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.

Superhard NbB2 −x thin films deposited by dc magnetron sputtering

Abstract: We have deposited weakly textured substoichiometric NbB2 −x thin films by magnetron sputtering from an NbB2 target. The films exhibit superhardness (42 ± 4 GPa), previously only observed in overstoichiometric TiB2 thin films, and explained by a self-organized nanostructuring, where thin TiB2 columnar grains hinder nucleation and slip of dislocations and a B-rich tissue phase between the grains prevent grain-boundary sliding. The wide homogeneity range for the NbB2 phase allows a similar ultra-thin B-rich tissue phase to form between thin (5–10 nm) columnar NbB2 −x grains also for films with a B/Nb atomic ratio of 1.8, as revealed here by analytical aberration-corrected scanning transmission electron microscopy. Furthermore, a coefficient of friction of 0.16 is measured for an NbB2 −x film sliding against stainless steel with a wear rate of 5 × 10− 7 mm3/Nm. X-ray photoelectron spectroscopy results suggest that the low friction is due to the formation of a lubricating boric acid film.

Amorphous TiCu-Based Additives for Improving Hydrogen Storage Properties of Magnesium Hydride.

Abstract: Magnesium hydride has long been regarded as a promising candidate material for hydrogen and heat storage due to its high hydrogen capacity, reversibility, and low cost. Catalytic doping has been de...

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.