Databases of 2D material-substrate interfaces and 2D charged building blocks

Discovery of materials using“bottom-up”or“top-down”approach is of great interest in materials science.Layered materials consisting of two-dimensional(2D)building blocks provide a good platform to explore new materials in this respect.In van der Waals(vdW)layered materials,these building blocks are charge neutral and can be isolated from their bulk phase(top-down),but usually grow on substrate.In ionic layered materials,they are charged and usually cannot exist independently but can serve as motifs to construct new materials(bottom-up).In this paper,we introduce our recently constructed databases for 2D material-substrate interface(2DMSI),and 2D charged building blocks.For 2DMSI database,we systematically build a workflow to predict appropriate substrates and their geometries at substrates,and construct the 2DMSI database.For the 2D charged building block database,1208 entries from bulk material database are identified.Information of crystal structure,valence state,source,dimension and so on is provided for each entry with a json format.We also show its application in designing and searching for new functional layered materials.The 2DMSI database,building block database,and designed layered materials are available in Science Data Bank at https://doi.org/10.57760/sciencedb.j00113.00188.

Design and Realization of Image Database of Pitaya Disease

With the expansion of cultivation scale,pitaya diseases are gradually increasing.Traditionally relying on human observation to judge the disease is limited by the skills and experience of the observer,which cannot guarantee the accuracy and real-time of the judgment,and consumes much manpower and time.In this study,by collecting,segmenting,and labeling images of 4 main diseases of pitaya in the field,an image database of main diseases of pitaya in the field was constructed to provide a basis for computer image recognition of pitaya diseases.Thereby,it benefits reducing manual error and improving the accuracy and real-time of disease identification for agricultural production,but also lays a foundation for the future development of intelligent agriculture.