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Near-equiatomic multi-component high-entropy alloys (HEAs) have engendered much attention of late due to the remarkable mechanical properties of some of these new metallic materials. In particular, one of the first reported HEAs, the equiatomic, single-phase, face-centered cubic (fcc) alloy CrMnFeCoNi, often termed the Cantor alloy, has been shown to display an exceptional combination of strength, ductility and fracture toughness, i.e., damage tolerance, at room temperature, properties that are further enhanced at cryogenic temperatures. Despite this alloy being the most studied HEA to date, its resistance to crack growth under cyclic fatigue loading has not yet been characterized. Here, we examine its fatigue-crack propagation behavior, primarily at lower, near-threshold, growth rates, both at room temperature (293 K) and at 198 K. At 293 K, the alloy shows a fatigue threshold, ΔK TH, of ∼4.8 MPa√m, which increases by more than 30% to ΔK TH ∼6.3 MPa√m with decrease in temperature to 198 K; additionally, the Paris exponent m was found to increase from roughly 3.5 to 4.5 with decreasing temperature. Examination of the fracture surfaces and crack paths indicate a transition from predominantly transgranular crack propagation at room temperature to intergranular-dominated failure at the lower temperature. Such a change in crack path is generally associated with an increasing degree of physical contact between the two fracture surfaces, i.e., roughness-induced fatigue crack closure, which is likely to be the main reason for the difference in the measured thresholds. Additionally, we believe that the higher thresholds found at 198 K are associated with the alloy's higher strength at lower temperatures, which both reduces the crack-tip opening displacements at a given stress-intensity range and prevents plastic deformations of the grains in the wake of the crack. At room temperature, such plastically deformed grains can be associated with a loss of contact shielding of the crack-tip through closure, resulting in a lower threshold compared to 198 K.
Polycyclic aromatic hydrocarbons (PAHs) are attractive synthetic building blocks for more complex conjugated nanocarbons, but their use for this purpose requires appreciable quantities of a PAH with reactive functional groups. Despite tremendous recent advances, most synthetic methods cannot satisfy these demands. Here we present a general and scalable [2+2+n] (n = 1 or 2) cycloaddition strategy to access PAHs that are decorated with synthetically versatile alkynyl groups and its application to seven structurally diverse PAH ring systems (thirteen new alkynylated PAHs in total). The critical discovery is the site-selectivity of an Ir-catalyzed [2+2+2] cycloaddition, which preferentially cyclizes tethered diyne units with preservation of other (peripheral) alkynyl groups. The potential for generalization of the site-selectivity to other [2+2+n] reactions is demonstrated by identification of a Cp 2 Zr-mediated [2+2+1] / metallacycle transfer sequence for synthesis of an alkynylated, selenophene-annulated PAH. The new PAHs are excellent synthons for macrocyclic conjugated nanocarbons. As a proof of concept, four were subjected to Mo catalysis to afford large, PAH-containing arylene ethylene macrocycles, which possess a range of cavity sizes reaching well into the nanometer regime. More generally, this work is a demonstration of how site-selective reactions can be harnessed to rapidly build up structural complexity in a practical, scalable fashion.
Pt nanoparticle model catalysts with 28 ± 2 nm diameters and 100 ± 2 nm square periodicity have been fabricated with electron beam lithography on silica substrates. The reactivity and selectivity of the Pt/SiO2 array favor dehydrogenation for a cyclohexene and hydrogen mixture to hydrogenation at 100 °C. Experiments with silicon deposited on Pt foil reveal the feasibility of platinum silicide formation at the Pt/SiO2 interface under reaction condition.
Physician practices that intended to join the early ACO programs had greater capabilities and experience to manage risk than those practices that decided not to join. The early ACO programs thus attracted the more capable physician practices, but those practices still fell short of implementing key recommended behaviors. The findings have implications for future physician practice selection into ACOs.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
The software "BaseBuddy" (basebuddy.lbl.gov) is a user-friendly web app designed for codon optimization of heterologous genes. Codon optimization is a widely used technique to enhance the expression levels of non-native genes. Our app is built on the DNA Chisel Python library (Zulkower and Rosser, 2020), which offers highly customizable and transparent gene optimization. Unlike DNA Chisel, which is a command-line interface software with numerous optional functions, our web app simplifies the process for users. Additionally, while DNA Chisel relies on the outdated Kazusa codon usage database, our app introduces the option to utilize the most recent version of the CoCoPUTs database (Athey et al., 2017). By incorporating CoCoPUTs, we also expand the range of target organisms and maintain up-to-date sequencing data for more accurate codon optimization results.
The first use of ether protecting groups in the design of imaging systems based on substituted poly(hydroxystyrenes) is reported. Polymers containing 4-(2-cyclohexenyloxy) or 4-(1-phenylethyloxy) derivatives of 4-vinylphenol or 3,5-dimethyl-4-vinylphenol have been prepared from the corresponding monomers. Due to their design, which allows for facile elimination or rearrangement reactions, the ether protecting groups can be removed easily by acidolysis, or thermolysis, or a combination thereof. In some instances, the protecting groups can be split quantitatively from the polymers, while in others a thermal Claisen rearrangement or an acid-catalyzed alkylation occur with the formation of some alkylated phenolic moieties. Application of the design to imaging systems is achieved through the use of triarylsulfonium salts as photochemical triggers. Exposure of films of poly[4-(2-cyclohexenyloxy)-3,5-dimethyl-styrene] containing some of the onium salt to irradiation at 254 nm results in the formation of acid in the exposed areas which catalyzes the polymer deprotection and allows for the development of images in either positive or negative mode through a differential dissolution process.
Abstract Theories of perfectly flexible elastic curves and surfaces are frequently used to describe diverse phenomena ranging from bioelasticity and fluid capillarity to rubber elasticity and the mechanics of structural networks. It is our aim here to present a treatment of the coupled response of such continua accounting for three-dimensional interactions in the presence of finite deformations and strains. A more expansive discussion of the subject of the present paper may be found in [1].
Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses).
