Valence electron concentration as key parameter to control the fracture resistance of refractory high-entropy carbides.

Although high-entropy carbides (HECs) have hardness often superior to that of parent compounds, their brittleness-a problem shared with most ceramics-has severely limited their reliability. Refractory HECs in particular are attracting considerable interest due to their unique combination of mechanical and physical properties, tunable over a vast compositional space. Here, combining statistics of crack formation in bulk specimens subject to mild, moderate, and severe nanoindentation loading with ab initio molecular dynamics simulations of alloys under tension, we show that the resistance to fracture of cubic-B1 HECs correlates with their valence electron concentration (VEC).

An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction.

Methods within the domain of artificial intelligence are gaining traction for solving a range of materials science objectives, notably the use of deep neural networks for computer vision for the analysis of electron diffraction patterns. An important component of deploying these models is an understanding of the performance as experimental diffraction conditions are varied. This knowledge can inspire confidence in the classifications over a range of operating conditions and identify where performance is degraded.

Efficient few-shot machine learning for classification of EBSD patterns.

Deep learning is quickly becoming a standard approach to solving a range of materials science objectives, particularly in the field of computer vision. However, labeled datasets large enough to train neural networks from scratch can be challenging to collect. One approach to accelerating the training of deep learning models such as convolutional neural networks is the transfer of weights from models trained on unrelated image classification problems, commonly referred to as transfer learning.

Deep Neural Network Enabled Space Group Identification in EBSD.

Electron backscatter diffraction (EBSD) is one of the primary tools in materials development and analysis. The technique can perform simultaneous analyses at multiple length scales, providing local sub-micron information mapped globally to centimeter scale. Recently, a series of technological revolutions simultaneously increased diffraction pattern quality and collection rate.

Phase Mapping in EBSD Using Convolutional Neural Networks.

The emergence of commercial electron backscatter diffraction (EBSD) equipment ushered in an era of information rich maps produced by determining the orientation of user-selected crystal structures. Since then, a technological revolution has occurred in the quality, rate detection, and analysis of these diffractions patterns. The next revolution in EBSD is the ability to directly utilize the information rich diffraction patterns in a high-throughput manner.

Crystal symmetry determination in electron diffraction using machine learning.

Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique.

Novel remapping approach for HR-EBSD based on demons registration.

In this study, the possibility of utilizing a computer vision algorithm, i.e., demons registration, to accurately remap electron backscatter diffraction patterns for high resolution electron backscatter diffraction (HR-EBSD) applications is presented.

Automated Reconstruction of Spherical Kikuchi Maps.

An automated approach to fully reconstruct spherical Kikuchi maps from experimentally collected electron backscatter diffraction patterns and overlay each pattern onto its corresponding position on a simulated Kikuchi sphere is presented in this study. This work demonstrates the feasibility of warping any Kikuchi pattern onto its corresponding location of a simulated Kikuchi sphere and reconstructing a spherical Kikuchi map of a known phase based on any set of experimental patterns. This method consists of the following steps after pattern collection: (1) pattern selection based on multiple threshold values; (2) extraction of multiple scan parameters and phase information; (3) generation of a kinematically simulated Kikuchi sphere as the "skeleton" of the spherical Kikuchi map; and (4) overlaying the inverse gnomonic projection of multiple selected patterns after appropriate pattern center calibration and refinement.

Nanomotors responsive to nerve-agent vapor plumes.

Enzyme-powered nanomotors responsive to the presence of nerve agents in the surrounding atmosphere are employed for remote detection of chemical vapor threats. Distinct changes in the propulsion behavior, associated with the partition of the sarin simulant diethyl chlorophosphate (DCP), offer reliable and rapid detection of the nerve-agent vapor threat.

Micromotor-Based Biomimetic Carbon Dioxide Sequestration: Towards Mobile Microscrubbers.

We describe a mobile CO2 scrubbing platform that offers a greatly accelerated biomimetic sequestration based on a self-propelled carbonic anhydrase (CA) functionalized micromotor. The CO2 hydration capability of CA is coupled with the rapid movement of catalytic micromotors, and along with the corresponding fluid dynamics, results in a highly efficient mobile CO2 scrubbing microsystem. The continuous movement of CA and enhanced mass transport of the CO2 substrate lead to significant improvements in the sequestration efficiency and speed over stationary immobilized or free CA platforms.

Micromotor-based on-off fluorescence detection of sarin and soman simulants.

Self-propelled micromotor-based fluorescent "On-Off" detection of nerve agents is described. The motion-based assay utilizes Si/Pt Janus micromotors coated with fluoresceinamine toward real-time "on-the-fly" field detection of sarin and soman simulants.

Micromotor-based energy generation.

A micromotor-based strategy for energy generation, utilizing the conversion of liquid-phase hydrogen to usable hydrogen gas (H2), is described. The new motion-based H2-generation concept relies on the movement of Pt-black/Ti Janus microparticle motors in a solution of sodium borohydride (NaBH4) fuel. This is the first report of using NaBH4 for powering micromotors.

Water-driven micromotors for rapid photocatalytic degradation of biological and chemical warfare agents.

Threats of chemical and biological warfare agents (CBWA) represent a serious global concern and require rapid and efficient neutralization methods. We present a highly effective micromotor strategy for photocatalytic degradation of CBWA based on light-activated TiO2/Au/Mg microspheres that propel autonomously in natural water and obviate the need for external fuel, decontaminating reagent, or mechanical agitation. The activated TiO2/Au/Mg micromotors generate highly reactive oxygen species responsible for the efficient destruction of the cell membranes of the anthrax simulant Bacillus globigii spore, as well as rapid and complete in situ mineralization of the highly persistent organophosphate nerve agents into nonharmful products.

Fully loaded micromotors for combinatorial delivery and autonomous release of cargoes.

Integrating functional self-propelled Zinc micromotors are created by coup-ling electrodeposition with hard dual-templating synthesis. The micromotors concurrently possess four robust functions including a remarkably high loading capacity, combinatorial delivery of cargoes, autonomous release of encapsulated payloads, and self-destruction. This concept could be expanded to simultaneous encapsulation of various payloads for different functionalities such as therapy, diagnostics, and imaging.

Self-propelled chemically-powered plant-tissue biomotors.

Self-propelled biocatalytic motors based on plant tissues are described. The tissue motors rely on their rich catalase activity towards biocatalytic decomposition of the H2O2 fuel and generation of the bubble thrust. These biomotors obviate the need for pure enzymes, and offer a remarkably low cost, good lifetime and thermostability.

Tunable hierarchical macro/mesoporous gold microwires fabricated by dual-templating and dealloying processes.

Tailor-made highly ordered macro/mesoporous hierarchical metal architectures have been created by combining sphere lithography, membrane template electrodeposition and alloy-etching processes. The new double-template preparation route involves the electrodeposition of Au/Ag alloy within the interstitial (void) spaces of polystyrene (PS) microspheres which are closely packed within the micropores of a polycarbonate membrane (PC), followed by dealloying of the Ag component and dissolution of the microsphere and membrane templates. The net results of combining such sphere lithography and silver etching is the creation of highly regular three-dimensional macro/mesoporous gold architecture with well-controlled sizes and shapes.