Materials scientists often use grain size as the determining variable when correlating a ceramic’s structure with its properties. But the morphology and orientation of the grains can also significantly affect a material’s properties. Researchers in China developed a framework that can correlate a material’s structural hierarchy with its properties, and their latest paper explores the potential of linking this framework to Vickers hardness.
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The complex feedback loop between solid electrolyte interphase formation and lithium deposition means researchers have struggled to develop a general framework for understanding and predicting lithium morphology. Researchers at the University of California, Los Angeles, modified the electrodeposition process to decouple lithium deposition from interface growth and thus reveal the intrinsic deposition morphology of lithium.
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Automating experiments can help speed up the materials development process. Researchers led by North Carolina State University developed a new high-throughput automated testing system that deposits multiple samples on the same substrate, thus saving time and energy.
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Testing the durability of building materials is typically a slow, tedious, and labor-intensive process. Researchers at the University of Illinois Urbana-Champaign used computer vision to develop a fast and affordable method for testing cement durability, demonstrating the potential to improve quality control in the cement industry through automated methods.
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Verifying the purity of ultrahigh-purity materials can be a challenge. Researchers in Italy and Norway developed a new analytical protocol based on laser ablation paired with inductively coupled plasma mass spectrometry to determine the purity of silicon carbide.
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What if you could predict a material’s internal microstructure based solely on its external surface characteristics? A new deep learning method developed at Massachusetts Institute of Technology provides such a capability, and all data and codes used for the study are freely available for anyone to use through GitHub.
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The accuracy of models for predicting thermal properties of cemented carbides has been limited by dependance on unreliable conductivity data or time-consuming grain size measurements. Two researchers at a Sweden-based tooling company formulated a regression model that offers fairly accurate predictions using only reliable and readily measurable material characteristics.
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The process by which a crystal nucleates and grows within a glass during heat treatments remains a conceptually ill-understood phenomenon. Researchers in Brazil developed a nuclear magnetic resonance strategy combined with atomistic computer simulations that allowed them to shed unprecedented light on the structural changes that take place in a glass during relaxation and crystal nucleation.
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For researchers to improve the properties of carbon nanotubes grown en masse, they must first be able to measure and characterize how individual nanotubes are assembled within carbon nanotube “forests.” In a recent paper, researchers at the University of Missouri outlined a deep learning technique to segment these forests in scanning electron microscopy images.
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As marine energy technologies mature, there is a risk that companies will learn the hard way that their devices will not last long in salt water. A new multiyear study provides much-needed data on the benefits and pitfalls of about 300 different specimens built from materials commonly used in marine energy devices.
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