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.

Synthesis of Porous Carbon Supported Palladium Nanoparticle Catalysts By Atomic Layer Deposition: Application for Rechargeable Lithium-O2 Battery

Abstract: In this study, atomic layer deposition (ALD) was used to deposit nanostructured palladium on porous carbon as the cathode material for Li-O2 cells. Scanning transmission electron microscopy showed discrete crystalline nanoparticles decorating the surface of the porous carbon support, where the size could be controlled in the range of 2-8 nm and depended on the number of Pd ALD cycles performed. X-ray absorption spectroscopy at the Pd K-edge revealed that the carbon supported Pd existed in a mixed phase of metallic palladium and palladium oxide. The conformality of ALD allowed us to uniformly disperse the Pd catalyst onto the carbon support while preserving the initial porous structure. As a result, the charging and discharging performance of the oxygen cathode in a Li-O2 cell was improved. Our results suggest that ALD is a promising technique for tailoring the surface composition and structure of nanoporous supports in energy storage devices.

Spin relaxation and dephasing mechanism in (Ga,Mn)As studied by time-resolved Kerr rotation

Abstract: Spin dynamics in (Ga,Mn)As films grown on GaAs(001) was investigated by Time-resolved magneto-optical Kerr effect. The Kerr signal decay time of (Ga,Mn)As without external magnetic field applied was found to be several hundreds picoseconds, which suggested that photogenerated polarized holes and magnetic ions are coupled as a ferromagnetic system. Nonmonotonic temperature dependence of relaxation and dephasing (R&D) time and Larmor frequency manifests that Bir–Aronov–Pikus mechanism dominates the spin R&D time at low temperature, while D’yakonov–Perel mechanism dominates the spin R&D time at high temperature, and the crossover between the two regimes is Curie temperature.

Thermal Conductivity Test of YBCO Coated Conductor Tape Stacks Interleaved With Insulated Stainless Steel Tapes

Abstract: A 32 Tesla, all-superconducting user magnet, which consists of two high temperature superconductor YBCO inner coils producing a field of 17 T in an low temperature superconductor Nb3Sn and NbTi outer magnet producing a background field of 15 T, is being developed at the National High Magnetic Field Laboratory. The YBCO inner coils are pancake-wound with YBCO coated conductor tapes with an interleaved insulation of sol-gel coated stainless steel tapes. The coils are to be cooled directly in liquid helium bath. Heat losses in the windings, such as ac losses during ramping and heat loss in the internal joints, are supposed to be transferred to the coil external surfaces through heat conduction. Thus, thermal conductivity of the coil structure is critical for the internal cooling of the coil and also quench propagation if any. Thermal conductivity measurements were carried out in the radial direction on stacks of alternating YBCO tapes and stainless steel tapes. This paper presents the test results that showed a very low thermal conductivity in the radial direction. For comparison purposes, calculated thermal conductivities in the axial and azimuthal direction are also 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.