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Tag: classification
  • Classifying and Benchmarking High-Entropy Alloys and Associated Materials for Electrocatalysis: A Brief Review of Best Practices

    Abstract: In light of the immense compositional diversity of high-entropy materials (HEMs) recently reported (e.g., high-entropy chalcogenides, perovskites, ceramics, etc.) and the relatively amorphous definition of High-Entropy, it is imperative that consistent material classification and benchmarking practices be employed to facilitate comparison between reported figures of merit. In this opinion, an updated form of the numerical high entropy definition is reviewed, which renders a universal entropy metric applicable to high-entropy alloys and emerging HEMs alike. Analytical methods to verify the existence of a solid-solution microstructure, elucidate atomic valence states, and probe atomic disorder are discussed with literature examples to facilitate the physical classification of HEMs. Electrocatalytic benchmarking is discussed in the context of water splitting reactions and best practices are reviewed for determining the electrocatalytically active surface area, reaction overpotential, and electrocatalyst stability.
  • Natural Language Indexing for Pedoinformatics

    Abstract: The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in non-quantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.