Theoretical study on the morphology of cobalt nanoparticles modulated by alkali metal promoters

Cobalt nanoparticles(NPs)catalysts are extensively used in heterogeneous catalytic reactions,and the addition of alkali metal promoters is a common method to modulate the catalytic performance because the catalyst's surface structures and morphologies are sensitive to the addition of promoters.However,the underlying modulation trend remains unclear.Herein,the adsorption of alkali metal promoters(Na and K)on the surfaces of face-centered-cubic(FCC)and hexagonal-closest packed(HCP)polymorphous cobalt was systematically investigated using density functional theory.Furthermore,the effect of alkali promoters on surface energies and nanoparticle morphologies was revealed on the basis of Wulff theory.For FCC-Co,the exposed area of the(111)facet in the nanoparticle increases with the adsorption coverage of alkali metal oxide.Meanwhile,the(311),(110),and(100)facets would disappear under the higher adsorption coverage of alkali metals.For HCPCo,the Wulff morphology is dominated by the(0001)and(1011)facets and is independent of the alkali metal adsorption coverage.This work provides insights into morphology modulation by alkali metal promoters for the rational design and synthesis of cobalt-based nanomaterials with desired facets and morphologies.

Integrated data analysis on the electron temperature profile of HL-2A with the Bayesian probability inference method

Data analysis on tokamak plasmas is mainly based on various diagnostic systems,which are usually modularized and independent of each other.This leads to a large amount of data not being fully and effectively exploited so that it is not conducive to revealing the deep physical mechanism.In this work,Bayesian probability inference with machine learning methods have been applied to the electron cyclotron emission and Thomson scattering diagnostic systems on HL-2A/2M,and the effects of integrated data analysis(IDA)on the electron temperature of HL-2A with Bayesian probability inference are demonstrated.A program is developed to infer the whole electron temperature profile with a confidence interval,and the program can be applied in online analysis.The IDA results show that the full profile of the electron temperature can be obtained and the diagnostic information is more comprehensive and abundant with IDA.The inference models for electron temperature analysis are established and the developed programs will serve as an experimental data analysis tool for HL-2A/2M in the near future.

Plagiarism Detection in Homework Based on Image Hashing

The problem of high similarity in homework has troubled teachers with time. Previous plagiarism detection systems are mainly realized by string matching which has a limitation, i.e., image homework cannot be detected. To this issue, we propose a new method of plagiarism detection in homework. First,we get fingerprint features of image homework by converting text homework into images. Then, we use image hashing algorithm and hamming distance to calculate the similarity of these features. Finally, we perform the empirical study on course of Computer Network Experiment, the test shows that our method not only reliably keeps the detection speedily, but also consistently ensures precision and false positive rate.

Machine learning predicts atomistic structures of multielement solid surfaces for heterogeneous catalysts in variable environments

Solid surfaces usually reach thermodynamic equilibrium through particle exchange with their environment under reactive conditions.A prerequisite for understanding their functionalities is detailed knowledge of the surface composition and atomistic geometry under working conditions.Owing to the large number of possible Miller indices and terminations involved in multielement solids,extensive sampling of the compositional and conformational space needed for reliable surface energy estimation is beyond the scope of ab initio calculations.Here,we demonstrate,using the case of iron carbides in environments with varied carbon chemical potentials,that the stable surface composition and geometry of multielement solids under reactive conditions,which involve large compositional and conformational spaces,can be predicted at ab initio accuracy using an approach that combines the bond valence model,Gaussian process regression,and ab initio thermodynamics.Determining the atomistic structure of surfaces under working conditions paves the way toward identifying the true active sites of multielement catalysts in heterogeneous catalysis.