Multicomponent alloys with high entropy of mixing,e.g.,high entropy alloys(HEAs)and/or multiprincipal-element alloys(MEAs),are attracting increasing attentions,because the materials with novel properties are being...
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Multicomponent alloys with high entropy of mixing,e.g.,high entropy alloys(HEAs)and/or multiprincipal-element alloys(MEAs),are attracting increasing attentions,because the materials with novel properties are being developed,based on the design strategy of the equiatomic ratio,multicomponent,and high entropy of mixing in their liquid or random solution ***,HEAs with the ultrahigh strength and fracture toughness,excellent magnetic properties,high fatigue,wear and corrosion resistance,great phase stability/high resistance to heat-softening behavior,sluggish diffusion effects,and potential superconductivity,etc.,were *** HEAs can even have very high irradiation resistance and may have some self-healing effects,and can potentially be used as the first wall and nuclear fuel cladding *** behaviors and flow units are powerful methods to understand the plastic deformation or fracture of *** methods have been successfully used to study the plasticity of amorphous alloys(also bulk metallic glasses,BMGs).The flow units are proposed as:free volumes,shear transition zones(STZs),tension-transition zones(TTZs),liquid-like regions,soft regions or soft spots,*** flow units in the crystalline alloys are usually dislocations,which may interact with the solute atoms,interstitial types,or substitution ***,the flow units often change with the testing temperatures and loading strain rates,e.g.,at the low temperature and high strain rate,plastic deformation will be carried out by the flow unit of twinning,and at high temperatures,the grain boundary will be the weak area,and play as the flow *** serration shapes are related to the types of flow units,and the serration behavior can be analyzed using the power law and modified power law.
Physical systems evolve from one state to another along paths of least energy barrier. Without a priori knowledge of the energy landscape, multidimensional search methods aim to find such minimum energy pathways betwe...
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Physical systems evolve from one state to another along paths of least energy barrier. Without a priori knowledge of the energy landscape, multidimensional search methods aim to find such minimum energy pathways between the initial and final states of a kinetic process. However, in many cases, the user has to repeatedly provide initial guess paths, thus implying that the reliability of the final result is heavily user-dependent. Recently, the idea of “distortion symmetry groups” as a complete description of the symmetry of a path has been introduced. Through this, a new framework is enabled that provides a powerful means of classifying the infinite collection of possible pathways into a finite number of symmetry equivalent subsets, and then exploring each of these subsets systematically using rigorous group theoretical methods. The method, which we name the distortion symmetry method, is shown to lead to the discovery of previously hidden pathways for the case studies of bulk ferroelectric switching and domain wall motion in proper and improper ferroelectrics, as well as in multiferroic switching. These provide novel physical insights into the nucleation of switching pathways at experimentally observed domain walls in Ca3Ti2O7, as well as how polarization switching can proceed without reversing magnetization in BiFeO3. Furthermore, we demonstrate how symmetry-breaking from a highly symmetric pathway can be used to probe the non-Ising (Bloch and Néel) polarization components integral to transient states involved in switching in PbTiO3. The distortion symmetry method is applicable to a wide variety of physical phenomena ranging from structural, electronic and magnetic distortions, diffusion, and phase transitions in materials.
In this paper, we consider the capillarity-driven evolution of a solid toroidal island on a flat rigid substrate, where mass transport is controlled by surface diffusion. This problem is representative of the geometri...
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IntroductionPancreatic cancer (PC) has a poor prognosis with high mortality, due to the lack of effective early diagnostic and prognostic *** and methodsIn order to target and diagnose PC, we developed a dual-modal im...
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IntroductionPancreatic cancer (PC) has a poor prognosis with high mortality, due to the lack of effective early diagnostic and prognostic *** and methodsIn order to target and diagnose PC, we developed a dual-modal imaging probe using Glypican-1 (GPC-1) antibody conjugated with Gd–Au nanoclusters (NCs; Gd-Au-NC-GPC-1). GPC-1 is a type of cell surface heparan sulfate proteoglycan, which is often highly expressed in PC. The probe was successfully prepared with a hydrodynamic diameter ranging from 13.5 to 24.4 *** characteristics showed absorption at 280 nm and prominent emission at 650 nm. Confocal microscopic imaging showed effective detection of GPC-1 highly expressed PC cells by Gd-Au-NC-GPC-1, which was consistent with flow cytometry results. In vitro relaxivity characterization demonstrated that the r1 value of the probe was 17.722 s−1mM−1Gd, which was almost 4 times higher compared with that of Gd-diethylenetriaminepentacetate (DTPA; r1 value =4.6 s−1mM−1Gd). Gd-Au-NC-GPC-1 exhibited similar magnetic resonance (MR) signals when compared to Gd-DTPA even at lower Gd concentrations. Much higher MR signals were registered in PC cells (COLO-357) compared with normal cells (293T). Furthermore, Gd-Au-NC-GPC-1 could effectively detect PC cells in vivo by dual-modal fluorescence imaging/magnetic resonance imaging (FI/MRI) at 30 minutes postinjection. In addition, Gd-Au-NC-GPC-1 did not show significant biotoxicity to normal cells at tested concentrations both in vitro and in ***-Au-NC-GPC-1 has demonstrated to be a promising dual-modal FI/MRI contrast agent for targeted diagnosis of PC.
Physical systems evolve from one state to another along paths of least energy barrier. Without a priori knowledge of the energy landscape, multidimensional search methods aim to find such minimum energy pathways betwe...
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Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential ene...
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The monoscale consideration will not be applicable to multiscale materials, which motivates the formulation of multiscale fatigue model for metal materials. Scale segmentation involves multiple ranges. Strain energy d...
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