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 a new nanocrystalline titanium aluminum fluoride phase by reaction of Ti2AlC with hydrofluoric acid

Abstract: A new nanocrystalline phase of titanium aluminum fluoride, with a stoichiometry of Ti(2)AlF(9)-exact stoichiometry measured herein is Ti(2.1)Al(0.9)F(9),-was synthesized by the fluorination of Ti(2 ...

Potentiometric glucose sensor based on the glucose oxidase immobilized iron ferrite magnetic particle/chitosan composite modified gold coated glass electrode

Abstract: A potentiometric glucose sensor based on the glucose oxidase immobilized on iron ferrite (Fe3O4) nanoparticles/chitosan composite modified gold coated glass substrate was fabricated. The electrode ...

Measurement of theWboson mass

Abstract: This paper presents a measurement of the mass of the W boson using data collected with the CDF detector during the 1992--1993 collider run at the Fermilab Tevatron. A fit to the transverse mass spectrum of a sample of 3268 W\ensuremath{\rightarrow}\ensuremath{\mu}\ensuremath{ u} events recorded in an integrated luminosity of 19.7 ${\mathrm{pb}}^{\mathrm{\ensuremath{-}}1}$ gives ${\mathit{M}}_{\mathit{W}}^{\mathrm{\ensuremath{\mu}}}$=80.310\ifmmode\pm\else\textpm\fi{}0.205 (stat)\ifmmode\pm\else\textpm\fi{}0.130 (syst) GeV/${\mathit{c}}^{2}$. A fit to the transverse mass spectrum of a sample of 5718 W\ensuremath{\rightarrow}e\ensuremath{ u} events recorded in 18.2 ${\mathrm{pb}}^{\mathrm{\ensuremath{-}}1}$ gives ${\mathit{M}}_{\mathit{W}}^{\mathit{e}}$=80.490\ifmmode\pm\else\textpm\fi{}0.145 (stat)\ifmmode\pm\else\textpm\fi{}0.175 (syst) GeV/${\mathit{c}}^{2}$. Combining the muon and electron results, accounting for correlated uncertainties, yields ${\mathit{M}}_{\mathit{W}}$=80.410\ifmmode\pm\else\textpm\fi{}0.180 GeV/${\mathit{c}}^{2}$.

Designing a hybrid electrode toward high energy density with a staged Li+ and PF6- deintercalation/intercalation mechanism.

Abstract: Existing lithium-ion battery technology is struggling to meet our increasing requirements for high energy density, long lifetime, and low-cost energy storage. Here, a hybrid electrode design is developed by a straightforward reengineering of commercial electrode materials, which has revolutionized the “rocking chair” mechanism by unlocking the role of anions in the electrolyte. Our proof-of-concept hybrid LiFePO4 (LFP)/graphite electrode works with a staged deintercalation/intercalation mechanism of Li+ cations and PF6− anions in a broadened voltage range, which was thoroughly studied by ex situ X-ray diffraction, ex situ Raman spectroscopy, and operando neutron powder diffraction. Introducing graphite into the hybrid electrode accelerates its conductivity, facilitating the rapid extraction/insertion of Li+ from/into the LFP phase in 2.5 to 4.0 V. This charge/discharge process, in turn, triggers the in situ formation of the cathode/electrolyte interphase (CEI) layer, reinforcing the structural integrity of the whole electrode at high voltage. Consequently, this hybrid LFP/graphite-20% electrode displays a high capacity and long-term cycling stability over 3,500 cycles at 10 C, superior to LFP and graphite cathodes. Importantly, the broadened voltage range and high capacity of the hybrid electrode enhance its energy density, which is leveraged further in a full-cell configuration.

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.