tailored by adding small amounts of CuO without need to substitution on cation sites. In the light of obtained results, a multilayered refrigerant composed of PSMO and PSMO-CuO is proposed to cover the magnetic cooling temperature range close to room-temperature. The resulting entropy change remains practically constant between 273 K and 293 K. Such a behavior is highly appreciated from a practical point of view, particularly in cases where the cooling process is carried out by using the AMR and Ericsson cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PreDSLpmo: A neural network-based prediction tool for functional annotation of lytic polysaccharide monooxygenases

Lytic polysaccharide monooxygenases (LPMOs), a family of copper-dependent oxidative enzymes, boost the degradation of polysaccharides such as cellulose, chitin, and others. While experimental methods are used to validate LPMO function, a computational method that can aid experimental methods and provide fast and accurate classification of sequences into LPMOs and its families would be an important step towards understanding the breadth of contributions these enzymes make in deconstruction of recalcitrant polysaccharides. In this study, we developed a machine learning-based tool called PreDSLpmo that employs two different approaches to functionally classify protein sequences into the major LPMO families (AA9 and AA10). The first approach uses a traditional neural network or multilayer percerptron-based approach, while the second employs bi-directional long short-term memory for sequence classification. Finally, our method shows improvement in predictive power when compared with dbCAN2, an existing HMM-profile-based CAZyme predicting tool, on both validation and independent benchmark set.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Memory-optimized shift operator alternating direction implicit finite difference time domain method for plasma

Through introducing the alternating direction implicit (ADI) technique and the memory-optimized algorithm to the shift operator (SO) finite difference time domain (FDTD) method, the memory-optimized SO-ADI FDTD for nonmagnetized collisional plasma is proposed and the corresponding formulae of the proposed method for programming are deduced. In order to further the computational efficiency, the iteration method rather than Gauss elimination method is employed to solve the equation set in the derivation of the formulae. Complicated transformations and convolutions are avoided in the proposed method compared with the Z transforms (ZT) ADI FDTD method and the piecewise linear JE recursive convolution (PLJERC) ADI FDTD method. The numerical dispersion of the SO-ADI FDTD method with different plasma frequencies and electron collision frequencies is analyzed and the appropriate ratio of grid size to the minimum wavelength is given. The accuracy of the proposed method is validated by the reflection coefficient test on a nonmagnetized collisional plasma sheet. The testing results show that the proposed method is advantageous for improving computational efficiency and saving computer memory. The reflection coefficient of a perfect electric conductor (PEC) sheet covered by multilayer plasma and the RCS of the objects coated by plasma are calculated by the proposed method and the simulation results are analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues

In upscaling methods, closures for nonlinear problems present a well-known challenge. While a number of theoretical methods have been proposed for handling such closures, nonlinearities still remain a significant obstacle for many problems. In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in multiscale tissues. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used in an algorithm for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space rather than incorporating them as part of a constraint in the optimization process. Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are three outcomes of this work of particular note. 1) Our overall approach results in an upscaled nonlinear PDE. The PDE is closed using a neural network, and our approach results in the definition of the classical effectiveness factor for effecting closure. 2) We identify particular source terms for the closure problem that are important for representing the structure of the closure. These source terms involve macroscale concentrations and their gradients. We adopt these source terms to use as explicit features in the learning algorithm. We find the trained networks that include the macroscale source terms generate models that are able to predict the correction factor with increased fidelity over those that do not. 3) We find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometrical complexity of the two example tissue types. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling where the exact form of the macroscale equation remains unknown.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Ultrasonic Elastography for Nondestructive Evaluation of Dissimilar Material Joints

Dissimilar material joints or multilayered metals have become inevitable in the manufacturing industry due to the increasing demand for multifunctional materials with variable mechanical, thermal, or electrical characteristics in a single assembly. Lattice mismatch of materials at the interface of dissimilar materials leads to inferior mechanical characteristics. In particular, the mismatch in elastic properties indicated by a large initial elastic deformation is critical to determine the extent of variation in stress. However, nanoindentation, the most common and accepted technique to measure elastic modulus, is destructive, time-consuming, and can only examine mechanical properties within a limited area. A non-invasive elastographic mapping technique evaluates the mechanical properties using ultrasonic elastography to study incompressibility. The dissimilar joint between steel and copper was obtained via friction stir welding. The variation of the stress developed at the welded joint of the two different metals was evaluated from the dynamic bulk modulus map. A tensile test of the involved workpiece confirmed a good agreement with our analysis based on the dynamic bulk modulus elastographic mapping results. This study provides a rapid and non-invasive technique for the bulk metallurgic elastic modulus inspection to overcome the limitations of conventional methods.

Jin, Yuqi↗
DOE OSTI2023

A novel design of transitional layer structure between reduced activation ferritic martensitic steels and tungsten for plasma facing materials

Plasma-facing components (PFCs) are among the most critical gaps for fusion energy to establish technical and economic feasibility. Tungsten as a first wall/blanket material in PFCs requires to be integrating with reduced activation ferritic martensitic (RAFM) steels as a structural component. Currently, major drawbacks are the requirement of brazing, the formation of a brittle interface, and a large difference between the coefficients of thermal expansion of tungsten and steel. Here, a novel transitional multilayer structure was designed and investigated to join tungsten and RAFM steels using three interlayers. The composition of each interlayer was selected based on computational thermodynamics and diffusion kinetics to ensure a body-centered cubic (bcc) single-phase structure and prevent the formation of a brittle intermetallic phase region in the temperature range of 600–1150 °C. Although the transitional layer structure was designed for additive manufacturing, spark plasma sintering (SPS) as proof of concept was used to bond the individual layers. Interfaces were investigated using scanning and transmission electron microscopy methods but no layered intermetallic phase was observed. Nanoindentation maps across the interface suggest major hardness differences at the interface between tungsten and the vanadium interlayer, as well as the interface between RAFM steel and the FeCrAl interlayer.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Evaluation of the effects of neutron irradiation on first-generation corrosion mitigation coatings on SiC for accident-tolerant fuel cladding

In this work, high purity SiC and SiC/SiC composites coated with commercial TiN, Cr, CrN, or CrN/Cr multilayer coatings were irradiated in Ar or flowing PWR water in the Massachusetts Institute of Technology Nuclear Reactor Laboratory (MITR). Irradiation in Ar was performed in the core. In the water environment, identical samples were placed in one of three different locations: in-core, providing exposure to neutron damage and radiolysis-affected water; above-core, where samples were exposed to radiolysis-affected water but not neutron damage, or outside of the core, where samples were exposed to the coolant water without the effects of radiation. Radiation in Ar revealed significant cracking of all but the TiN coatings, attributed to differential swelling between the coating and substrate. Lattice swelling was not observed in any of the coatings, but 0.2% void swelling was observed in the Cr coating. All of the coatings failed during water exposures in the core. Likewise, CrN/Cr spalled in each condition. Cr was protective, except under radiation damage as a result of cracking, and TiN severely degraded in the core with no coating was found following exposure. A SiC/coating ATF cladding system is anticipated to perform adequately following improvements in coating ductility and purity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

High spatial resolution thermal conductivity mapping of SiC/SiC composites

Herein, silicon carbide (SiC) ceramic matrix composites are being investigated as a new generation of fuel cladding materials due to its higher accident tolerance compared to Zircaloy. Characterization of the thermal conductivity of SiC constituents and their interfaces and interphases in SiC/SiC composites is needed as inputs to models of cladding performance. We used time-domain thermoreflectance (TDTR) to map the thermal conductivity with a spatial resolution of 2 µm. The SiC/SiC composite is comprised of Hi-Nicalon Type S fibers in a matrix made by chemical vapor infiltration (CVI matrix). The interphase material is a pyrolytic carbon/SiC multilayer. We report thermal conductivity maps of the SiC fiber and SiC matrix at temperatures of 25 °C, 90 °C, 164 °C, and 250 °C. The fiber has a uniform and isotropic thermal conductivity of 22 W m -1 K -1 at 25 °C; the thermal conductivity of fiber is approximately independent of temperature. The matrix thermal conductivity is not uniform and varies from 50 to 120 W m -1 K -1 at 25 °C; the thermal conductivity of the matrix scales approximately with the inverse of the square root of temperature, i.e., ∝ 1/T 1/2 . The thermal conductivity of the interphase at room temperature is 6 W m -1 K -1 . The calculated spatially averaged thermal conductivity of this SiC/SiC composite at 1000°C is 29 W m -1 K -1 .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Hydrothermal Corrosion of First-Generation Dual-Purpose Coatings on Silicon Carbide for Accident-Tolerant Fuel Cladding

In this study, four different commercial coatings, TiN, Cr, CrN, and Cr/CrN (multilayer) were deposited by physical vapor deposition (PVD) on high resistivity chemical vapor deposited (CVD) SiC for the dual purpose of corrosion mitigation and fission gas hermeticity. The coatings were exposed for up to 2600 hours in flowing high temperature (288-350°C), high purity, liquid water with dissolved hydrogen (0.15-3ppm) or dissolved oxygen (1-2ppm). The Cr/CrN coatings did not adhere well and spalled quickly during exposure. In oxygenated conditions, none of the coatings were protective . CrN coatings spalled preferentially at defect sites, but reacted slowly enough in the absence of defects to potentially be chemically protective. TiN coatings fully oxidized within a few hundred hours and spalled. In hydrogen, all the coatings were chemically stable. Coating failures were identified and attributed to localized spallation, potentially related to coating defects. Coating improvements are recommended to reduce spallation and improve coating protection for future generations of dual-purpose coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Corrosion behavior of a series of combinatorial physical vapor deposition coatings on SiC in a simulated boiling water reactor environment

Here, the simulated boiling water condition corrosion properties of multilayer and combinatorial physical vapor deposition metal coatings (Zr, Cr, and/or Ti) deposited on a SiC f -SiC composite were investigated. Various compositions within the ternary system were corroded to the most suitable composition preventing underlying SiC f -SiC from further attack. Mass measurements and visual observations were conducted to identify which coatings formed protective oxides and which coatings spalled. Transmission electron microscopy was conducted to observe the microstructure of the physical vapor deposition metal layer before and after corrosion as well as identify the oxide which were forming.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Compressible battery foams to prevent cascading thermal runaway in Li-ion pouch batteries

Lithium-ion battery packs require thermal management to achieve optimum life and safety. This is becoming crucial for battery packs composed of high-energy-density cells. Pouch cells themselves achieve highest packaging efficiency but require additional structural support and thermal management when grouped into modules, especially under abusive conditions such as thermal runaway. Novel foam battery pads have demonstrated to cushion volume changes of pouch cells and are reengineered in this study to mitigate cell-to-cell thermal runaway propagation. The compressible pads are made of polyurethane foams incorporating flame-retardant additives or coatings, including intumescent and fire wall materials. Here their performances were evaluated by conducting nail penetration tests on modules composed of pouch cells at 100% state of charge (SOC), with the foams placed in between the cells. Experimental results show cascading thermal runaway was considerably delayed by polyurethane foams incorporating flame-retardant additives or coatings. Complete prevention of cascading failure was achieved with dense polyurethane foams with multilayered coatings of both fire wall and intumescent materials.

25 ENERGY STORAGE↗
DOE OSTI2016

As cast precipitation microstructures in twin-roller melt-spun Cu{sub 90}Co{sub 10} alloys

As cast Cu{sub 90}Co{sub 10} ribbons rapidly solidified by twin-roller melt spinning, exhibit special microstructure features. This processing method provides scenarios where a different phase selection takes place; coherent Co precipitates form directly from solidification, with neither a spinodal-like composition oscillation nor a discontinuously precipitated laminar phase. Samples are processed at tangential wheel speeds of 10 m/s (V10), 15 m/s (V15), 20 m/s (V20) and 30 m/s (V30). Microstructures resulting from this single step process are characterized and the hysteresis properties and the magnetoresistance effects evaluated. Samples V30 have a quite uniform density of coherent precipitates, with a narrow size distribution around 4 nm. On contrary, non-uniform precipitate distributions are found in samples cooled at lower rates; zones with a high density of coherent Co-rich precipitates are found forming colonies. These colonies are consistent with the extended compositional fluctuations occurring during very early stages in the cooling process. Samples may exhibit wide (V10) and even bimodal (V15) size distributions. Only samples V30 behave close to the ideal superparamagnetism. Samples V20 present relatively large coercivity and relative remanence and behave as an interacting superparamagnet, while the hysteresis loops of ribbons cooled at lower rates exhibit a ferromagnetic contribution in addition to the superparamagnetic-like one. This ferromagnetic component arises from blocked precipitates, larger than the upper bound size for superparamagnetic behavior at 300 K (12 nm). Room temperature magnetoresistance values associated to granular scattering units decrease as the mean precipitate size increases, but they remain below 2%, which is lower than that measured in samples annealed after rapid solidification, indicating that in this latter case contributions from the spinodally segregated matrix take place in addition to that of Co granules. - Highlights: •Microstructures of twin-rolled Cu{sub 90}Cu{sub 10} alloys are for the first time characterized. •Coherent Co-rich precipitates distribute forming dense colonies inside the grains. •No coexisting multilayer-like structures (spinodal or laminar phases) are detected. •Magnetic hysteresis arises from inter-particle interaction in these dense colonies. •Magnetoresistance level in these purely granular systems is low (1.6% at 300 K).

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microstructure evolution and mechanical performance of nickel based superalloy C1023 at elevated temperatures

Highlights: • Growth and strengthening mechanisms of carbide in superalloy C1023 were investigated. • Carbide dendrite primary arm grows selectively based on the vertex of blocky carbonitride. • Monolayer or multilayer carbide dendrite arm has certain crystal relationship with γ′ and γ. • Plastic deformation of γ hinders interior crack propagating from MC to matrix at 650 °C. • Oxidation of GBs significantly weakens the tensile strength of the superalloy at 850 °C. - Abstract: Microstructure evolution of polycrystalline nickel based superalloy C1023 casting was investigated by comparing as cast superalloy and heat treated superalloy in the paper. Influences of strengtheners and carbides on the tensile behaviors of heat treated superalloy at different temperatures (20 °C, 650 °C, and 850 °C) were studied. Resulting data from deep corrosion suggested that growth mode of scripted carbide included nucleation on the vertex of blocky carbonitride, expansion of dendrite arm in C-rich area, and enlargement of dendrite cap. Decomposition of MC carbide generated numerous octahedral M{sub 23}C{sub 6} carbide as well as nanoscale Cr-Mo-rich nuclei. Diffusion of Mo from γ to precipitates such as γ', MC, and M{sub 23}C{sub 6}, occurred during the heat treatment while irregular γ' (4 nm–500 nm) transformed into cuboidal particles. At 650 °C, no significant deterioration of tensile strength was found while plastic deformation ability of γ base was improved. Oxidation of grain boundaries (GBs) dominated the crack behavior of the superalloy at 850 °C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Study of reaction sequences during MSR synthesis of TiC by controlled ball milling of titanium and graphite

Highlights: • Insights on oxygen-bonded features formed during reactive ball milling • Evolution of off-stoichiometric TiC before ignition • Growth sequence of the TiC manifested as stacks and multilayers • Dual morphology of the MSR reaction product - Abstract: During reactive ball milling of Ti and C, mechanically induced self-propagating reaction (MSR) synthesis of TiC is reported to occur via a process including initial formation of small amounts of TiC prior to exothermic ignition, followed by MSR and the formation of stiochiometric TiC (Dorofeev et al., 2011; Oghenevweta et al., 2016 [1, 2]). However, both the chemical evolution and mechanisms of reaction and growth of TiC both prior to, during and after ignition are not fully understood. This research focuses on fundamental understanding of the reaction sequences during MSR synthesis of TiC from elemental Ti and C (graphite). Magnetically controlled ball milling of stoichiometric (1:1) powder mixtures of titanium (Ti) and graphite (C) was performed under He gas with the exothermic ignition point by determined via in-situ temperature measurement. X-ray diffraction, Scanning Transmission Electron Microscopy (STEM) with electron energy loss spectroscopy (EELS), Raman Spectroscopy and X-ray Photoelectron Spectroscopy (XPS) were employed to interpret the reaction sequences. The reaction sequence involved the following: During milling prior to ignition, severely deformed Ti, spheriodised graphite and amorphous carbon forms, and minor amounts of off-stiochiometric nano-TiC{sub 1−x} crystals. In addition to the formation of TiC{sub 1−x}, XPS revealed the formation of oxide skins coating the surface regions of the milled powders. There is also an evolution towards stiochiometric TiC with increasing milling time. Immediately after ignition, a reacted product comprising a mixture of multi-layer stacks of thin TiC plates form, in addition to a matrix comprising large TiC particles which have been liquid phase sintered under the high local heat of reaction by thin layers of unreacted Ti. MSR occurs via a dual mechanism involving rapid growth of existing TiC, plus nucleation and growth of new TiC. High spatially resolved EELS combined with STEM revealed additional information about the inhomogeneous chemical environment of the milled powders.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Facile synthesis and photocatalytic activity of La-doped BiOCl hierarchical, flower-like nano-/micro-structures

Highlights: • Novel La-doped BiOCl flower-like hierarchical films. • Greatly enhanced photocatalytic degradation effect on dyes. • Specific growth mechanism was proposed. • Photocatalysis mechanism was proposed. - Abstract: A series of La-doped BiOCl hierarchical, flower-like nano-/micro-structures (NMFs) with ginkgo-leaf-like “petals” were prepared by etching La-doped Bi{sub 2}O{sub 3} films in HCl solution at room temperature. For the La-doped Bi{sub 2}O{sub 3} films, they were fabricated through hot-dipping Bi{sub 2}O{sub 3} thin films in La{sub 2}O{sub 3} powder at 650 °C. Microstructure examination revealed that the ginkgo-leaf-like La-doped BiOCl petals have a multilayered structure consisting of several ultra-thin nanosheets with different radii due to the external-to-internal etching process. The investigation on their formation mechanism revealed that the La-doped BiOCl hierarchical NMFs grew anisotropically through Ostwald ripening mechanism. Besides, the photodegradation of rhodamine B under simulated sunlight irradiation revealed that the La-doped BiOCl hierarchical NMFs possessed higher performance than pure BiOCl sample. And the active species trapping tests indicated that the photogenerated holes were the main active radicals for the degradation of rhodamine B.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗