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High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

Corrosion is a ubiquitous failure mode of materials. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials previously reported to be either three-dimensional or two-dimensional. However, using new tools and analysis techniques, we have realized that a more localized form of corrosion, which we call 1D wormhole corrosion, has previously been miscategorized in some situations. Using electron tomography, we show multiple examples of this 1D and percolating morphology. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy and ab initio density functional theory calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration zone, up to 100 times the equilibrium value at the melting point. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance.

The interfacial instability between a thiophosphate solid electrolyte and oxide cathodes results in rapid capacity fade and has driven the need for cathode coatings. In this work, the stability, evolution, and performance of uncoated, Li₂ZrO₃‐coated, and Li₃B₁₁O₁₈‐coated LiNi_0.5Co_0.2Mn_0.3O₂cathodes are compared using first‐principles computations and electron microscopy characterization. Li₃B₁₁O₁₈is identified as a superior coating that exhibits excellent oxidation/chemical stability, leading to substantially improved performance over cells with Li₂ZrO₃‐coated or uncoated cathodes. The chemical and structural origin of the different performance is interpreted using different microscopy techniques which enable the direct observation of the phase decomposition of the Li₂ZrO₃coating. It is observed that Li is already extracted from the Li₂ZrO₃in the first charge, leading to the formation of ZrO₂nanocrystallites with loss of protection of the cathode. After 50 cycles separated (Co, Ni)‐sulfides and Mn‐sulfides can be observed within the Li₂ZrO₃‐coated material. This work illustrates the severity of the interfacial reactions between a thiophosphate electrolyte and oxide cathode and shows the importance of using coating materials that are absolutely stable at high voltage.