A comprehensive understanding of material deformation behavior holds importance in enhancing the properties of materials, which can be achieved through modeling the relationship between microstructure and deformation behavior by an appropriate crystal plasticity (CP) model. However, before simulations, obtaining a high-fidelity representative microstructure (RM) to simultaneously meet the efficiency and accuracy requirements of the CP model is still a challenge. Thus, taking the Al-Al2Cu eutectics as an example, we successfully obtained a high-fidelity RM with two-point statistics and some fashionable image processing techniques. Then a high-resolution RMCP model was developed and the deformation behavior of Al-Al2Cu eutectics at room temperatures was uncovered. Due to multiple activated slip systems on {111}<110> inducing obvious cross-slip behavior, severe and heterogenous plastic deformation occurs in α-Al. And dislocations originate from the microstructure defects. In θ-Al2Cu, all possible slip systems are not activated, resulting in no plastic deformation. The predictions of the RMCP model are verified to be reliable by comparing with the results of in-situ tensile tests. Our proposed RMCP method is not only applicable to the two-phase eutectic systems, but also suitable for various multiphase and polycrystalline systems.
Research on the solubility behavior of l-tryptophan methyl ester hydrochloride, an important pharmaceutical intermediate, is necessary for the design of its crystallization and separation processes. The solubility data of l-tryptophan methyl ester hydrochloride were measured by the static gravimetric method in 12 pure solvents (methanol, water, ethanol, n-propanol, n-butanol, isobutanol, sec-butanol, isopropanol, propanone, 2-butanone, ethyl acetate, and acetonitrile) at 283.2–323.2 K and 101.2 kPa. The solubility of l-tryptophan methyl ester hydrochloride in all studied solvents increases with the increase of temperature. In addition, the solubility sequence of l-tryptophan methyl ester hydrochloride at 298.2 K is methanol (0.033403 mol/mol) > water (0.011939 mol/mol) > ethanol (0.007368 mol/mol) > n-propanol (0.003708 mol/mol) > n-butanol (0.002632 mol/mol) > isobutanol (0.001716 mol/mol) > sec-butanol (0.001651 mol/mol) > isopropanol (0.001573 mol/mol) > propanone (0.000605 mol/mol) > 2-butanone (0.000401 mol/mol) > ethyl acetate (0.000074 mol/mol) > acetonitrile (0.000065 mol/mol). Methanol had the highest solubility of 0.033403 mol/mol, while acetonitrile had the lowest solubility of 0.000065 mol/mol. The main factors influencing the solubility behavior include the empirical solvent polarity parameters (ET(30)), hydrogen bonding, and cohesive energy density. Three solubility fitting models were used to correlate the experimental mole fraction solubility data, including the modified Apelblat model, the nonrandom two liquid (NRTL) model, and the Margules model. Furthermore, mixing thermodynamic characteristics of l-tryptophan methyl ester hydrochloride in selected solvents were calculated by the NRTL model, and the results indicated that the mixing process was spontaneous and driven by entropy. In order to choose the best model for l-tryptophan methyl ester hydrochloride, the relative applicability of these models was evaluated by the Akaike Information Criterion (AIC). The study of the solubility of l-tryptophan methyl ester hydrochloride not only enriches the solubility database and provides guidance and basis for crystallization but also provides rich solubility data for machine learning models.
1,4-Diethoxybenzene is an important organic synthesis intermediate used in the synthesis of pesticides, pharmaceuticals, fuels, and reagents. In the absence of relevant solubility data for 1,4-diethoxybenzene, it is necessary to study in detail its solubility behavior in various solvents. The solubility data for 1,4-diethoxybenzene in 12 pure solvents, including methanol, ethanol, acetone, 2-butanone, acetonitrile, dimethyl carbonate, ethyl lactate, methyl acetate, ethyl acetate, n-propyl acetate, and isobutyl acetate, was determined. Experiments showed that the solubilities of 1,4-diethoxybenzene molar fractions all increased with increasing temperature. The order of their solubility at 298.15 K is as follows: butyl acetate (0.212 mol/mol) > ethyl acetate (0.179 mol/mol) > 2-butanone (0.169 mol/mol) > n-propyl acetate (0.165 mol/mol) > methyl acetate (0.145 mol/mol) > acetone > isobutyl acetate (0.112 mol/mol) > dimethyl carbonate (0.099 mol/mol) > ethyl lactate (0.074 mol/mol) > acetonitrile (0.067 mol/mol) > ethanol (0.015 mol/mol) > methanol (0.009 mol/mol). Modified Apelblat models, Margules models, UNIQUAC models, and NRTL models were used for correlation of solubility data. The results of ARD and RMSD obtained from the calculations show that each model correlates well with the experimental data. In particular, better solubility correlation results were obtained with the modified Apelblat model. Hirshfeld surface (HS) analysis and molecular electrostatic potential surfaces (MEPS) were utilized to analyze the interactions within 1,4-diethoxybenzene solutions. The Hansen solubility parameters (HSPs) were utilized to assess the solvents' capability and to elucidate its ability to dissolve 1,4-diethoxybenzene. The main factors influencing the solubility behavior include solvent polarity (ET(30)), hydrogen bond, cohesive energy density, and Hansen solubility parameters (HSPs). Furthermore, mixing thermodynamic characteristics of 1,4-diethoxybenzene in selected solvents were calculated by the NRTL model, which revealed that the mixing process was spontaneous and entropy driven. These experimental results can be used for the purification, crystallization, and industrial applications of 1,4-diethoxybenzene as well as similar substances. Therefore, it is necessary to study the solvation behavior of 1,4-diethoxybenzene in different monosolvents to provide sufficient data for the design of its crystallization process.
In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic (EM) waves into the SAR radiation area and follows the Nyquist sampling law to set the density of transmitted EM waves to effectively identify the beam radiation area. The ray-tracing algorithm is used to obtain the backscatter amplitude and real-time slant range of the transmitted EM wave, which can effectively record the multiple backscattering among the components of the distributed target so that the backscattering subfields of each component can be correlated. According to the RD condition equation, the backscattering amplitude is assigned to the corresponding range gate, and the three-dimensional (3D) target is mapped into the two-dimensional (2D) SAR slant-range coordinate system, and the SAR target simulated image is directly obtained. Finally, the simulation images of the proposed method are compared qualitatively and quantitatively with those obtained by commercial simulation software, and the effectiveness of the proposed method is verified.
Abstract Quantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision ( R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.
In the theoretical development of normal grain growth, the roles of ‘drift’ (curvature effect) and ‘diffusion’ (stochastic effect) have been an open question for many years. By coupling contributions of microstructure entropy and grain topological interactions with thermodynamic extremal principle (TEP), this letter extends the existing thermodynamic framework of grain growth. It not only explains the curvature and stochastic effects by thermodynamics but provides a new mean-field model for grain growth. Through thermodynamic modification, this new model can yield a high-accuracy representation of existing ultra-large phase-field simulated results.
Quantifying microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure-property (SP) linkage, we proposed a novel concept referred to irregular-representative volume element (IRVE) based on panoramic image stitching technology (PIST) and constructed a data-driven scheme integrating irregular domain-oriented two-point statistics, principal component analysis (PCA) and symbolic regression based on genetic programming (GPSR). Combining with advanced image processing and genetic programming technologies, this scheme improved the microstructure quantization framework. This scheme not only can be applied in different complex conditions for extracting information of material microstructure, but also can be competent for embodying details of microstructure from the perspective of large scale. We demonstrated that IRVE has both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat-resistant steels, it showed powerful ability in building an equational SP linkage with high precision (R=0.91, RMSE=13.17), and the generalization ability of the linkage was also validated by an unseen steel in the training set (absolute error of prediction is less than ±10MPa). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.
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