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). Electronic structure analyses show that VEC ≳ 9.4 electrons per formula unit enhances alloy fracture resistance due to a facile rehybridization of electronic metallic states, which activates transformation plasticity at the yield point. Our work demonstrates a reliable strategy for computationally guided and rule-based (i.e., VEC) engineering of deformation mechanisms in high entropy, solid solution, and doped ceramics.

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. Elucidating the relative impact of each parameter will suggest the most important parameters to vary during the collection of future training data. Knowing which data collection efforts to prioritize is of concern given the time required to collect or simulate vast libraries of diffraction patterns for a wide variety of materials without considering varying any parameters. In this work, five parameters, frame averaging, detector tilt, sample-to-detector distance, accelerating voltage, and pattern resolution, essential to electron diffraction are individually varied during the collection of electron backscatter diffraction patterns to explore the effect on the classifications produced by a deep neural network trained from diffraction patterns captured using a fixed set of parameters. The model is shown to be resilient to nearly all the individual changes examined here.

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. The powerful feature extractors learned previously can potentially be fine-tuned for a new classification problem without hindering performance. Transfer learning can also improve the results of training a model using a small amount of data, known as few-shot learning. Herein, we test the effectiveness of a few-shot transfer learning approach for the classification of electron backscatter diffraction (EBSD) pattern images to six space groups within the [Formula: see text] point group. Training history and performance metrics are compared with a model of the same architecture trained from scratch. In an effort to make this approach more explainable, visualization of filters, activation maps, and Shapley values are utilized to provide insight into the model's operations. The applicability to real-world phase identification and differentiation is demonstrated using dual phase materials that are challenging to analyze with traditional methods.

Bulk high-entropy nitrides and carbonitrides.

High-entropy ceramics have potential to improve the mechanical properties and high-temperature stability over traditional ceramics, and high entropy nitrides and carbonitrides (HENs and HECNs) are particularly attractive for high temperature and high hardness applications. The synthesis of 5 bulk HENs and 4 bulk HECNs forming single-phase materials is reported herein among 11 samples prepared. The hardness of HENs and HECNs increased by an average of 22% and 39%, respectively, over the rule-of-mixtures average of their monocarbide and mononitride precursors. Similarly, elastic modulus values increased by an average of 17% in nitrides and 31% in carbonitrides over their rule-of-mixtures values. The enhancement in mechanical properties is tied to an increase in the configurational entropy and a decrease in the valence electron concentration, providing parameters for tuning mechanical properties of high-entropy ceramics.

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. Aided by machine learning techniques, this new methodology is, as demonstrated herein, capable of accurately separating phases in a material by crystal symmetry, chemistry, and even lattice parameters with fewer human decisions. This work is the first demonstration of such capabilities and addresses many of the major challenges faced in modern EBSD. Diffraction patterns are collected from a variety of samples, and a convolutional neural network, a type of machine learning algorithm, is trained to autonomously recognize the subtle differences in the diffraction patterns and output phase maps of the material. This study offers a path to machine learning coupled phase mapping as databases of EBSD patterns encompass an increasing number of the possible space groups, chemistry changes, and lattice parameter variations.

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. After collection, current EBSD pattern indexing techniques (whether Hough-based or dictionary pattern matching based) are capable of reliably differentiating between a "user selected" set of phases, if those phases contain sufficiently different crystal structures. EBSD is currently less well suited for the problem of phase identification where the phases in the sample are unknown. A pattern analysis technique capable of phase identification, utilizing the information-rich diffraction patterns potentially coupled with other data, such as EDS-derived chemistry, would enable EBSD to become a high-throughput technique replacing many slower (X-ray diffraction) or more expensive (neutron diffraction) methods. We utilize a machine learning technique to develop a general methodology for the space group classification of diffraction patterns; this is demonstrated within the $\lpar 4/m\comma \;\bar{3}\comma \;\;2/m\rpar$ point group. We evaluate the machine learning algorithm's performance in real-world situations using materials outside the training set, simultaneously elucidating the role of atomic scattering factors, orientation, and pattern quality on classification accuracy.

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. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification.

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. First, the angular resolution of demons registration is demonstrated to be lower than the conventional cross-correlation based method, particularly at misorientation angles >0.157 rad. In addition, GPU acceleration has been applied to significantly boost the speed of iterative registration between a pair of patterns with 0.175 rad misorientation to under 1 s. Second, demons registration is implemented as a first-pass remapping, followed by a second pass cross-correlation method, which results in angular resolution of ~0.5 × 10-4 rad, a phantom stress value of ~35 MPa and phantom strain of ~2 × 10-4, on dynamically simulated patterns, without the need of implementing robust fitting or iterative remapping. Lastly, the new remapping method is applied to a large experimental dataset collected from an as-built additively-manufactured Inconel 625 cube, which shows significant residual stresses built-up near the large columnar grain region and regularly arranged GND structures.

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. The proposed method is the first automated approach to reconstructing spherical Kikuchi maps from experimental Kikuchi patterns. It potentially enables more accurate orientation calculation, new pattern center refinement methods, improved dictionary-based pattern matching, and phase identification in the future.

High-entropy high-hardness metal carbides discovered by entropy descriptors.

High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor-entropy forming ability-for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equally-sampled states near the ground state and quantifies configurational disorder capable of stabilizing high-entropy homogeneous phases. The methodology is applied to disordered refractory 5-metal carbides-promising candidates for high-hardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rock-salt high-entropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for high-entropy systems by rationally combining first principles with experimental synthesis and characterization.

Lightweight Open-Cell Scaffolds from Sea Urchin Spines with Superior Material Properties for Bone Defect Repair.

Sea urchin spines (Heterocentrotus mammillatus), with a hierarchical open-cell structure similar to that of human trabecular bone and superior mechanical property (compressive strength ∼43.4 MPa) suitable for machining to shape, were explored for potential applications of bone defect repair. Finite element analyses reveal that the compressive stress concentrates along the dense growth rings and dissipates through strut structures of the stereoms, indicating that the exquisite mesostructures play an important role in high strength-to-weight ratios. The fracture strength of magnesium-substituted tricalcium phosphate (ß-TCMP) scaffolds produced by hydrothermal conversion of urchin spines is about 9.3 MPa, comparable to that of human trabecular bone. New bone forms along outer surfaces of ß-TCMP scaffolds after implantation in rabbit femoral defects for one month and grows into the majority of the inner open-cell spaces postoperation in three months, showing tight interface between the scaffold and regenerative bone tissue. Fusion of beagle lumbar facet joints using a Ti-6Al-4V cage and ß-TCMP scaffold can be completed within seven months with obvious biodegradation of the ß-TCMP scaffold, which is nearly completely degraded and replaced by newly formed bone ten months after implantation. Thus, sea urchin spines suitable for machining to shape have advantages for production of biodegradable artificial grafts for bone defect repair.

High-Entropy Metal Diborides: A New Class of High-Entropy Materials and a New Type of Ultrahigh Temperature Ceramics.

Seven equimolar, five-component, metal diborides were fabricated via high-energy ball milling and spark plasma sintering. Six of them, including (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)B2, (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2, (Hf0.2Zr0.2Mo0.2Nb0.2Ti0.2)B2, (Hf0.2Mo0.2Ta0.2Nb0.2Ti0.2)B2, (Mo0.2Zr0.2Ta0.2Nb0.2Ti0.2)B2, and (Hf0.2Zr0.2Ta0.2Cr0.2Ti0.2)B2, possess virtually one solid-solution boride phase of the hexagonal AlB2 structure. Revised Hume-Rothery size-difference factors are used to rationalize the formation of high-entropy solid solutions in these metal diborides. Greater than 92% of the theoretical densities have been generally achieved with largely uniform compositions from nanoscale to microscale. Aberration-corrected scanning transmission electron microscopy (AC STEM), with high-angle annular dark-field and annular bright-field (HAADF and ABF) imaging and nanoscale compositional mapping, has been conducted to confirm the formation of 2-D high-entropy metal layers, separated by rigid 2-D boron nets, without any detectable layered segregation along the c-axis. These materials represent a new type of ultra-high temperature ceramics (UHTCs) as well as a new class of high-entropy materials, which not only exemplify the first high-entropy non-oxide ceramics (borides) fabricated but also possess a unique non-cubic (hexagonal) and layered (quasi-2D) high-entropy crystal structure that markedly differs from all those reported in prior studies. Initial property assessments show that both the hardness and the oxidation resistance of these high-entropy metal diborides are generally higher/better than the average performances of five individual metal diborides made by identical fabrication processing.

Calcium phosphate-bearing matrices induce osteogenic differentiation of stem cells through adenosine signaling.

Synthetic matrices emulating the physicochemical properties of tissue-specific ECMs are being developed at a rapid pace to regulate stem cell fate. Biomaterials containing calcium phosphate (CaP) moieties have been shown to support osteogenic differentiation of stem and progenitor cells and bone tissue formation. By using a mineralized synthetic matrix mimicking a CaP-rich bone microenvironment, we examine a molecular mechanism through which CaP minerals induce osteogenesis of human mesenchymal stem cells with an emphasis on phosphate metabolism. Our studies show that extracellular phosphate uptake through solute carrier family 20 (phosphate transporter), member 1 (SLC20a1) supports osteogenic differentiation of human mesenchymal stem cells via adenosine, an ATP metabolite, which acts as an autocrine/paracrine signaling molecule through A2b adenosine receptor. Perturbation of SLC20a1 abrogates osteogenic differentiation by decreasing intramitochondrial phosphate and ATP synthesis. Collectively, this study offers the demonstration of a previously unknown mechanism for the beneficial role of CaP biomaterials in bone repair and the role of phosphate ions in bone physiology and regeneration. These findings also begin to shed light on the role of ATP metabolism in bone homeostasis, which may be exploited to treat bone metabolic diseases.

Three-dimensional scaffolding to investigate neuronal derivatives of human embryonic stem cells.

Access to unlimited numbers of live human neurons derived from stem cells offers unique opportunities for in vitro modeling of neural development, disease-related cellular phenotypes, and drug testing and discovery. However, to develop informative cellular in vitro assays, it is important to consider the relevant in vivo environment of neural tissues. Biomimetic 3D scaffolds are tools to culture human neurons under defined mechanical and physico-chemical properties providing an interconnected porous structure that may potentially enable a higher or more complex organization than traditional two-dimensional monolayer conditions. It is known that even minor variations in the internal geometry and mechanical properties of 3D scaffolds can impact cell behavior including survival, growth, and cell fate choice. In this report, we describe the design and engineering of 3D synthetic polyethylene glycol (PEG)-based and biodegradable gelatin-based scaffolds generated by a free form fabrication technique with precise internal geometry and elastic stiffnesses. We show that human neurons, derived from human embryonic stem (hESC) cells, are able to adhere to these scaffolds and form organoid structures that extend in three dimensions as demonstrated by confocal and electron microscopy. Future refinements of scaffold structure, size and surface chemistries may facilitate long term experiments and designing clinically applicable bioassays.

Cancer cell migration within 3D layer-by-layer microfabricated photocrosslinked PEG scaffolds with tunable stiffness.

Our current understanding of 3-dimensional (3D) cell migration is primarily based on results from fibrous scaffolds with randomly organized internal architecture. Manipulations that change the stiffness of these 3D scaffolds often alter other matrix parameters that can modulate cell motility independently or synergistically, making observations less predictive of how cells behave when migrating in 3D. In order to decouple microstructural influences and stiffness effects, we have designed and fabricated 3D polyethylene glycol (PEG) scaffolds that permit orthogonal tuning of both elastic moduli and microstructure. Scaffolds with log-pile architectures were used to compare the 3D migration properties of normal breast epithelial cells (HMLE) and Twist-transformed cells (HMLET). Our results indicate that the nature of cell migration is significantly impacted by the ability of cells to migrate in the third dimension. 2D ECM-coated PEG substrates revealed no statistically significant difference in cell migration between HMLE and HMLET cells among substrates of different stiffness. However, when cells were allowed to move along the third dimension, substantial differences were observed for cell displacement, velocity and path straightness parameters. Furthermore, these differences were sensitive to both substrate stiffness and the presence of the Twist oncogene. Importantly, these 3D modes of migration provide insight into the potential for oncogene-transformed cells to migrate within and colonize tissues of varying stiffness.

Evolution of iridium-based molecular catalysts during water oxidation with ceric ammonium nitrate.

Organometallic iridium complexes have been reported as water oxidation catalysts (WOCs) in the presence of ceric ammonium nitrate (CAN). One challenge for all WOCs regardless of the metal used is stability. Here we provide evidence for extensive modification of many Ir-based WOCs even after exposure to only 5 or 15 equiv of Ce(IV) (whereas typically 100-10000 equiv are employed during WOC testing). We also show formation of Ir-rich nanoparticles (likely IrO(x)) even in the first 20 min of reaction, associated with a Ce matrix. A combination of UV-vis and NMR spectroscopy, scanning transmission electron microscopy, and powder X-ray diffraction is used. Even simple IrCl(3) is an excellent catalyst. Our results point to the pitfalls of studying Ir WOCs using CAN.

Bacterial toxin-triggered drug release from gold nanoparticle-stabilized liposomes for the treatment of bacterial infection.

We report a new approach to selectively deliver antimicrobials to the sites of bacterial infections by utilizing bacterial toxins to activate drug release from gold nanoparticle-stabilized phospholipid liposomes. The binding of chitosan-modified gold nanoparticles to the surface of liposomes can effectively prevent them from fusing with one another and from undesirable payload release in regular storage or physiological environments. However, once these protected liposomes "see" bacteria that secrete toxins, the toxins will insert into the liposome membranes and form pores, through which the encapsulated therapeutic agents are released. The released drugs subsequently impose antimicrobial effects on the toxin-secreting bacteria. Using methicillin-resistant Staphylococcus aureus (MRSA) as a model bacterium and vancomycin as a model anti-MRSA antibiotic, we demonstrate that the synthesized gold nanoparticle-stabilized liposomes can completely release the encapsulated vancomycin within 24 h in the presence of MRSA bacteria and lead to inhibition of MRSA growth as effective as an equal amount of vancomycin-loaded liposomes (without nanoparticle stabilizers) and free vancomycin. This bacterial toxin enabled drug release from nanoparticle-stabilized liposomes provides a new, safe, and effective approach for the treatment of bacterial infections. This technique can be broadly applied to treat a variety of infections caused by bacteria that secrete pore-forming toxins.

Effects of age and loading rate on equine cortical bone failure.

Although clinical bone fractures occur predominantly under impact loading (as occurs during sporting accidents, falls, high-speed impacts or other catastrophic events), experimentally validated studies on the dynamic fracture behavior of bone, at the loading rates associated with such events, remain limited. In this study, a series of tests were performed on femoral specimens obtained post-mortem from equine donors ranging in age from 6 months to 28 years. Fracture toughness and compressive tests were performed under both quasi-static and dynamic loading conditions in order to determine the effects of loading rate and age on the mechanical behavior of the cortical bone. Fracture toughness experiments were performed using a four-point bending geometry on single and double-notch specimens in order to measure fracture toughness, as well as observe differences in crack initiation between dynamic and quasi-static experiments. Compressive properties were measured on bone loaded parallel and transverse to the osteonal growth direction. Fracture propagation was then analyzed using scanning electron and scanning confocal microscopy to observe the effects of microstructural toughening mechanisms at different strain rates. Specimens from each horse were also analyzed for dry, wet and mineral densities, as well as weight percent mineral, in order to investigate possible influences of composition on mechanical behavior. Results indicate that bone has a higher compressive strength, but lower fracture toughness when tested dynamically as compared to quasi-static experiments. Fracture toughness also tends to decrease with age when measured quasi-statically, but shows little change with age under dynamic loading conditions, where brittle "cleavage-like" fracture behavior dominates.

Loading rate effects on the R-curve behavior of cortical bone.

Rising resistance curve (R-curve) behavior in bone during quasi-static experiments has demonstrated the importance of microstructural toughening mechanisms in resisting fracture. However, despite clinical bone fracture primarily occurring under dynamic loading and the significant changes in material behavior observed with increasing strain rates, there have been no previous investigations into whether crack growth resistance is maintained during dynamic fracture. Using a novel modified split-Hopkinson pressure bar coupled with a high-speed camera to measure crack propagation, we present the first evidence of rising R-curve behavior in bone under dynamic loading (∼2 × 10(5)MPam(1/2)s(-1)). Results indicate that rising R-curve behavior is maintained, although with lower crack initiation toughness and propagation resistance than observed in quasi-static experiments. Observations of crack initiation and propagation in double-notched specimens using confocal fluorescence microscopy and electron microscopy suggest that this is due to subtle differences in toughening mechanisms between quasi-static and dynamic fracture.

Templated mineralization of synthetic hydrogels for bone-like composite materials: role of matrix hydrophobicity.

Bone-mimetic mineral-polymer composite materials have several applications ranging from artificial bone grafts to scaffolds for bone tissue engineering; templated mineralization is an effective approach to fabricate such composites. In this study, we synthesized bone-like composites using synthetic hydrogels having pendant side chains terminating with carboxyl groups as a template for mineralization. The role of matrix hydrophobicity on mineralization was examined using poly(ethylene glycol) hydrogels modified with varying lengths of anionic pendant side chains (CH(2) horizontal lineCHCONH(CH(2))(n)COOH, where n = 1, 3, 5, and 7). The ability of these hydrogels to undergo templated mineralization was found to be strongly dependent upon the length of the pendant side chain as is evident from the extent of calcification and morphology of the minerals. Moreover, mineralized phases formed on the hydrogels were confirmed to resemble apatite-like structures. In addition to demonstrating the importance of material hydrophobicity as a design parameter for the development of bone-like synthetic materials, our study also provides a potential explanation for the in vitro differences between the apatite-nucleating capacity of aspartate-rich osteopontin and glutamate-rich bone sialoprotein.