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.

An efficient kinetic resolution of racemic Betti base based on an enantioselective N,O-deketalization.

Abstract: An efficient kinetic resolution of racemic Betti base with L-(+)-tartaric acid in acetone was developed based on a novel enantioselective N,O-deketalization, by which the enantiopure R- and S-enantiomers of Betti base were obtained as the corresponding N,O-ketal compound and salt with L-(+)-tartaric acid, respectively, in excellent yields with a practically foolproof operation.

Field-induced domain wall propagation velocity in magnetic nanowires

Abstract: A thory of field-induced domain wall (DW) propagation is developed. The theory not only explains why a DW in a defect-free nanowire must propagate at a finite velocity, but also provides a proper definition of DW propagation velocity. This definition, valid for an arbitrary DW structure, allows one to compute the instantaneous DW velocity in a meaningful way even when the DW is not moving as a rigid body. A new velocity-field formula beyond the Walker breakdown field, which is in excellent agreement with both experiments and numerical simulations, is derived.

Structure of a new bulk Ti5Al2C3 MAX phase produced by the topotactic transformation of Ti2AlC

Abstract: Upon annealing cold-pressed Ti 2 AlC, −325 mesh powders, at 1500 °C for 8 h in argon, the resulting partially sintered sample contained 43(±2) wt.% of the layered ternary carbide Ti 5 Al 2 C 3 . Herein, the X-ray powder diffraction pattern of Ti 5 Al 2 C 3 is reported for the first time and its structure and stoichiometry are confirmed through high-resolution transmission electron microscopy. This phase has a trigonal structure (space group P3m1) with a unit cell consisting of 3 formula units and cell parameters of a = 3.064 A, c = 48.23 A. The lattice parameters determined through first principles calculations agree reasonably well with the experimentally determined values. At 147.1 GPa, the calculated bulk modulus falls between the bulk moduli of Ti 2 AlC and Ti 3 AlC 2 . The transformation from Ti 2 AlC to Ti 5 Al 2 C 3 is topotactic.

γ-Fe₂O₃ Nanocrystalline Microspheres with Hybrid Behavior of Battery-Supercapacitor for Superior Lithium Storage.

Abstract: Maghemite (γ-Fe2O3) nanocrystalline microspheres (MNMs) self-assembled with 52 nm nanocrystals bridged with FeOOH around grain boundaries were formed by solvothermal reaction and thermal oxidation. The unique architecture endows the MNMs with the lithium storage behavior of a hybrid battery-supercapacitor electrode: initial charge capacity of 1060 mAh g(-1) at the 100 mA g(-1) rate, stable cyclic capacity of 1077.9 mAh g(-1) at the same rate after 140 cycles, and rate capability of 538.8 mAh g(-1) at 2400 mA g(-1). This outstanding performance was attributed to the nanocrystal superiority, which shortens the Li(+) diffusion paths. The mechanism of this hybrid anode material was investigated with experimental measurements and structural analysis. The results indicate that at the first discharge, the MNM nanocrystal microsphere, whose structure can buffer the volume change that occurs during lithiation/delithiation, goes through four stages: Li(+) insertion in cation vacancies, spinel-to-rocksalt transformation, Li(+) intercalation of Li(1.75+x)Fe2O3 nanocrystals, and interfacial Li storage around nanocrystal boundaries. Only the latter two stages were reversible at and after the second charging/discharging cycle, exhibiting the hybrid behavior of a battery-supercapacitor with superior lithium storage.

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.