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Understanding the fracture toughness of metallic glasses remains challenging. Here, the authors show that a fictive temperature controls an abrupt mechanical toughening transition in metallic glasses, and can explain the scatter in previously reported fracture toughness data.

Metal oxide resistive switches rely on the migration of oxygen vacancies and electrons under applied voltage. Here, Cho et al. use nanocomposites to control the electronic and ionic conductivities in spatially distinct channels, and fabricate memristors with high on/off ratios and reproducibility.

Transition metal dichalcogenides, by virtue of their two-dimensional structures, could provide the largest active surface for reactions with minimal materials consumed, which has long been pursued in the design of ideal catalysts. Nevertheless, their structurally perfect basal planes are typically inert; their surface defects, such as under-coordinated atoms at the surfaces or edges, can instead serve as catalytically active centers. Here we show a reaction probability > 90 % for adsorbed methanol (CH3OH) on under-coordinated Pt sites at surface Te vacancies, produced with Ar+ bombardment, on layered PtTe2 — approximately 60 % of the methanol decompose to surface intermediates CHxO (x = 2, 3) and 35 % to CHx (x = 1, 2), and an ultimate production of gaseous molecular hydrogen, methane, water and formaldehyde. The characteristic reactivity is attributed to both the triangular positioning and varied degrees of oxidation of the under-coordinated Pt at Te vacancies.Methanol on under-coordinated Pt sites at surface Te vacancies on layered PtTe2 decomposes at a probability >90 % which ultimately produces gaseous molecular hydrogen, methane, water and formaldehyde. [ABSTRACT FROM AUTHOR]

Abstract Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm−3 at an electric field of 5104 kV cm−1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

Abstract Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Abstract Transition metal dichalcogenides, by virtue of their two-dimensional structures, could provide the largest active surface for reactions with minimal materials consumed, which has long been pursued in the design of ideal catalysts. Nevertheless, their structurally perfect basal planes are typically inert; their surface defects, such as under-coordinated atoms at the surfaces or edges, can instead serve as catalytically active centers. Here we show a reaction probability > 90 % for adsorbed methanol (CH3OH) on under-coordinated Pt sites at surface Te vacancies, produced with Ar+ bombardment, on layered PtTe2 — approximately 60 % of the methanol decompose to surface intermediates CHxO (x = 2, 3) and 35 % to CHx (x = 1, 2), and an ultimate production of gaseous molecular hydrogen, methane, water and formaldehyde. The characteristic reactivity is attributed to both the triangular positioning and varied degrees of oxidation of the under-coordinated Pt at Te vacancies.

Abstract Using highly sensitive and selective in situ techniques to investigate the dynamics of intermediates formation is key to better understand reaction mechanisms. However, investigating the early stages of solid-state reactions/transformations is still challenging. Here we introduce in situ fluorescence spectroscopy to observe the evolution of intermediates during a two-step [2 + 2] photocycloaddition process in a coordination polymer platform. The structural changes and kinetics of each step under ultraviolet light irradiation versus time are accompanied by the gradual increase-decrease of intensity and blue-shift of the fluorescence spectra from the crystals. Monitoring the fluorescence behavior using a laser scanning confocal microscope can directly visualize the inhomogeneity of the photocycloaddition reaction in a single crystal. Theoretical calculations allow us to rationalize the fluorescence behavior of these compounds. We provide a convenient strategy for visualizing the solid-state photocycloaddition dynamics using fluorescence spectroscopy and open an avenue for kinetic studies of a variety of fast reactions.

Understanding whether intraspecific trait variation results from local adaptation or phenotypic plasticity is crucial to predict species responses to climate change. Here the authors investigate geographically distinct burying beetle populations, showing that photoperiodism is a locally adapted, not phenotypically plastic, trait.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.