The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the ...
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The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the process–structure–property triangle that is central to material science. There has been increasing interest to comprehensively understand and subsequently exploit process–structure–property (PSP) relationships to design processing pathways that result in tailored microstructures exhibiting optimal properties. However, unraveling process–structure–property relationships usually requires systematic and tedious combinatorial search of process and system variables to identify the microstructures that are produced. This is further complicated by the necessity to interrogate the properties of the huge set of corresponding microstructures. Motivated by this challenge, we focus on developing a generic methodology to establish and explore PSP pathways. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) with the premise that a domain expert should be able to focus on domain specific PSP problems while the highly specialized HPC/HTC knowledge needed to approach such problems should be hidden from the domain expert. Our hypothesis is that PSP exploration can be naturally formulated in terms of a standard paradigm in cloud computing, namely the MapReduce programming model. We show how reformulating PSP exploration into a MapReduce workflow enables us to take advantage of advances in cloud computing while requiring minimal specialized knowledge of HPC. We illustrate this generic approach by exploring PSP relationships relevant to organic photovoltaics. We focus on identifying microstructural traits that correlate with specific properties of the photovoltaic process: exciton generation, exciton dissociation and charge generation. We integrate a graph-based microstructure characterization tool, and a microstructure-aware
The microstructures of EB-PVD TBC's have been characterized as a function of angular position and cylinder height on stationary cylindrical surfaces in the coating chamber. The microstructures were evaluated using...
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The microstructures of EB-PVD TBC's have been characterized as a function of angular position and cylinder height on stationary cylindrical surfaces in the coating chamber. The microstructures were evaluated using x-ray diffraction for qualitative crystallographic texture as well as by optical microscopy and scanning electron microscopy for crystal column growth morphology. The results revealed several very strong crystallographic textures as well as a variety of crystal growth morphology.
Flexible sensors have attracted significant attention as they could be directly attached to/implanted into the body or incorporated into textiles to monitor human activities and give feedbacks for healthcare.A typical...
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Flexible sensors have attracted significant attention as they could be directly attached to/implanted into the body or incorporated into textiles to monitor human activities and give feedbacks for healthcare.A typical fabrication method is the direct use of intrinsically flexible active materials such as carbon nanotubes(CNTs).CNTs are generally assembled into aligned structures to extend their remarkable chemical,mechanical,and electrical properties to macroscopic scale to afford high sensing *** this review,we present the recent advance of CNT assemblies as electrodes or functional materials for flexible *** realizations of aligned CNTs are firstly investigated.A variety of flexible sensors based on the aligned CNTs are then carefully explored,with an emphasis on understanding the working mechanism for their high sensing *** main attention is later paid to comparing two main categories of flexible sensors with fiber and film *** remaining challenges are finally highlighted to offer some insights for future study.
This paper presents a novel method for continuous particle sorting and collection by using of cascade squeeze-jumping effect under microfluidic configuration. Microparticles with different sizes can be successfully se...
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This study investigates the impact of iron (III) chloride hexahydrate (FeCl3·6H2O) incorporation on the structural, thermal, and dielectric properties of poly(vinylidene fluoride-co-hexafluoropropylene) [P(VDF-HF...
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This study investigates the impact of iron (III) chloride hexahydrate (FeCl3·6H2O) incorporation on the structural, thermal, and dielectric properties of poly(vinylidene fluoride-co-hexafluoropropylene) [P(VDF-HFP)] nanocomposites, which were prepared using a solution casting method with varying filler concentrations (1–4 wt%). Scanning electron microscopy revealed a systematic increase in porosity—from 0.72% in pure P(VDF-HFP) to 27.5% at 4 wt% FeCl3·6H2O—along with increased pore size and surface heterogeneity. Atomic force microscopy confirmed enhanced surface roughness correlating with increased filler content. Fourier-transform infrared spectroscopy demonstrated a significant α-to-β phase transformation, indicating the formation of the polar β-phase with increasing FeCl3·6H2O content. X-ray diffraction analysis corroborated these findings, revealing a notable increase in crystallinity and β-phase content, with 4 wt% FeCl3·6H2O achieving the highest β-phase fraction (88.99%). Thermogravimetric analysis confirmed thermal stability up to approximately 500 °C, with a gradual shift in degradation onset attributed to FeCl3·6H2O interactions. Dielectric measurements at 10 Hz showed a remarkable enhancement in dielectric constant—from 5.62 in pure P(VDF-HFP) to 19.16 at 4 wt% FeCl3·6H2O—while maintaining a low dielectric loss (< 0.30). These improvements are attributed to the synergistic effects of FeCl3·6H2O on porosity, phase transformation, crystallinity, thermal stability, and dielectric properties. The superior performance of these nanocomposites makes them promising candidates for flexible electronics, energy storage systems, and advanced sensors.
In this study we investigated the deformation behavior of the hexagonal ordered phase a2-Ti3Al in Duplex TiAl under tensile loading. Transmission electron microscopy (TEM) revealed that the orientation relation ships ...
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In this study we investigated the deformation behavior of the hexagonal ordered phase a2-Ti3Al in Duplex TiAl under tensile loading. Transmission electron microscopy (TEM) revealed that the orientation relation ships (OR) between α2-Ti3Al and the Ll 0 ordered γ-TiAl phase are very different as compared to the OR common in fully lamellar PST TiAl. We observed deformation related dislocation activity on pyramidal slip systems in the α2-phase during post situ TEM analyses. We rationalize this observation by the possible build up of pile up stresses in γ-TiAl due to the different OR with the α2-Ti3Al phase that can possibly lead to the activation of dislocation activity on pyramidal slip systems with similarly resolved stresses in the α2-Ti3Al phase.
Additive manufacturing features rapid production of complicated shapes and has been widely employed in biomedical,aeronautical and aerospace ***,additive manufactured parts generally exhibit deteriorated fatigue resis...
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Additive manufacturing features rapid production of complicated shapes and has been widely employed in biomedical,aeronautical and aerospace ***,additive manufactured parts generally exhibit deteriorated fatigue resistance due to the presence of random defects and anisotropy,and the prediction of fatigue properties remains *** this paper,recent advances in fatigue life prediction of additive manufactured metallic alloys via machine learning models are *** on artificial neural network,support vector machine,random forest,etc.,a number of models on various systems were proposed to reveal the relationships between fatigue life/strength and defect/microstructure/*** the success,the predictability of the models is limited by the amount and quality of ***,the supervision of physical models is pivotal,and machine learning models can be well enhanced with appropriate physical ***,future challenges and directions for the fatigue property prediction of additive manufactured parts are discussed.
A bulk metallic glass forming alloy is subjected to shear flow in its supercooled state by compression of a short rod to produce a flat disk. The resulting material exhibits enhanced crystallization kinetics during is...
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A bulk metallic glass forming alloy is subjected to shear flow in its supercooled state by compression of a short rod to produce a flat disk. The resulting material exhibits enhanced crystallization kinetics during isothermal annealing as reflected in the decrease of the crystallization time relative to the nondeformed case. The transition from quiescent to shear-accelerated crystallization is linked to strain accumulated during shear flow above a critical shear rate γ̇c≈0.3 s−1 which corresponds to Péclet number, Pe∼O(1). The observation of shear-accelerated crystallization in an atomic system at modest shear rates is uncommon. It is made possible here by the substantial viscosity of the supercooled liquid which increases strongly with temperature in the approach to the glass transition. We may therefore anticipate the encounter of nontrivial shear-related effects during thermoplastic deformation of similar systems.
A combination of electron microscopy and in-situ x-ray diffraction is employed to study the thermal oxidation of brass (Cu0.7Zn 0.3 alloy) in order to elucidate the mechanism of one-dimensional growth of ZnO nanostruc...
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