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.

Prelithiated Li-Enriched Gradient Interphase toward Practical High-Energy NMC–Silicon Full Cell

Abstract: It is highly desirable to realize high-energy-density lithium-ion batteries consisting of nickel-rich layered oxide cathodes (Ni-rich NMC) and Si-based anodes. A critical challenge for Ni-rich NMC ...

Nitrogen-Doped Nanoporous Graphenic Carbon: An Efficient Conducting Support for O 2 Cathode

Abstract: Herein, we synthesize a nanoporous N-doped graphenic carbon with high surface area and a higher graphenization/graphitic degree by further developing the metallothermic reduction of gaseous CO2 by adding N2 into the gas flow. The N-doped nanoporous carbon is composed of both a highly porous graphenic and non-graphitic matrix and homogeneously dispersed ordered graphitic nanodomains, which constitute a very unique composite carbon structure. The resulting N-doped graphenic carbon exhibits much more favorable reactivity as a carbon conducting support in the O2 cathode of Li-O2 batteries, increasing the specific capacity of the GC electrode from 5300 to 9600 mAh g−1. The N-doped carbon also exhibits lower overpotentials during initial cycling for the charging process as well as an enhanced cycling performance compared to the undoped carbon. These results demonstrate that N-doping has a strong correlation with the enhanced performance of O2 cathode of Li-O2 batteries.

Unprecedented Al supersaturation in single-phase rock salt structure VAlN films by Al + subplantation

Abstract: Modern applications of refractory ceramic thin films, predominantly as wear-protective coatings on cutting tools and on components utilized in automotive engines, require a combination of excellent mechanical properties, thermal stability, and oxidation resistance. Conventional design approaches for transition metal nitride coatings with improved thermal and chemical stability are based on alloying with Al. It is well known that the solubility of Al in NaCl-structure transition metal nitrides is limited. Hence, the great challenge is to increase the Al concentration substantially while avoiding precipitation of the thermodynamically favored wurtzite-AlN phase, which is detrimental to mechanical properties. Here, we use VAlN as a model system to illustrate a new concept for the synthesis of metastable single-phase NaCl-structure thin films with the Al content far beyond solubility limits obtained with conventional plasma processes. This supersaturation is achieved by separating the film-forming species in ...

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.