Amorphous Boron Nanorod as an Anode Material for Lithium-Ion Batteries at Room Temperature

We report an amorphous boron nanorod anode material for lithium-ion batteries prepared through smelting non-toxic boron oxide in liquid lithium. Boron in theory can provide capacity as high as 3099 mAh g-1 by alloying with Li to form B4Li5. However, experimental studies of boron anode were rarely reported for room temperature lithium-ion batteries. Among the reported studies the electrochemical activity and cycling performance of bulk crystalline boron anode material are poor at room temperature. In this work, we utilized amorphous nanostructured one-dimensional (1D) boron material aiming at improving the electrochemical reactivity between boron and lithium ions at room temperature. The amorphous boron nanorod anode exhibited, at room temperature, a reversible capacity of 170 mAh g-1 at a current rate of 10 mA g-1 between 0.01 and 2 V. The anode also demonstrated good rate capability and cycling stability. Lithium storage mechanism was investigated by both sweep voltammetry measurements and galvanostatic intermittent titration technique (GITT). The sweep voltammetric analysis suggested that the contributions from lithium ions diffusion into boron as well as the capacitive process to the overall lithium charge storage are 57% and 43%, respectively. Results from GITT indicated that the discharge capacity at higher potentials (\u3e ~ 0.2 V vs, Li/Li+) could be ascribed to a capacitive process and at lower potentials (\u3c ~0.2 V vs, Li/Li+) to diffusion-controlled alloying reactions. Solid state nuclear magnetic resonance (NMR) measurement further confirmed that the capacity is from electrochemical reactions between lithium ions and the amorphous boron nanorod. This work provides new insights into designing nanostructured boron material for lithium-ion batteries

Elastic and Thermodynamic Properties of Cerium-Doped Yttrium Aluminum Garnets

Cerium-doped yttrium aluminum garnets (Y3-xCexAl5O12, Ce:YAGs) are promising yellow light-emitting components of solid-state white light-emitting diodes. Although there have been numerous studies examining the effects of Ce concentrations on the luminescent properties of Y3-xCexAl5O12, the impacts of Ce dopant on the elastic and thermodynamic properties are not well understood. In this work, we used resonant ultrasound spectroscopy (RUS) to determine the effects of Ce doping (0.025, 0.1, 1 at. %) on the elastic and thermodynamic properties of Y3-xCexAl5O12. The elastic moduli calculated via the Voigt–Reuss–Hill (VRH) method demonstrated that low Ce dopant concentrations (≤0.1 at. %) induced negligible effects on the elasticity of the YAG host matrix, while a high Ce concentration (1 at. %) yielded significant softening. RUS spectral analysis and SEM images suggested that the elastic softening originated from microstructural differences induced at higher Ce dopant concentrations. In addition, we demonstrated an increase in elastic anisotropy at higher Ce concentrations, which further elucidated the correlations between structure and elasticity of Y3-xCexAl5O12. Debye temperatures (θD), heat capacities (Cp), and thermal conductivities (κ) were calculated for Ce:YAGs through the relations of RUS-derived parameters (sound velocities, elastic moduli) and previously determined thermal expansion coefficients. Ce:YAG was found to have a significant reduction in θD, Cp, and κ at Ce concentrations ≥1 at. %. Lastly, extrapolation of Cp and κ to higher temperatures allowed the modeling of thermal stress experienced by Y3-xCexAl5O12 disks up to 1073.15 K

Artificial Intelligence Based Analysis of Nanoindentation Load–Displacement Data Using a Genetic Algorithm

We developed an automated tool, Nanoindentation Neo package for the analysis of nanoindentation load–displacement curves using a Genetic Algorithm (GA) applied to the Oliver-Pharr method (Oliver et al.,1992). For some materials, such as polycrystalline isotropic graphites, Least Squares Fitting (LSF) of the unload curve can produce unrealistic fit parameters. These graphites exhibit sharply peaked unloading curves not easily fit using the LSF, which tends to overestimate the indenter tip geometry parameter. To tackle this problem, we extended our general materials characterization tool Neo for EXAFS analysis (Terry et al., 2021) to fit nanoindentation data. Nanoindentation Neo automatically processes and analyzes nanoindentation data with minimal user input while producing meaningful fit parameters. GA, a robust metaheuristic method, begins with a population of temporary solutions using model parameters called chromosomes; from these we evaluate a fitness value for each solution, and select the best solutions to mix with random solutions producing the next generation. A mutation operator then modifies existing solutions by random perturbations, and the optimal solution is selected. We tested the GA method using Silica and Al reference standards. We fit samples of graphite and a high entropy alloy (HEA) consisting of BCC and FCC phases

Visualizing Complex Crystal Structures for Constructive Mathematics Experiences

We have created multiple interactive Graphic User Interfaces (GUIs) of crystal structures to improve presentation of mathematical concepts. Crystal structure is selected due to it being naturally entwined with symmetry and relevant in real life. Our GUIs contain various common crystal structures taught in introductory material science class, such as Cubic, Face Centered Cubic (FCC), Body Centered Cubic(BCC) and Perovskite. The GUIs contain different widgets and toggles for students to manipulate the environment. The widgets can adjust various structural parameters, such as atomic size, bond distance, and repetition numbers. The modified crystal structure can be 3D printed by exporting to a STL file, a standard lithography 3D printing file format. By using all of these tools in tandem, students can both gain a deeper understanding of mathematical concepts, as well as demonstrate connections of mathematics in nature

Evaluation of Adsorption and Mechanical Strength of 13X Zeolite Mixtures with Phyllosilicate Binders Using Molecular Dynamics Simulation and Positron Annihilation Spectroscopy

There is growing interest in developing zeolites with novel internal structures that have optimal adsorptive capacity and high mechanical strength, while offering advantages, such as being light weight. We integrate computational and experimental methods to explore the effect of binder/zeolite types, and weight percentages on the mechanical strength of 13X zeolite and adsorption capacities of N2, H2O, and CO2 for additive manufacturing (AM) applications with the goal of maximizing both adsorption and strength. Zeolite 13X mixtures and phyllosilicate binders (either bentonite or kaolin) are combined using molecular dynamics (MD) simulations to create structures with various binder/zeolite weight percentages. Adsorption capabilities and mechanical strength are assessed using the grand canonical Monte Carlo (GCMC) and ReaxFF modules, respectively. Our modeling shows that an optimized zeolite/binder ratio for N2 adsorption is around 15 wt% for kaolin and roughly 10 wt% for bentonite. The resulting parameters can be applied to facilitate macro-scale computational fluid dynamics (CFD) and finite element method (FEM) simulations of an AM zeolite structure. We also performed Positron Annihilation Lifetime Spectroscopy (PALS) measurements on zeolite samples to explore the effect of changes in the internal volume. The results show an inverse relationship between the free volume and the solid loading. Adding a binder changes the morphology of the zeolite-binder compound and decreases open-volume area significantly

Li-Substituted Layered Spinel Cathode Material for Sodium Ion Batteries

The O3-type layered Na(NixFeyMnz)O2 (0 \u3c x, y, z \u3c 1) cathode materials have attracted great interest in sodium ion batteries due to the abundance and cost of raw materials and their high specific capacities. However, the cycling stability and rate capability at high voltages (\u3e 4.0 V) of these materials remain an issue. In this work, we successfully synthesized a Li-substituted layered-tunneled (O3-spinel) intergrowth cathode (LS-NFM) to address these issues. The remarkable structural compatibility and connectivity of the two phases were confirmed by X-ray diffraction (XRD), selected area electron diffraction (SAED), and high-resolution transmission electron microscopy (HRTEM). The LS-NFM electrode reached a discharge capacity of 96 mAh g−1 with a capacity retention of 86% after 100 cycles at a current rate of 100 mA g−1 in a voltage window of 2.0−4.2 V. Moreover, the LS-NFM cathode exhibited an enhanced rate capability in comparison to the undoped singlephased layered cathode (NFM). The enhanced rate capability of LS-NFM can be explained by the significantly increased effective Na+ diffusivity measured by the galvanostatic intermittent titration technique (GITT) compared to the undoped control NFM cathode, which can be ascribed to the improved charge transport kinetics through shortened diffusion path by direct connection between the 3D channels in the spinel phase and 2D channels in the layered phase. The results from ex situ hard/soft X-ray adsorption spectroscopy (XAS) suggest that the capacity of the LS-NFM cathode is mainly associated with the Ni2+/Ni4+ redox couple and slightly from the Fe3+/Fe4+ redox couple. The voltage profile of the LS-NFM cathode exhibits a reversible plateau above 4.0 V, indicating great stability at high voltages and structural stabilization by the spinel phase. In addition to the substitution of various transition metals, or the modification of the stoichiometry of each transition metal, this study provides a new strategy to improve electrochemical performance of layered cathode materials for sodium ion batteries

Origins of Irreversibility in Layered NaNi\u3csub\u3e\u3ci\u3ex\u3c/i\u3e\u3c/sub\u3eFe\u3csub\u3e\u3ci\u3ey\u3c/i\u3e\u3c/sub\u3eMn\u3csub\u3e\u3ci\u3ez\u3c/i\u3e\u3c/sub\u3eO\u3csub\u3e2\u3c/sub\u3e Cathode Materials for Sodium Ion Batteries

Layered NaNixFeyMnzO2 cathode (NFM) is of great interest in sodium ion batteries because of its high theoretical capacity and utilization of abundant, low-cost, environmentally friendly raw materials. Nevertheless, there remains insufficient understanding on the concurrent local environment evolution in each transition metal (TM) that largely influences the reversibility of the cathode materials upon cycling. In this work, we investigate the reversibility of TM ions in layered NFMs with varying Fe contents and potential windows. Utilizing ex situ synchrotron X-ray absorption near-edge spectroscopy and extended X-ray absorption fine structure of precycled samples, the valence and bonding evolution of the TMs are elucidated. It is found that Mn is electrochemically inactive, as indicated by the insignificant change of Mn valence and the Mn–O bonding distance. Fe is electrochemically inactive after the first five cycles. The Ni redox couple contributes most of the charge compensation for NFMs. Ni redox is quite reversible in the cathodes with less Fe content. However, the Ni redox couple shows significant irreversibility with a high Fe content of 0.8. The electrochemical reversibility of the NFM cathode becomes increasingly enhanced with the decrease of either Fe content or with lower upper charge cutoff potential

Thermal Evolutions and Resulting Microstructural Changes in Kerogen-Rich Marcellus Shale

Shale rock is a complex geochemical system, which contains inorganic minerals and organic matter (e.g., kerogen), of which the latter possesses porous, high-molecular-weight carbon structures. The pores within organic matter hold the majority of recoverable unconventional oil and natural gas. The organic matter also provides a possible source of hydrocarbon fuel upon pyrolysis. To promote engineering developments in hydrocarbon recovery using heating methods, it is essential to have a fundamental understanding of the nature of the thermal behavior of shale. Consequently, we have investigated the thermal evolution of a shale sample from the Marcellus Formation, Pennsylvania, using a multi-faceted materials science approach, including in situ X-ray diffraction, in situ diffuse reflectance spectroscopy, thermogravimetric analysis coupled with differential scanning calorimetry and mass spectrometry, and transmission electron microscopy. Our aim was to link the naturally heterogeneous and complex chemistry of the shale with its mineralogy and thermal stability up to 900 °C. The thermally induced decomposition of organic and inorganic phases resulted in systematic changes in the shale characteristics. More specifically, kerogen underwent complex decomposition reactions between 200 and 600 °C, depending upon the heating rate and atmosphere (oxidative or inert); pyrite decomposed from 300 to 400 °C; and above 600 °C, inorganic minerals, such as carbonate and clay, broke down. These decompositions created microscopic cracks and left empty pores within the rock. Our results provide insight into the pyrolysis process of shale for hydrocarbon recovery

Analysis of Extended X-Ray Absorption Fine Structure (EXAFS) Data Using Artificial Intelligence Techniques

We have addressed the issue of improper and unreliable analysis of materials characterization data by developing an artificial intelligence based methodology that can reliably and more efficiently analyze experimental results from extended X-ray absorption fine structure (EXAFS) measurements. Such methods help address growing reproducibility problems that are slowing research progress, discouraging the quest for research excellence, and inhibiting effective technology transfer and manufacturing innovation. We have developed a machine learning system for automated analysis of EXAFS spectroscopy measurements and demonstrated its effectiveness on measurements collected at powerful, third generation synchrotron radiation facilities. Specifically, the system uses a genetic algorithm to efficiently find sets of structural parameters that lead to high quality fits of the experimental spectra. A human analyst suggests a set of chemical compounds potentially present in the sample, used as theoretical standards. The algorithm then searches the large multidimensional space of combinations of these materials to determine the set of structural paths using the theoretical standards that best reproduces the experimental data. The algorithm further calculates a goodness of fit value from the suggested standards that can be used to identify the chemical moieties present in the measured sample