Metal additive manufacturing processes pose a daunting challenge in defining optimal process parameters that yield porosity defect-free, dense parts. This is particularly true in refractory metals, where high melting ...
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Selecting materials for hybrid cathodes for batteries, which involve a combination of intercalation and conversion materials, has gained interest due to their combined synergistic and compromised properties that are n...
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Selecting materials for hybrid cathodes for batteries, which involve a combination of intercalation and conversion materials, has gained interest due to their combined synergistic and compromised properties that are not attainable by their homogeneous counterparts. Herein, we present a data-driven, chemistry-agnostic, and inverse material design framework for discovering hybrid cathode materials (HCMs) for metal-based batteries. This framework systematically explores the potential materials space for any given working ion, evaluates the candidate’s stability, and identifies the growth modes/adsorption of the components for a stable hybrid cathode. To demonstrate the application of the framework and its various possible outcomes, we performed a case study, for which the main design objective was to discover HCMs with an average gravimetric energy density surpassing that of the widely used high energy density NMC333 cathode material. The framework identified LiCr4GaS8-Li2S as a promising HCM that achieves an average energy density of 1,424 Wh/kg (on a lithiated cathode basis) that exceeds NMC333’s maximum theoretical energy density of 1,028 Wh/kg. The identified material has several additional desirable features: 1) possession of thermodynamically stable lithiated and delithiated intercalation and conversion phases;2) minimal volume change, upon (de)lithiation, that mitigates the high-volume change of the conversion material;3) high energy density that ameliorates the low energy density of the intercalation material;4) ability of the intercalation component to act as both a conductive additive and immobilizer of S, while simultaneously contributing to the total cathode energy density;5) the intercalation material serves as an ideal support for the soft sulfur species and finally, 6) we anticipate that the life span, self-discharge, mechanical integrity, and capacity fading are better than those of conventional Li-S batteries. The developed framework was instrumental f
The utilization of femtosecond laser pulses in synthesizing silicon nanocrystals (SiNCs) poses great potential in producing stable nanostructures with chemically pure surfaces and easy-to- manipulate optical propertie...
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The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that lear...
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This work introduces an approach to compute periodic phase diagram of micromagnetic systems by solving a periodic linearized Landau-Lifshitz-Gilbert (LLG) equation using an eigenvalue solver with the Finite Element Me...
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The flourishing field of light-powered micro/nanorotors provides promising strategies for manufacturing and biomedical needs. However, the torque of optical rotors typically arises from the momentum exchange with phot...
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Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse c...
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Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space.
We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra...
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ISBN:
(纸本)9798350369311
We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ray irradiations for aerospace studies.
Recent discoveries of high-Tc superconducting hydrides at high pressure have opened up new possibilities for improving the superconducting transition temperature (Tc) using hydrogenation. Here, a unique thermodynamic ...
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Recent discoveries of high-Tc superconducting hydrides at high pressure have opened up new possibilities for improving the superconducting transition temperature (Tc) using hydrogenation. Here, a unique thermodynamic approach is developed based on the concept of rapid thermal annealing and is adopted to study the hydrogenation effect on the superconducting properties of Nb/Pd bilayer films. Below 300∘C annealing temperatures, the Tc is enhanced from 8.77 to 9.06 K and is correlated with the compression of the Nb unit cell. A weak lattice expansion occurs at higher annealing temperatures, and the Tc is gradually suppressed. Furthermore, the rapid thermal hydrogenation affects the Tc differently depending on the substrate on which the Nb/Pd bilayers are grown. For the c-cut Al2O3 substrate, the Tc reduction starts at 250∘C, while for the r-cut Al2O3, this occurs at 350∘C. We associate these features with the elastic behavior of Nb film upon hydrogenation. A proposed model shows that the increase of Tc could be caused by the compressive stress related to the rapid nucleation of hydrides or the removal of impurities. Our discoveries provide insights into how superconductivity can be manipulated by rapid thermal hydrogenation.
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