The Development of Data Science Education in China from the LIS Perspective

The aim of this paper is to introduce the development of data science in higher education in China, including the policy and educational programs at various levels. We investigated the data science education of five LIS (Library and Information Studies) schools in China, using Fudan University’s Data Management and Application Master’s Program as an example for more specific information about the curriculum structure, course focus and teaching methods in data science education. The paper further describes the action of promoting data science and data science education in the field of LIS by the China Academic Library Research Data Management Implementation Group

One Pot Synthesis of Large Gold Nanoparticles with Triple Functional Ferrocene Ligands

In biomedical, toxicological, and optoelectronic applications, the size of nanoparticles is one of the decisive factors. Therefore, synthesis of nanoparticles with controlled sizes is required. The current methods for synthesis of larger gold nanoparticles (GNPs, ~200 nm) are complex and tedious, producing nanoparticles with a lower yield and more irregular shapes. Using ferrocene as a primary reducing agent and stabilizer, sodium citrate as a dispersant, and sodium borohydride as an accessory reducing agent, GNPs of 200 nm were synthesized in a one pot reaction. Besides the roles of reducing agent and GNP stabilizer, ferrocene also served a role of quantitative marker for ligand loading, allowing an accurate determinate of surface ligands

Universal nanohydrophobicity predictions using virtual nanoparticle library

Abstract To facilitate the development of new nanomaterials, especially nanomedicines, a novel computational approach was developed to precisely predict the hydrophobicity of gold nanoparticles (GNPs). The core of this study was to develop a large virtual gold nanoparticle (vGNP) library with computational nanostructure simulations. Based on the vGNP library, a nanohydrophobicity model was developed and then validated against externally synthesized and tested GNPs. This approach and resulted model is an efficient and effective universal tool to visualize and predict critical physicochemical properties of new nanomaterials before synthesis, guiding nanomaterial design