Solute Partitioning Research Articles

Response of partitioning to cooling rate for different solutes in aluminum alloys

It is commonly recognized that the cooling rate has a substantial effect on solute partitioning and its resultant microsegregation during solidification. The classical dendrite tip undercooling theory clarifies the mitigation of microsegregation by increasing the cooling rate. However, most of the studies focused on binary alloys, leaving an open question as to whether the microsegregation of different solutes in a multi-component alloy system exhibits a relieving degree similar to increasing cooling rate. Taking a widely used 6022-type Al alloy (Al-0.76Mg-0.93Si-0.2Fe) as a model alloy, the current study reveals that the microsegregation of Mg gets alleviated to the greatest extent, followed by those of Si and Fe when the cooling rate increases from 5 to 128 K/s. This phenomenon is attributed to the solute-based difference in response to partitioning to cooling rate (denoted as Rk). We propose a theoretical equation to quantify Rk, and the Rk values of solute Mg, Si, and Fe successfully explain the rank of solute partitioning in experiments. Furthermore, a broad range of Rk values of other commonly used alloying elements in Al alloys were calculated and ranked, delivering a handy tool to predict the microsegregation behavior and solubility of different solute elements upon sub-rapid solidification, which is consistent with experimental observation. This framework can also be extended to other multi-component alloy systems.

Algorithm optimization of ME2SO-free cryopreservation and solute partitioning analysis of NK cells

Algorithm optimization of ME2SO-free cryopreservation and solute partitioning analysis of NK cells

Development and Numerical Testing of a Model of Equiaxed Alloy Solidification Using a Phase Field Formulation

A computational framework is developed to understand the transient behavior of isothermal and non-isothermal transformation between liquid and solid phases in a binary alloy using a phase-field method. The non-isothermal condition was achieved by applying a thermal gradient along the computational domain. The bulk solid and liquid phases were treated as regular solutions, along with introducing an order parameter (phase field) as a function of space and time to describe the interfacial region between the two phases. An antitrapping flux term was integrated into the present phase-field model to mitigate the amount of solute trapping, which is characterized by the non-equilibrium partitioning of the solute. The governing equations for the phase field and the solute composition were solved by the cell-centered finite volume method using the open-source computational tool OpenFOAM. Simulations were carried out for the evolution of equiaxed dendrites inside an undercooled melt of a binary alloy, considering the effect of various computational parameters such as interface thickness, strength of crystal anisotropy, stochastic noise amplitude, and initial orientation. The simulated results show that the solidification morphology is sensitive to the magnitude of anisotropy as well as the amplitude of noise. A strong influence of interface thickness on the growth morphology and solute redistribution during solidification was observed. Incorporating antitrapping flux resulted in the solute partitioning close to the equilibrium value. Simulations show that the grain shape is unaffected by changes to crystallographic orientation with respect to the Cartesian computational grid. Thermal gradients exerted discernible effects on the solute distribution and the dendritic growth pattern. Starting with multiple nucleation events the model predicted realistic polycrystalline solidification and as-solidified microstructure.

Development of low rare-earth containing magnesium alloy with high strength-ductility synergy by engineering nano-spaced lamellae

Conventional Mg-RE-based alloys containing a high content of RE solutes can generate high strength, but there is often low ductility. Herein, we reported a low RE alloying Mg–5Er-1Nd-1Zn-0.2Zr extrusion alloy with only 6 wt% RE addition, showing a high strength-ductility synergy, such as a yield strength of ∼380 MPa and an elongation of ∼12.5 %), which is superior to the majority of traditional Mg-RE-based wrought alloys with at least 12 wt% RE addition. Nano-spaced lamellae was formed in the α-Mg matrix during extrusion, such as stacking faults (SFs) evolved in dynamically recrystallized (DRXed) grains and the 14H long-period stacking-ordered (LPSO) phase developed in unDRXed grains as a result of dynamical solute partitioning during extrusion. And this alloy characterizes a high proportion (∼70 %) of elongated unDRXed grains with a strong basal texture. Thus, high strength is mostly related to SFs/LPSO lamellae strengthening and texture hardening, instead of the commonly accepted refinement strengthening and precipitation hardening in those of high RE containing Mg alloys. While high ductility is heavily dependent on SFs/LPSO lamellae to trigger strain hardening and <c + a> dislocations glide, independent of fine grains as in traditional Mg alloys with high elongation. The findings give an insight into the development of high-performance Mg wrought alloys with a high strength-ductility synergy.

Task Modules Partitioning, Scheduling and Floorplanning for Partially Dynamically Reconfigurable Systems with Heterogeneous Resources

Some field programmable gate arrays (FPGAs) can be partially dynamically reconfigurable with heterogeneous resources distributed on the chip. FPGA-based partially dynamically reconfigurable system (FPGA-PDRS) can be used to accelerate computing and improve computing flexibility. However, the traditional design of FPGA-PDRS is based on manual design. Implementing the automation of FPGA-PDRS needs to solve the problems of task modules partitioning, scheduling, and floorplanning on heterogeneous resources. Existing works only partly solve problems for the automation process of FPGA-PDRS or model homogeneous resources for FPGA-PDRS. To better solve the problems in the automation process of FPGA-PDRS and narrow the gap between algorithm and application, in this paper, we propose a complete workflow including three parts: pre-processing to generate the lists of task module candidate shapes according to the resource requirements, exploration process to search the solution of task modules partitioning, scheduling, and floorplanning, and post-optimization to improve the floorplan success rate. Experimental results show that, compared with state-of-the-art work, the pre-processing process can reduce the occupied area of task modules by 6% on average; the proposed complete workflow can improve performance by 9.6%, and reduce communication cost by 14.2% with improving the resources reuse rate of the heterogeneous resources on the chip. Based on the solution generated by the exploration process, the post-optimization process can improve the floorplan success rate by 11%.

Microwave enhancement of extractions and reactions in Liquid-Liquid biphasic systems

Microwaves (MWs) are a powerful technology for electrifying chemical processing and can enable selective heating, which is impossible in conventional systems. This effect was recently ascribed to enhancing liquid–liquid reactive extractions in biomass derivative chemistry. Expanding upon prior research that showcased MW-induced temperature gradients and their optimization, this study delves into the impact of these gradients on extraction using a combination of experiments and simulations. The findings illustrate that the colder organic phase enforced by the MW heating leads to alterations in solute partitioning and phase behavior. Partitioning can be further tuned by adding salts or by carefully selecting solvents, as demonstrated via COSMO-RS calculations. The colder organic phase also leads to Rayleigh instabilities and active buoyancy-driven mixing. These effects, measured experimentally and simulated in COMSOL, can greatly enhance mass transfer rates in liquid–liquid systems. Under conditions reported in the literature, the MW enhancement to reactive extractions is minimal. Conditions that may lead to more impressive extractions and reactive extractions are suggested. Additionally, it's observed that temperature gradients retard byproduct formation.

An examination of published datasets of skin permeability and partition coefficients

Permeability and partition coefficients of the skin barrier are important for assessing dermal absorption, bioavailability, and safety of cosmetics and medicine. We use the Potts and Guy equation to analyse the dependence of skin permeability on the hydrophobicity of permeants and highlight the significant differences in published datasets. Correlations of solute partition to skin are examined to understand the likely causes of the differences in the skin permeability datasets. Recently published permeability datasets show weak correlation and low dependence on hydrophobicity. As expected, early datasets show good correlation with hydrophobicity due to the related derivation. The weaker correlation of later datasets cannot be explained by the partition to skin lipids. All the datasets of solute partition to skin lipid showed a similar correlation to hydrophobicity where the log-linear correlation coefficient of partition is almost the same of the log-linear coefficient of Potts and Guy equation. Weak correlation and dependence of the late permeability datasets with SC lipid/water partition and that they are significantly under predicted by the Potts and Guy equation suggests either additional non-lipid pathway at play or a weaker skin barrier property.

COSMO-RS solute partition ratios for solvent mixtures of unknown composition: Henry's law constants as descriptors for mixture sigma profiles

Henry's law constants (H) for selected probe molecules have been used as descriptors to estimate the COSMO-RS sigma profiles of solvents and solvent mixtures. Henry's law constants were calculated with COSMOtherm for small sets of probe molecules in 155 organic solvents (training set), and these constants subsequently used as descriptors to model the solvent sigma profiles with 61 multiple linear regression (MLR) equations. Subsequent input into COSMOtherm of weighted basis molecule solvent mixtures whose sigma profiles closely matched those modelled for the training set solvents allowed estimation of air-solvent and water-solvent partition ratios for solutes in solvents and solvent mixtures without input of the solvent or solvent mixture identity. The best performing model had 16 descriptors and gave both a training and test set average root-mean square error (RMSE) of 0.008 and an average relative square error (RSE) of 0.07. Partition ratios (K) were then generated for a test set of 251 additional organic solute molecules in solvent/water media where solvents were test set compounds and H constants for the same probe molecules were used as descriptors. The best performing sigma profile model yielded log K RMSE values ranging from 0.17 to 0.92. Finally, this approach was applied to several mixtures ranging from simple binary mixtures to two mixtures considered to be of unknown or variable composition, complex reaction productions or biological materials (UVCBs), namely gasoline and an essential oil mixture. Mixture/water partition ratios were estimated for 251 solutes giving log K RMSE values ranging from 0.24 to 0.88.

Collecting Multi-type and Correlation-Constrained Streaming Sensor Data with Local Differential Privacy

Local differential privacy (LDP) is a promising privacy model for distributed data collection. It has been widely deployed in real-world systems (e.g. Chrome, iOS, macOS). In LDP-based mechanisms, an aggregator collects private values perturbed by each user and then analyses these values to estimate their statistics, such as frequency and mean. Most existing works focus on simple scalar value types, such as boolean and categorical values. However, with the emergence of smart sensors and Internet of Things, high-dimensional data are gaining increasing popularity. In many cases where more than one type of sensor data are collected simultaneously, correlations exist between various attributes of such data, e.g. temperature and luminance. To ensure LDP for high-dimensional data, existing solutions either partition the privacy budget ϵ among these correlated attributes or adopt sampling, both of which dilute the density of useful information and thus result in poor data utility. In this paper, we propose a relaxed LDP model, namely, univariate dominance local differential privacy (UDLDP), for high-dimensional data. We quantify the correlations between attributes and present a correlation-bounded perturbation (CBP) mechanism that optimizes the partitioning of privacy budget on each correlated attribute. Furthermore, we extend CBP to support sampling, which is a common bandwidth reduction technique in sensor networks and Internet of Things. We derive the best allocation strategy of sampling probabilities among attributes in terms of data utility, which leads to the correlation-bounded perturbation mechanism with sampling (CBPS). Finally, we discuss how to collect and leverage the correlation from real-time data stream with a by-round algorithm to enhance the utility. The performance of the proposed mechanisms is evaluated and compared with state-of-the-art LDP mechanisms on real-world and synthetic datasets.

A module classification method for light industrial equipment based on improved NSGA2-FCM algorithm

In response to the problem that it is easy to fall into local optimum when using the traditional clustering algorithm to divide the modules, this paper improves the initialisation strategy of the NSGA2 algorithm and combines it with the FCM algorithm to propose an improved NSGA2-FCM algorithm for clustering analysis. Firstly, the FBS mapping is used to model the functional structure of the product system and identify the relationship between the product functional structures. Secondly, a correlation synthesis matrix is constructed based on the relationships between the module division drivers. Finally, the improved NSGA2-FCM algorithm is applied to cluster analysis of the product to derive the best module division scheme. The algorithm avoids falling into local optima by optimising the initialisation strategy of the NSGA2 algorithm, while using the FCM algorithm to improve the accuracy of the clustering. This allows the algorithm to explore the solution space more effectively when finding the best module partitioning solution. Finally, the effectiveness of the algorithm for module classification of light industrial equipment is verified using beer fermenters as a case study.

Alloy composition and process design based on thermodynamic and kinetic simulation: The case of medium Mn steel

This paper presents a design method for alloy materials that can be applied to medium Mn steel containing Al and/or Si. By using thermodynamic calculations based on CALPHAD, a wide range of alloy composition spaces were systematically studied, and the relationship between retained austenite fraction and its stability with intercritical annealing temperature was investigated. Alloys that meet the Process Window (PW) were screened and their various characteristics of alloy elements within the process window were determined. Additionally, the study performs three different kinetic simulations of austenite growth and solute partitioning during isothermal annealing on alloys that meet PW. The prediction of the model is verified by comparing with the experimental data in the literature. The results show that the remote model is more suitable for the actual production of low temperature isothermal intercritical annealing. Therefore, the method provided in this work makes a guiding contribution to alloy development, which can determine the alloy composition and heat treatment process satisfying PW and optimize the overall properties.

Partitioning multi-layer edge network for neural network collaborative computing

There is a trend to deploy neural network on edge devices in recent years. While the mainstream of research often concerns with single edge device processing and edge-cloud two-layer neural network collaborative computing, in this paper, we propose partitioning multi-layer edge network for neural network collaborative computing. With the proposed method, sub-models of neural network are deployed on multi-layer edge devices along the communication path from end users to cloud. Firstly, we propose an optimal path selection method to form a neural network collaborative computing path with lowest communication overhead. Secondly, we establish a time-delay optimization mathematical model to evaluate the effects of different partitioning solutions. To find the optimal partition solution, an ordered elitist genetic algorithm (OEGA) is proposed. The experimental results show that, compared with traditional cloud computing, single-device edge computing and edge-cloud collaborative computing, the proposed multi-layer edge network collaborative computing has a smaller runtime delay with limited bandwidth resources, and because of the pipeline computing characteristics, the proposed method has a better response speed when processing large number of requests. Meanwhile, the OEGA algorithm has better performance than conventional methods, and the optimized partitioning method outperforms other methods like random and evenly partition.

PMP: A partition-match parallel mechanism for DNN inference acceleration in cloud–edge collaborative environments

To address the challenges of delay-sensitive deep learning tasks, Deep Neural Network (DNN) models are often partitioned and deployed to the cloud–edge environment for parallel and collaborative inference. However, existing parallel coordination mechanisms are not suitable for the cloud–edge environment, as the high inter-layer dependence of DNNs can increase transmission latency and wait times for inference, which contradicts the advantage of low latency in edge computing. To resolve this contradiction, the PMP mechanism takes into account the inter-layer transfer dependence of partitioning solutions and employs a multi-objective equalization algorithm to derive DNN model partitioning strategies suitable for multi-way parallel computing. Moreover, the mechanism establishes a DNN inference time prediction model based on these partitions and utilizes an iterative matching algorithm to approximate an optimal DNN inference workflow. Extensive evaluations of the proposed mechanism are conducted using various DNN models, and the results demonstrate its superiority over existing schemes, including local, CoEdge, and EdgeFlow. Notably, PMP achieves significant reductions in total inference latency compared to these schemes, with reductions of 80.9%, 37.9%, and 9.1%, respectively.

A comparative study of solute trapping in Fe-(33–45 at%) Cu alloys manufactured by laser directed energy deposition

The high cooling rates in laser directed energy deposition (DED-LB) of alloys lead to substantial amounts of solute trapping as solute atoms cannot diffuse away from the solid/liquid interface before it advances. In some concentrated alloys, this results in supersaturated solid phases that form nanoscale hierarchical microstructures when the solute atoms precipitate out during reheating from subsequent laser passes. We choose the Iron-Copper (Fe-Cu) binary alloy as model system as it is chemically homogeneous in the liquid phase and has negligible solid solubility at room temperature. Two alloys with nominal compositions in atomic (at.) %, Fe67Cu33 and Fe55Cu45, were manufactured using DED-LB. Scanning transmission electron microscopy (STEM), energy dispersive spectroscopy (EDS) and wavelength dispersive spectroscopy (WDS) were used to characterize the nanostructures and heterogeneous chemical compositions. A non-equilibrium solute partitioning model was used to compute the supersaturated chemistries of the constituent phases and validated with experimentally measured compositions. The measured phase compositions of the two alloys were very similar, at roughly 12 at% Cu and 4 at% Fe in the α(bcc)-Fe and ε(fcc)-Cu phases respectively, despite having different processing parameters and mechanical behavior. This indicates that the total thermal history, that depends on both the processing parameters and laser scan pattern, plays a stronger role on the final microstructure evolution than just the initial quantity of trapped solute. Additionally, we find that current non-equilibrium solute partitioning models applied on the continuum scale fall short of predicting accurate quantitative phase compositions in concentrated alloys, although the qualitative trends are captured correctly.

A dual-clustering algorithm for a robust medical grid partition problem considering patient referral

The medical grid including hospitals at all levels is a new hierarchical diagnosis and treatment system. It is assumed to provide health services for residents in a certain area, allowing free referral of patients, so as to effectively utilize medical resources. Therefore, from the perspective of the government, the key issue is how to divide the medical grid in a robust and balanced manner. In this paper, various deterministic factors, such as hospital level, location and department, as well as uncertain factors, including patient distribution or population density, are considered in decision-making. To solve this problem, a dual-clustering algorithm based on K-means and K-medoids (DCKK) is developed with local search methods to minimize the average patient waiting and travelling time. The experimental results show that DCKK algorithm can generate better and more robust grid partition solutions than the existing mainstream algorithms in different scenarios. In addition, the rules between the number of medical grids and the number of patients, as well as the hospital sharing between medical grids, are also studied. Finally, a real medical grid partition case of Ji'nan, China, with forty hospitals in four urban areas, is studied, and five medical grids are recommended.

Purification of High-Purity Tin via Vertical Zone Refining

The present investigation delves into the potential of vertical zone refining as an effective purification technique for achieving high-purity tin (Sn) metal. The utilization of vertical zone refining offers distinct advantages over traditional horizontal zone refining, as it allows for enhanced control over the molten zone and solid–liquid interface, ultimately leading to superior impurity separation efficiency. The present study reveals that the solute partition coefficients (k0) of various impurity elements, such as Zn, Ag, Al, Mg, Ca, Ni, In, Co, Cu, As, Pb, Fe, and Bi, during the vertical zone refining process consistently demonstrate values below one. Notably, the partition coefficient of Sb deviates slightly from the others, being greater than one but approaching one. The authors achieve exceptional levels of purity in both the bottom and middle regions of the rod by subjecting the Sn melt to nine passes of vertical zone refining at a heating temperature of 405 °C and a downward pulling rate of 10 µm/s, resulting in purities exceeding 6N4. Furthermore, by evaluating the effective partition coefficients (keff), it was determined that impurity elements, such as Cu and Bi, closely approach their equilibrium partition coefficients, reaching values of approximately 0.492 and 0.327, respectively. To further enhance the purity of Sn metal and maximize product yield, we propose the utilization of electrolytic refining and vacuum distillation, with particular emphasis on the efficient separation of five specific elements, including Cu, Fe, As, Pb, and Sb. By elucidating these findings, this study not only contributes valuable insights into the efficacy of vertical zone refining as a purification technique for high-purity tin metal, but also offers important recommendations for refining strategies and impurity element separation.

Toward multiscale simulations for solidification microstructure and microsegregation for selective laser melting of nickel-based superalloys

In this study, we carried out multiscale simulations that integrate a macro-scale finite element method simulation for mass and heat transfer of the transient molten pool and a micro-scale quantitative phase-field model for dendritic growth and solute distribution based on selective laser melting (SLM) experiments. Macroscopic simulations reveal an approximately inverse coupling of solidification velocity Vs and thermal gradient G near the solid–liquid interface at the tail end of the molten pool. It was found that the maximum flow velocity of the Inconel 718 alloy melt in the molten pool exceeds 2×10−2 m/s and the maximum negative pressure reaches −5.28×10−4 Pa. Through quantitative phase-field simulations, the map about G/Vs for each solidification morphology was obtained, and it was found that solidification velocity dependent solute partition coefficients k(Vs) can strongly influence the estimated stability regions. The power law relationship of the primary dendritic arm spacing (PDAS) λ1 versus solidification velocity λ1∝Vsα agrees with the previous experimental results for lower G, and the exponent α decreases with increasing G. The PDAS λ1 versus cooling rate R following λ1∝R−0.512 is consistent with previous investigations. We found that λ1 is a nonlinear function with G−0.5Vs−0.25, i.e., λ1∝(G−0.5Vs−0.25)α, with α ranging from 0.887 to 2.466 for various G. Comparison of the predicted λ1 with Trivedi's model reveals that λ1 is still nonlinear with G−0.25Vs−0.25.

Consequences of solute partitioning on hardness in stabilized nanocrystalline alloys

Phosphorus segregation at grain boundaries (GBs) in Ni–P nanocrystalline (NC) thin films has been demonstrated to impede grain growth at elevated temperatures. In this study, we investigate the impact of phosphorus segregation in Ni-1at.%P and Ni-4at.%P alloys on their hardness by using nano-indentation and post-mortem transmission electron microscopy to characterize deformation-induced microstructural changes. The NC film exhibited approximately 20% higher average hardness values when phosphorus was in solution, as a result of the as-deposited processing, compared to films that underwent subsequent thermal annealing to stabilize the grain size by partitioning the phosphorus to the GBs. Under various strain rate loading conditions, the deformation was determined to be GB-mediated control. The effects of phosphorus partitioning on intragranular and GB deformation mechanisms were evaluated using atomistic simulations. While larger grain sizes shift deformation accommodation to intragranular mechanisms, the reduction of the GB fraction with increasing grain size can increase the available solute concentrations in the boundaries leading to active GB-mediated deformation. This interplay between solid solution strengthening, solute partitioning, grain size, global and local solute concentration, and deformation is discussed in detail.

Diffuse modelling of pearlite growth in Calphad-coupled multicomponent multi-phase-field simulations

During cooling of steels and cast irons, austenite can decompose by a eutectoid transformation into pearlite, a two-phased mixture of ferrite and cementite. Since the internal lamellar structure is commonly too fine to be distinguished on the scale of the austenite grain structure, pearlite is often modelled as an effective, pseudo-single phase. Such a pragmatic treatment would also be desirable to reduce the computational effort of large-scale multi-phase-field simulations, but a fundamental hindrance is that no consistent thermodynamic description exists for effective pearlite in multicomponent databases. Alternatively, we here propose to model pearlite as diffuse mixture of two phases with individual local fractions and concentrations, such that solute partitioning and thermodynamic driving forces can be consistently derived from standard Calphad databases. The essential computational advantage is that only the outer interfaces of the pearlite nodules have to be numerically resolved, which allows for increased grid spacing and time-steps. The impact of the unresolved lamellar structure on the curvature undercooling is modelled analytically based on a characteristic spacing, which may be calibrated either experimentally or by small-scale simulations. The potential and the limitations of the new approach, implemented in the frame of the Micress® software, shall be discussed.

Annealing hardening effect aroused by solute segregation in gradient ultrafine-grained Mg-Gd-Zr alloy

In this study, a remarkable annealing hardening effect was detected in gradient ultrafine-grained (UFG) Mg-0.32Gd-0.11Zr (at.%) alloy sheet fabricated by sliding friction treatment (SFT). Under the precipitation-free condition, the annealed UFG structure shows an obvious hardness increment from 1.40 GPa to 1.89 GPa after 200°C heating for 12 h, which exhibits a much higher hardening response than the annealed coarse-grained (CG) structure. The high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and elemental mapping reveal prominent segregation of solute Gd atoms along grain boundaries, which endows the UFG structure with excellent grain boundary stability. Moreover, Gd segregation is also found around the extrinsic stacking fault (E-SFs) and the low-angle grain boundaries composed of edge dislocations. The large-scale solute partitioning provides a significant segregation hardening effect, which completely resists the softening effect aroused by the grain coarsening and dislocation annihilation. This work realizes a good combination of surface mechanical processing for fabricating UFGs and subsequent heat treatment, which earns desirable segregation hardening effects.