Jun Lu

Bio: Jun Lu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Medicine & Materials science. The author has an hindex of 135, co-authored 1526 publications receiving 99767 citations. Previous affiliations of Jun Lu include Drexel University & Argonne National Laboratory.

Mn3O4 Nanocrystals: Facile Synthesis, Controlled Assembly, and Application

Abstract: We report a facile process for the controllable synthesis of the Mn3O4 nanocrystals with different sizes and shapes, which includes dots, rods, and wires in the presence of the surfactants dodecanol and oleylamine. It is notable that the uniform-sized nanocrystals were achieved under mild experimental conditions and the common inorganic salt, such as manganese(II) nitrate, was adopted as the precursor. Furthermore, the as-prepared monodisperse nanocrystals, as ideal building blocks, can be rationally assembled into three-dimensional (3D) Mn3O4 colloidal spheres, using a facile ultrasonication strategy. In particular, the 3D colloidal spheres can be successfully converted to LiMn2O4 nanocrystals, which show distinct electrochemical performance, mainly depending on their crystallinity and size.

Advances in the Development of Single‐Atom Catalysts for High‐Energy‐Density Lithium–Sulfur Batteries

Abstract: Although lithium–sulfur (Li–S) batteries are promising next‐generation energy‐storage systems, their practical applications are limited by the growth of Li dendrites and lithium polysulfide shuttling. These problems can be mitigated through the use of single‐atom catalysts (SACs), which exhibit the advantages of maximal atom utilization efficiency (≈100%) and unique catalytic properties, thus effectively enhancing the performance of electrode materials in energy‐storage devices. This review systematically summarizes the recent progress in SACs intended for use in Li‐metal anodes, S cathodes, and separators, briefly introducing the operating principles of Li–S batteries, the action mechanisms of the corresponding SACs, and the fundamentals of SACs activity, and then comprehensively describes the main strategies for SACs synthesis. Subsequently, the applications of SACs and the principles of SACs operation in reinforced Li–S batteries as well as other metal–S batteries are individually illustrated, and the major challenges of SACs usage in Li–S batteries as well as future development directions are presented.

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.