Assessing thin films as predictors of bulk properties in high-throughput alloy design
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Engineering topics
Publications and source records attributed to Li, Nan.
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Excellent mechanical properties of nanomaterials are usually attributed to their nanoscale feature size and high-density interfaces. This work explores the effects of intrinsic interface properties on mechanical properties of nano metallic laminates (NMLs). We show that the interface strength significantly affects the mechanical properties of NMLs. Via in situ mechanical testing and characterization, we investigate the deformation behavior and concurrent microstructure evolution in NMLs with four types of dissimilar interfaces. Our results show that the strength and deformation behaviors of NMLs are largely determined by interface strength. Strong interfaces can simultaneously increase the strength, work hardening capability, and deformability of NMLs. In conclusion, this work brings new insight into the design of advanced metallic materials through tuning of interface properties.
Modeling time-series data with convolutional neural networks (CNNs) requires building a model to learn in batches as opposed to training sequentially. Coupling CNNs with in situ or operando techniques opens the possibility of accurately segmenting dynamic reactions and mass transport phenomena to understand how materials behave under the conditions in which they are used. In this article, in situ ion irradiation transmission electron microscopy (TEM) images are used as inputs into the CNN to assess the defect generation rate, defect cluster density, and saturation of defects. We then use the output segmentation maps to correlate with conventional TEM micrographs to assess the model’s ability to detail nanoscale interactions. Next, we discuss the implications of preprocessing and hyperparameters on model variability, accuracy when expanded to other datasets, and the role of regularization when controlling model variance. Ultimately, we eliminate human bias when extrapolating physical metrics, speed up analysis time, decouple reactions that happen at 100 ms intervals, and deploy models that are both accurate and transferable to similar experiments.
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Introduction Bioterrorism is an important issue in the field of biosecurity, and effectively dealing with bioterrorism has become an urgent task worldwide. Healthcare workers are considered bioterrorism first responders, who shoulder essential responsibilities and must be equipped to deal with bioterrorism. This study aims to extract and summarize the main research components of the bioterrorism knowledge, attitude, and practice dimensions among healthcare workers. Method This study utilized a systematic review research design based on the PRISMA 2020 guidelines. A literature search was conducted in the PubMed, Web of Science, and Scopus databases for peer-reviewed literature, and the Mixed Methods Appraisal Tool (MMAT) version 2018 was used to assess the quality of the literature. Result A total of 16 studies were included in the final selection. Through the analysis and summary of the included studies, three main aspects and 14 subaspects of the knowledge dimension, three main aspects and 10 subaspects of the attitude dimension, and two main aspects and six subaspects of the practice dimension were extracted. Conclusion This study conducted a literature review on bioterrorism knowledge, attitudes, and practices for healthcare workers based on the PRISMA 2020 guidelines. The findings can guide improvements in health literacy and provide beneficial information to professional organizations that need to respond effectively to bioterrorism.
Modulating the segmental order in the morphology of conjugated polymers is widely recognized as a crucial factor for achieving optimal electronic properties and mechanical deformability. However, it is worth noting that the segmental order is typically associated with the crystallization process, which can result in rigid and brittle long-range ordered crystalline domains. To precisely control the morphology, a comprehensive understanding of how highly anisotropic conjugated polymers form segmentally ordered structures with ongoing crystallization is essential, yet currently elusive. To fill this knowledge gap, we developed a novel approach with a combination of stage-type fast scanning calorimetry and micro-Raman spectroscopy to capture the series of specimens with a continuum in the polymer percent crystallinity and detect the segmental order in real-time. Through the investigation of conjugated polymers with different backbones and side-chain structures, we observed a generally existing phenomenon that the degree of segmental order saturates before the maximum crystallinity is achieved. This disparity allows the conjugated polymers to achieve good charge carrier mobility while retaining good segmental dynamic mobility through the tailored treatment. Moreover, the crystallization temperature to obtain optimal segmental order can be predicted based on T g and T m of conjugated polymers. In conclusion, this in-depth characterization study provides fundamental insights into the evolution of segmental order during crystallization, which can aid in designing and controlling the optoelectronic and mechanical properties of conjugated polymers.
Kinking is an important and plasticity-enhancing deformation/failure mode in numerous mechanically anisotropic materials including high-strength nano metallic laminates (NMLs). However, our current limited understanding of the mechanics of kinking and its dependence on microstructural attributes is insufficient for thoroughly comprehending and eventually being able to control failure behaviors of materials. In this study, we investigate kinking dependencies on microstructural attributes in NMLs via in situ micropillar compression, multiscale microstructure characterization, dislocation dynamic simulations, and crystal plasticity modeling. Additionally, by examining several NML systems (Cu/Fe, Ag/Fe, Al-4Mg/Fe), we demonstrate that the development of internal stresses during loading activates local layer-parallel glide triggering kinking in NMLs. Furthermore, this work reveals the effect of key microstructural features including layer thickness, layer waviness, interface barrier strength, and work hardening capacity on kink band formation in NMLs. More broadly, our efforts represent a generically applicable approach for probing large-strain deformation behavior of complex materials via synergetic modeling and experimental efforts.
The use of bioelectronic devices relies on direct contact with soft biotissues. For transistor-type bioelectronic devices, the semiconductors that need to have direct interfacing with biotissues for effective signal transduction do not adhere well with wet tissues, thereby limiting the stability and conformability at the interface. Here we report a bioadhesive polymer semiconductor through a double-network structure formed by a bioadhesive brush polymer and a redox-active semiconducting polymer. The resulting semiconducting film can form rapid and strong adhesion with wet tissue surfaces together with high charge-carrier mobility of ~1 square centimeter per volt per second, high stretchability, and good biocompatibility. Further fabrication of a fully bioadhesive transistor sensor enabled us to produce high-quality and stable electrophysiological recordings on an isolated rat heart and in vivo rat muscles.
Estimating soil organic carbon (SOC) stocks in agricultural fields is essential for environmental and agronomic research, management, and policy. Stratified sampling is a classic strategy for estimating mean soil properties, and has recently been codified in SOC monitoring protocols. However, for the specific task of estimating the SOC stock of an agricultural field, concrete guidance is needed for which covariates to stratify on and how much stratification can improve estimation efficiency. It is also unknown how stratified sampling of SOC stocks compares to modern alternatives, notably doubly balanced sampling. To address these gaps, we collected high-density (average of 7 samples ha -1 ) and deep (average of 75 cm) measurements of SOC stocks at eight commercial fields under maize-soybean production in two US Midwestern states. We combined these measurements with a Bayesian geostatistical model to evaluate stratified and balanced sampling strategies that use a set of readily-available geographic, topographic, spectroscopic, and soil survey data. We examined the number of samples needed to achieve a given level of SOC stock estimation accuracy. While stratified sampling using these variables enables an average sample size reduction of 17% (95% CI, 11% to 23%) compared to simple random sampling, doubly balanced sampling is consistently more efficient, reducing sample sizes by 32% (95% CI, 25% to 37%). The data most important to these efficiency gains are a remotely-sensed SOC index, SSURGO estimates of SOC stocks, and the topographic wetness index. We conclude that in order to meet the urgent challenge of climate change, SOC stocks in agricultural fields could be more efficiently estimated by taking advantage of this readily-available data, especially with doubly balanced sampling.
Soft and stretchable electronics have emerged as highly promising tools for biomedical diagnosis and biological studies, as they interface intimately with the human body and other biological systems. Most stretchable electronic materials and devices, however, still have Young’s moduli orders of magnitude higher than soft bio-tissues, which limit their conformability and long-term biocompatibility. Here, we present a design strategy of soft interlayer for allowing the use of existing stretchable materials of relatively high moduli to versatilely realize stretchable devices with ultralow tissue-level moduli. We have demonstrated stretchable transistor arrays and active-matrix circuits with moduli below 10 kPa—over two orders of magnitude lower than the current state of the art. Benefiting from the increased conformability to irregular and dynamic surfaces, the ultrasoft device created with the soft interlayer design realizes electrophysiological recording on an isolated heart with high adaptability, spatial stability, and minimal influence on ventricle pressure. In vivo biocompatibility tests also demonstrate the benefit of suppressing foreign-body responses for long-term implantation. With its general applicability to diverse materials and devices, this soft-interlayer design overcomes the material-level limitation for imparting tissue-level softness to a variety of bioelectronic devices.
Kink banding is a common, though not well understood, failure mechanism in anisotropic materials such as nano metallic laminates (NMLs). In this work, we investigate the effect of annealing on kink band (KB) formation in Ag/Fe NMLs prepared by accumulative roll bonding (ARB) using in situ micropillar compression, scanning/transmission electron microscopy (S/TEM), and transmission Kikuchi diffraction (TKD) analyses. Our results show that annealing increases the KB initiation strain, decreases the probability of KB formation, and decreases the load drop magnitude accompanying kink banding in Ag/Fe NMLs. Post-compression analyses reveal that annealing facilitates more uniform deformation of pillars and affects the formation of geometrically necessary grain boundaries (GNBs) near the kink band boundary (KBB). Compared to its as-rolled counterpart, annealed Ag/Fe has wider KBs with blunter KBBs.
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Damage from low-temperature irradiation and the subsequent degradation of materials performance pose significant challenges for the storage of radioactive materials and for peripheral components in some nuclear reactor designs. Fully understanding the mechanical behavior of such materials requires test data for strain rates in both the quasi-static (< 10/s) and dynamic (>> 10/s) regimes. While dynamic testing has generally been avoided in the past for neutron irradiated (contamination concerns) and ion irradiated (insufficient volume) materials, surface-sensitive Richtmyer-Meshkov instability (RMI) tests were used in the present work to overcome these limitations. Here, nanopillar compression, nanoindentation, and RMI testing data from a helium implanted surface layer (~10 µm thick) were compiled to explore the effects of helium bubbles on the materials strength of high-purity copper at strain rates of 0.001/s – 10 8 /s. While nano-mechanical testing revealed increases in yield strength and hardness with increasing helium dose from 1000 to 4000 appm He, RMI indicated no significant changes in strength as compared to unimplanted copper. Here, this discrepancy in behavior was rationalized through a combination of recent literature and follow-on molecular dynamics (MD) simulations, leading to the conclusion that the nanoscale helium bubbles acting as dispersed barriers to dislocation motion at quasi-static strain rates collapse under shock loading and cease to be effective barriers at high strain rates.
We report nanoindentation performed with a conospherical tip on the (100) face of cytosine monohydrate (CM) revealed a highly anisotropic response over a range of loads. Post-indent atomic force microscopy images identified an asymmetric deformation response owing to the pro-chiral structure of the surface. Activation of low rugosity slip planes induces movement of π-stacks rather than their displacement along the 1-dimensional hydrogen bonded ribbon direction. Anisotropy arises because slip can only propagate to one side of the indent, as the tip itself imparts a barrier to slip on the preferred plane thereby forcing the activation of secondary slip systems and pileup. The anisotropic deformation is of interest in relation to previous work which proposed a ribbon–rotation model to account for the topotactic conversion between CM and the product of its dehydration. The asymmetry in the nanomechanical properties exhibited by CM provides further support for the rotational model put forth and also serves to underscore the inherent relationship between a hydrate's mechanical properties and its solid state dehydration mechanism.
The Hubble tension, revealed by a ~5σ discrepancy between measurements of the Hubble-Lemaitre constant among observations of the early and local Universe, is one of the most significant problems in modern cosmology. In order to better understand the origin of this mismatch, independent techniques to measure H 0 , such as strong lensing time delays, are required. Notably, the sample size of such systems is key to minimising the statistical uncertainties and cosmic variance, which can be improved by exploring the datasets of large-scale sky surveys such as Dark Energy Spectroscopic Instrument (DESI). We identify possible strong lensing time-delay systems within DESI by selecting candidate multiply imaged lensed quasars from a catalogue of 24 440 816 candidate QSOs contained in the ninth data release of the DESI Legacy Imaging Surveys (DESI-LS). Using a friend-of-friends-like algorithm on spatial co-ordinates, our method generates an initial list of compact quasar groups. This list is subsequently filtered using a measure of the similarity of colours among a group’s members and the likelihood that they are quasars. A visual inspection finally selects candidate strong lensing systems based on the spatial configuration of the group members. We identified 620 new candidate multiply imaged lensed quasars (101 grade-A, 214 grade-B, 305 grade-C). This number excludes 53 known spectroscopically confirmed systems and existing candidate systems identified in other similar catalogues. When available, these new candidates will be further checked by combining the spectroscopic and photometric data from DESI.