Phase stability is an important design parameter for Ni-based superalloys to be used in future advanced ultra-supercritical (AUSC) power plants as exposure times in this type of environment are ...considerable. In this investigation, microstructures based on candidate alloy 263 were obtained with varying amounts of η precipitates using isothermal exposure at 800°C for times ranging from 1,000 h to 10,000 h. The effect of η phase stability on the creep properties was determined using creep specimens isothermally aged at 800°C for 8 h, 3,000 h, 5,000 h and 10,000 h prior to creep screening. The creep life was found to exponentially decrease with increasing density of η phase while the elongation to failure was found to increase. Furthermore, the minimum creep rate was related to the density of η phase; a relationship that did not depend on the alloy formulation. Modification to the Ti and Al concentrations slowed down the γ' to η transformation while doubling the γ' fraction after standard heat treatment. By modifying the Ti and Al content, and thereby improving γ' stability over η, the creep lives of specimens isothermally aged for up to 5,000 h were greater than that of the nominal alloy in its standard aged condition.
Transition metal–zeolite composites are versatile catalytic materials for a wide range of industrial and lab-scale processes. Significant advances in fabrication and characterization of well-defined ...metal centers confined in zeolite matrixes have greatly expanded the library of available materials and, accordingly, their catalytic utility. In this review, we summarize recent developments in the field from the perspective of materials chemistry, focusing on synthesis, postsynthesis modification, (operando) spectroscopy characterization, and computational modeling of transition metal–zeolite catalysts.
We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine ...learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the development of safe all-solid-state Li-ion batteries. With a predictive universal structure–property relationship for fast ion conduction not well understood, the search for new solid Li ion conductors has relied largely on trial-and-error computational and experimental searches over the last several decades. In this work, we perform a guided search of materials space with a machine learning (ML)-based prediction model for material selection and density functional theory molecular dynamics (DFT-MD) simulations for calculating ionic conductivity. These materials are screened from over 12 000 experimentally synthesized and characterized candidates with very diverse structures and compositions. When compared to a random search of materials space, we find that the ML-guided search is 2.7 times more likely to identify fast Li ion conductors, with at least a 44 times improvement in the log-average of room temperature Li ion conductivity. The F1 score of the ML-based model is 0.50, 3.5 times better than the F1 score expected from completely random guesswork. In a head-to-head competition against six Ph.D. students working in the field, we find that the ML-based model doubles the F1 score of human experts in its ability to identify fast Li-ion conductors from atomistic structure with a 1000-fold increase in speed, clearly demonstrating the utility of this model for the research community. In addition to having high predicted Li-ion conductivity, all materials reported here lack transition metals to enhance stability against reduction by the Li metal anode and are predicted to exhibit low electronic conduction, high stability against oxidation, and high thermodynamic stability, making them promising candidates for solid-state electrolyte applications on these several essential metrics.
Graphitic carbon nitride (g-C3N4) has, since 2009, attracted great attention for its activity as a visible-light-active photocatalyst for hydrogen evolution. Since it was synthesized in 1834, g-C3N4 ...has been extensively studied both catalytically and structurally. Although its 2D structure seems to have been solved, its 3D crystal structure has not yet been confirmed. This study attempts to solve the 3D structure of graphitic carbon nitride by means of X-ray diffraction and of neutron scattering. Initially, various structural models are considered and their XRD patterns compared to the measured one. After selecting possible candidates as g-C3N4 structure, neutron scattering is employed to identify the best model that describes the 3D structure of graphitic carbon nitride. Parallel chains of tri-s-triazine units organized in layers with an A–B stacking motif are found to describe the structure of the synthesized graphitic carbon nitride well. A misalignment of the layers is favorable because of the decreased π–π repulsive interlayer interactions.
MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented ...functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machine-learning models can bypass the band gap underestimation problem of local and semilocal functionals used in density functional theory (DFT) calculations, without subsequent correction using the time-consuming GW approach.
We report on the latest properties of solution spun carbon nanotube fiber (CNTF) and discuss these results in the context of the field of CNTF, as well as the broader field of high-performance ...fibers. Using high aspect ratio, high purity carbon nanotubes (CNTs), we have produced neat CNTF with an electrical conductivity of 10.9 MS/m and a tensile strength of 4.2 GPa. We find that properties for solution spun CNTF have doubled every three years since the first reports in the mid-2000s. Companies are driving up the scale and lowering the costs of CNT and CNTF production. If the recent improvement trends in properties, cost, and scale can be sustained for the next several years, CNTF will be uniquely poised for large-scale market adoption.
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Strongly coupled electronic and thermal transport behavior in thermoelectric (TE) materials has limited their figure of merit (zT). Here we provide breakthrough in decoupling TE ...parameters in n-type (Hf0.6Zr0.4)NiSn0.99Sb0.01 half-Heusler (hH) alloys through multi-scale nanocomposite architecture comprising of tungsten nanoinclusions. The tungsten nanoparticles not only assist electron injection, thereby improving electrical conductivity, but also enhance the Seebeck coefficient through energy filtering effect. The microstructure comprises of disordered phases with varying size of microstructural features, which assists in effective scattering of heat-carrying phonons over diverse mean-free-path ranges. Cumulatively, these effects are shown to result in outstanding thermoelectric performance of zTmax ∼ 1.4 at 773 K and zTavg ∼ 0.93 between 300 and 973 K. Using this material, a TE generator is demonstrated, which exhibits high power density of 13.93 W cm−2 and conversion efficiency of 10.7% under ΔT = 674 K. The fundamental material design principle for TE nanocomposites demonstrated here can be generalized and extended to other TE systems.
Understanding the effect of electrode-electrolyte interface (EEI) on the kinetics of electrode reaction is critical to design high-energy Li-ion batteries. While electrochemical impedance ...spectroscopy (EIS) is used widely to examine the kinetics of electrode reaction in Li-ion batteries, ambiguities exist in the physical origin of EIS responses for composite electrodes. In this study, we performed EIS measurement by using a three-electrode cell with a mesh-reference electrode, to avoid the effect of counter electrode impedance and artefactual responses due to asymmetric cell configuration, and composite or oxide-only working electrodes. Here we discuss the detailed assignment of impedance spectra for LiCoO2 as a function of voltage. The high-frequency semicircle was assigned to the impedance associated with ion adsorption and desorption at the electrified interface while the low-frequency semicircle was related to the charge transfer impedance associated with desolvation/solvation of lithium ions, and lithium ion intercalation/de-intercalation into/from LixCoO2. Exposure to higher charging voltages and greater hold time at high voltages led to no significant change for the high-frequency component but greater resistance and greater activation energy for the low-frequency circle. The greater charge transfer impedance was attributed to the growth of EEI layers on the charged LixCoO2 surface associated with electrolyte oxidation promoted by ethylene carbonate dehydrogenation.
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