Search

The coarsening of γ' precipitates in superalloys involves multiple factors and impacts the performance of mechanical properties, such as strength and creep resistance. Classical kinetic theory describes the coarsening of γ' under limited conditions, as it shows deficiencies in γ' precipitation with excessive coarsening. Finding effective factors underlying the kinetic behavior significantly affecting precipitate coarsening remains largely unsolved. We address this by using machine learning models to identify materials descriptors describing γ' precipitate coarsening in Co-based superalloys. Using descriptors that include Young's modulus difference and valence electron number, we obtain an explicit relation assisted by symbolic regression that fits the experimental γ' size data far better than classical kinetic theory in describing γ' coarsening. We infer that the Young's modulus difference dominates γ' coarsening mechanism in different Co-based superalloys. Moreover, alloys containing additional elements with large Young's modulus should favor smaller γ' precipitates size, thus providing guidance for designing advanced multicomponent Co-based superalloys with high γ' coarsening resistance. [Display omitted] We utilize a computer vision framework incorporating the deep learning-based convolutional neural networks to extract accurate microstructural information from γ/γ' microstructure images for analysis in classical kinetic theories and machine learning (ML) models. Coupled with the classical kinetic theory analysis, it can well fit the earlier coarsening of regular γ' precipitates in some Co-based superalloys. However, the classical kinetic theory can not give a reasonable fitting and explanation on the fast coarsening of irregular γ' precipitate, which fully exposes its deficiency in dealing with practical problems. We use ML models and proposed descriptor selection strategy by fitting experimental γ' size data to describe γ' precipitate coarsening using descriptors such as Young's modulus difference (σ E) and valence electron number (V E N), which we identify from ML models. The relation we propose for γ' size prediction equation in terms of aging temperature (T), aging time (t), σ E and V E N is far superior in fitting to the data than classical kinetic theory, and Co-based superalloys with large σ E value tend to follow the γ' coarsening predicted by LSW theory. For strengthening materials, this model for γ' size, which inversely depends on σ E , predicts that the addition of elements with large Young's modulus (E), such as W, Mo, Re and Ru will favor smaller γ' precipitates in Co-based superalloys. This provides a means to design multicomponent Co-based superalloys with high γ' coarsening resistance. [ABSTRACT FROM AUTHOR]

Many microplastics (MPs) were produced in daily life, which would enter sewage treatment plants (STPs) with the wastewater. Although the STPs has a good interception effect on these MPs, there will still be a part of MPs entering the environment with the effluent and sludge treatment, causing a certain ecological risk. This study investigated the abundance, characteristics and retention of MPs in different STPs, as well as the ecological risks caused by MPs entering the environment. The abundance of MPs in influent and effluent was ranged from 2.02 to 2.50 items L−1 and 0.27–0.48 items L−1, respectively. The abundance of MPs in dewatered sludge and sediment of Lake Dianchi was ranged from 3.719–6.949 × 103 items (kg Ds)−1 and 1.84–5.23 × 103 items (kg Ds)−1, respectively. So roughly 80% of the MPs were trapped and transferred into the dewatered sludge. The observed colors of MPs were transparent, black, blue, red, pale brown, green and gray, and their main species were polypropylene (PP) and polyethylene (PE). To further evaluate the ecological risks of MPs, the oyster mushroom was cultivated in a medium supplemented with MPs. It was found that MPs could be absorbed by oyster mushrooms with a 7–11% of absorption rate, the fibers were widely distributed in the stipes and the pileus. This study had theoretical significance for exploring the distribution of MPs in STPs and clarifying the ecological risk posed by MPs in the environment. [Display omitted] • The abundance and characteristics of MPs in four treatment process were documented. • The retention mechanism of MPs in four sewage treatment plants were explored. • The accumulation of MPs in the sediment of a receiving water was investigated. • The ecological risk on accumulation of MPs was evaluated by using oyster mushrooms. [ABSTRACT FROM AUTHOR]

Abstract: The present work conducts crystal characterization by High Resolution Transmission Electron Microscopy (HRTEM) on Cu/Sn-nanolayer/Ni sandwich structures associated with the use of Energy Dispersive X-ray (EDX) analysis. The results show that DO22–(Cu,Ni)3Sn intermetallic compound (IMC) ordered structure is formed in the sandwich structures at the as-electrodeposited state. The formed DO22-(Cu,Ni)3Sn IMC is a homogeneous layer with a thickness about 10nm. The DO22–(Cu,Ni)3Sn IMC nanolayer is stable during annealing at 250°C for 810min. The formation and stabilization of the metastable DO22–(Cu,Ni)3Sn IMC nanolayer are attributed to the less strain energy induced by lattice mismatch between the DO22 IMC and fcc Cu crystals in comparison with that between the equilibrium DO3 IMC and fcc Cu crystals. [Copyright &y& Elsevier]

Abstract: The Energy-Dispersive-X-ray-based permeation and oxidation test has been further developed by an improved theoretical analysis, in which chemical potential gradients rather than concentration gradients are employed. The developed test is able to characterize diffusion kinetics in diffusion barriers at the nanometer scale. The Cu flux coefficient in (Cu, Ni)3Sn intermetallic compound nanolayers was determined from the test to be 8.48×10−15 mol·(m·s·J/mol)–1 exp(−52.3kJ·mol−1/RT) in a temperature range of 250°C–400°C. [Copyright &y& Elsevier]

The microstructure evolution of Al0.3FeCoNiCrx (x = 0, 0.3, 0.5, 1, 1.5) high-entropy alloys (HEAs) were studied using X-Ray diffraction technique and scanning electron microscope equipped with energy dispersive spectrometer. The short-term and long-term corrosion behaviours of these alloys in 3.5 wt.% NaCl solution were studied by electrochemical impedance spectroscopy, potentiodynamic polarisation measurement, immersion test and corrosion morphology analysis. The results show that all the designed HEAs present single-phase FCC structure, and the increase in Cr content changes the microstructural morphology from cellular to a typical dendritic–interdendritic state. Without the influence of phase transformation, the corrosion resistance of Al0.3FeCoNiCrx HEAs gradually increases with the increase in Cr content. Our designed alloys exhibit excellent corrosion resistance compared to the existing HEAs in the AlFeCoNiCr composition system. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • We develop a method called VFSMS to ensure accuracy and speed of image stitching. • Experimental results show that VFSMS achieve state-of-art performance. • We share our software at https://www.mgedata.cn/app_entrance/microscope. Abstract In material research, it is often highly desirable to observe images of whole microscopic sections with high resolution. So that micrograph stitching is an important technology to produce a panorama or larger image by combining multiple images with overlapping areas, while retaining microscopic resolution. However, due to high complexity and variety of microstructure, most traditional methods could not balance speed and accuracy of stitching strategy. To overcome this problem, we develop a method named very fast sequential micrograph stitching (VFSMS), which employ incremental searching strategy and GPU acceleration to guarantee the accuracy and the speed of stitching results. Experimental results demonstrate that the VFSMS achieve state-of-art performance on three types' microscopic datasets on both accuracy and speed aspects. Besides, it significantly outperforms the most famous and commonly used software, such as ImageJ, Photoshop and Autostitch. The software is available at https://www.mgedata.cn/app_entrance/microscope. [ABSTRACT FROM AUTHOR]

This research aims to provide a solution to the scarcity and fragmentation of industrial data on die casting aluminum alloys. Quantifying the coupling between die casting process-composition-properties of aluminum alloys through small datasets, is a critical step in predicting part properties and optimizing process selection. To visualize the connections and discuss the effect of the interaction between different parameters on the property, data is fed into a self-organizing mapping model. Whereafter, an innovative data extension method is proposed to predict both yield and tensile strengths with more than 96% accuracy using a small data set. Moreover, two novel methods of multi-parameter combined range selection, the multi-objective optimization based on the agent model and the superimposition of the contour map, are guided by being informed in which region the mechanical properties fall. Finally, the feasibility of application range selection method is verified experimentally. Mapping based on data-driven process-composition-properties relationships and the free combination of application ranges are reliable theoretical solutions which are valuable for practical applications. [ABSTRACT FROM AUTHOR]

Transmembrane 9 superfamily proteins (TM9SFs) define a highly conserved protein family, each member of which is characterized by a variable extracellular domain and presumably nine transmembrane domains. Although previous studies have delineated the potential cytological roles of TM9SFs like autophagy and secretory pathway, their functions during development are largely unknown. To establish the basis for dissecting the functions of TM9SFs in vivo , we employed the open-source database, structure prediction, immunofluorescence and Western blot to describe the gene and protein expression patterns of TM9SFs in human and mouse. While TM9SFs are ubiquitously and homogeneously expressed in all tissues in human with RNA sequencing and proteomics analysis, we found that all mice Tm9sf proteins are preferentially expressed in lung except Tm9sf1 which is enriched in brain although they all distributed in various tissues we examined. In addition, we further explored their expression patterns in the mice central nervous system (CNS) and its extension tissue retina. Interestingly, we could show that Tm9sf1is developmentally up-regulated in brain. In addition, we also detected all Tm9sf proteins are located in neurons and microglia instead of astrocytes. Importantly, Tm9sf3 is localized in the nuclei which is distinct from the other members that are dominantly targeted to the plasma membrane/cytoplasm as expected. Finally, we also found that Tm9sf family members are broadly expressed in the layers of INL, OPL, and GCL of retina and likely targeted to the plasma membrane of retinal cells. Thus, our data provided a comprehensive overview of TM9SFs expression patterns, illustrating their ubiquitous roles in different organs, implying the possible roles of Tm9sf2/3/4 in lung functions and Tm9sf1 in neurodevelopment, and highlighting a unique cell biological functions of TM9SF3 in neuronal and microglia. [ABSTRACT FROM AUTHOR]

Muti-focus image fusion is the extraction of focused regions from different images to create one all-in-focus fused image. The key point is that only objects within the depth-of-field have a sharp appearance in the photograph, while other objects are likely to be blurred. We propose an unsupervised deep learning model for multi-focus image fusion. We train an encoder–decoder network in an unsupervised manner to acquire deep features of input images. Then, we utilize spatial frequency, a gradient-based method to measure sharp variation from these deep features, to reflect activity levels. We apply some consistency verification methods to adjust the decision map and draw out the fused result. Our method analyzes sharp appearances in deep features instead of original images, which can be seen as another success story of unsupervised learning in image processing. Experimental results demonstrate that the proposed method achieves state-of-the-art fusion performance compared to 16 fusion methods in objective and subjective assessments, especially in gradient-based fusion metrics. [ABSTRACT FROM AUTHOR]

Simulating the frictional properties of complex interfaces is computational resource consuming. In this paper, we propose a density functional theory (DFT) calculation combined machine learning (ML) strategy to investigate the sliding potential energy corrugation between geometrical corrugated graphene (Gr) sheets. By the aid of few DFT calculations and geometrical descriptors Σr−n (n = 1, 2, 6, 12), the trained ML models can accurately predict the sliding potential evolutions of Gr/Pt and Gr/Re systems. To be specific, based on DFT calculations of sliding along [110] direction, the trained linear regression (LIN) models can properly give out the potential energy evolution along the [100] direction with deviation less than 5%. By the dataset of given distances (9.3 Ĺ, 9.65 Ĺ and 10 Ĺ) between two Re monolayers in Gr/Re systems, LIN and Bayesian ridge regression (BR) models can quantitatively predict the potential energy evolution of unknown distances (9.2 Ĺ, 9.4 Ĺ, 9.5 Ĺ and 9.6 Ĺ). The predicted magnitudes of potential energy corrugations by BR model divert less than 3 meV Ĺ−2 from DFT calculations. The prediction results for extrapolated distances (9.0 Ĺ and 9.1 Ĺ) deviate notably, but the extension of training dataset effectively improves the predictive ability of ML models, especially for the LIN model. Thus, the supposed strategy could become an effective method to investigate the frictional characteristics of complex interfaces. [ABSTRACT FROM AUTHOR]

Summary: The microscopic image is important data for recording the microstructure information of materials. Researchers usually use image‐processing algorithms to extract material features from that and then characterise the material microstructure. However, the microscopic images obtained by a microscope often have random damaged regions, which will cause the loss of information and thus inevitably influence the accuracy of microstructural characterisation, even lead to a wrong result. To handle this problem, we provide a deep learning‐based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al–La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption. Lay Description: A basic goal of materials data analysis is to extract useful information from materials datasets that can in turn be used to establish connections along the composition–processing–structure–properties chain. The microscopic images obtained by a microscope is the key carrier of material microstructural information. Researchers usually use image analysis algorithms to extract regions of interest or useful features from microscopic images, aiming to analyse material microstructure, organ tissues or device quality etc. Therefore, the integrity and clarity of the microscopic image are the most important attributes for image feature extraction. Scientists and engineers have been trying to develop various technologies to obtain perfect microscopic images. However, in practice, some extrinsic defects are often introduced during the preparation and/or shooting processes, and the elimination of these defects often requires mass efforts and cost, or even is impossible at present. Take the microstructure image of metallic material for example, samples prepared to microstructure characterisation often need to go through several steps such as cutting, grinding with sandpaper, polishing, etching, and cleaning. During the grinding and polishing process, defects such as scratches could be introduced. During the etching and cleaning process, some defects such as rust caused by substandard etching, stains etc. may arise and be persisted. These defects can be treated as damaged regions with nonfixed positions, different sizes, and random shapes, resulting in the loss of information, which seriously affects subsequent visual observation and microstructural feature extraction. To handle this problem, we provide a deep learning‐based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al–La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterisation results compared to other image inpainting software for both accuracy and time consumption. [ABSTRACT FROM AUTHOR]

Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • The interlayer sliding energy barriers of MoS 2 with polarization was predicted by the ML method. • A charge density normalization method was proposed to accelerate and simplify the preparation of training DFT charge density dataset. • The LR model can accurately fit the energy changes during sliding in both parallel and anti-parallel stacking modes of MoS 2. • The prediction error of sliding potential energy barrier can be well controlled within 5%. Machine learning is an effective method to predict the potential energy surface of interlayer sliding between two-dimensional (2D) materials but with less density functional theory (DFT) calculation burden. In this study, the machine learning method has been used to predict the sliding energy barriers between two MoS 2 layers with both parallel and anti-parallel stacking modes, respectively. The sliding energy barrier prediction of anti-parallel stacking modes with only structure factors as features deviates from the DFT calculations remarkably. However, the introduction of interlayer charge density as the prediction feature significantly improves the prediction accuracy of anti-parallel stacking modes. This study enlightens the precise prediction of sliding energy barriers of polarized 2D materials with machine learning method. [ABSTRACT FROM AUTHOR]

[Display omitted] • We have proposed an augmentation strategy for empirical formula based on machine learning mothed. • A novel empirical formula of Ms as the function of compositions was fitted based on an augmented dataset combined experimental data with predicted data. • Compared with previous empirical formula, the accuracy and robustness of the novel empirical formula we proposed was significantly improved without additional experimental cost. • This strategy offers a recipe to build empirical formula based on a small experimental dataset. Tuning the martensite transformation temperature through composition design has become an important way to broaden the applicable temperature range of shape memory alloys (SMAs). The empirical formula based on traditional statistics is a key reference for composition design. Due to the lack of experimental data, a large deviation may exist among the prediction results from the empirical formulas obtained by different data sources. In present work, we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a Gaussian radial basis kernel function support vector machine (SVR.rbf) model was screened out based on mathematical and domain knowledge criteria. An augmented empirical formula of Ms as the function of compositions was fitted based on the abundant augmented dataset combined experimental data with predicted data by the SVR.rbf model. Compared with previous empirical formula fitted by small experimental dataset, the accuracy and robustness of the augmented empirical formula was significantly improved without additional experimental cost. This strategy offers a recipe to build empirical formula based on a small experimental dataset. [ABSTRACT FROM AUTHOR]

Rare earth microalloying is one of the most important methods to improve the mechanical properties of die steels. This paper investigates the relationships among addition of La, microstructure, and mechanical properties of H13 steel. The influences of following factors on obdurability improvement of La‐microalloyed H13 steel, such as grain refinement, deoxidization and desulphurization, grain boundary. and precipitations, have been studied. The results indicate that with the increase of La content in the range of 0–0.45 wt%, the primary dendritic spacing and average grain size of as‐cast samples decrease at the beginning and then increase. The tensile strength(σb), yield strength(σs), and impact toughness(αk) of hot‐forged samples after quenching and tempering treatments increase at the beginning and then decrease with the increase of La content. The mechanical test results indicate that with 0.03 wt% La content, the obdurability of hot–forged H13 steel after quenching and tempering treatments increases obviously. As compared with La‐free H13 steel, the σb and σs of hot‐forged H13 steel containing 0.03 wt% La increase by 5.0% and 4.6%, αk increases by 67.8% from 29.0 to 48.7 J·cm−2. La‐microalloyed effect is attributed to grain refinement, La2O2S precipitations and dislocation density of H13 steel. Compared with La‐free hot‐forged samples, the compact toughness of samples containing 0.03 wt% La increases by 67.8% from 29.0 to 48.7 J·cm–2. Appropriate addition of La contributes to grain refinement, La2O2S precipitation, the proportion of low angle grain boundaries, and density of dislocation of H13 steels. It is indicated that La‐microalloying contributes to the obdurability improvement of H13 steels. [ABSTRACT FROM AUTHOR]