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.

The Absence and Importance of Operando Techniques for Metal-Free Catalysts.

Abstract: Operando characterization techniques have played a crucial role in modern technological developments. In contrast to the experimental uncertainties introduced by ex situ techniques, the simultaneous measurement of desired sample characteristics and near-realistic electrochemical testing provides a representative picture of the underlying physics. From Li-ion batteries to metal-based electrocatalysts, the insights offered by real-time characterization data have enabled more efficient research programs. As an emerging class of catalyst, much of the mechanistic understanding of metal-free electrocatalysts continues to be elusive in comparison to their metal-based counterparts. However, there is a clear absence of operando characterization performed on metal-free catalysts. Through the proper execution of operando techniques, it can be expected that metal-free catalysts can achieve exceptional technological progress. Here, the motivation of using operando characterization techniques for metal-free carbon-based catalyst system is considered, followed by a discussion of the possibilities, difficulties and benefits of their applications.

Strontium diffusion in magnetron sputtered gadolinia-doped ceria thin film barrier coatings for solid oxide fuel cells

Abstract: Strontium (Sr) diffusion in magnetron sputtered gadolinia-doped ceria (CGO) thin films is investigated. For this purpose, a model system consisting of a screen printed (La,Sr)(Co,Fe)O3−δ (LSCF) lay ...

Low-temperature growth of dense and hard Ti0.41Al0.51Ta0.08N films via hybrid HIPIMS/DC magnetron co-sputtering with synchronized metal-ion irradiation

Abstract: Hard Ti1−xAlxN thin films are of importance for metal-cutting applications. The hardness, thermal stability, and oxidation resistance of these coatings can be further enhanced by alloying with TaN. We use a hybrid high-power pulsed and dc magnetron co-sputtering (HIPIMS/DCMS) technique to grow dense and hard Ti0.41Al0.51Ta0.08N alloys without external heating (Ts < 150 °C). Separate Ti and Al targets operating in the DCMS mode maintain a deposition rate of ∼50 nm/min, while irradiation of the growing film by heavy Ta+/Ta2+ ions from the HIPIMS-powered Ta target, using dc bias synchronized to the metal-ion-rich part of each HIPIMS pulse, provides effective near-surface atomic mixing resulting in densification. The substrate is maintained at floating potential between the short bias pulses to minimize Ar+ bombardment, which typically leads to high compressive stress. Transmission and scanning electron microscopy analyses reveal dramatic differences in the microstructure of the co-sputtered HIPIMS/DCMS films...

Ratios of bottom meson branching fractions involving J/ psi mesons and determination of b quark fragmentation fractions.

Abstract: We report a measurement of the ratios of the decay rates of the ${B}^{+}$, ${B}^{0}$, and ${B}_{s}^{0}$ mesons into exclusive final states containing a $\frac{J}{\ensuremath{\psi}}$ meson. The final states were selected from 19.6 pb-1 of $p\overline{p}$ collisions recorded by the Collider Detector at Fermilab. These data are interpreted to determine the $b$ quark fragmentation fractions ${f}_{u}$, ${f}_{d}$, and ${f}_{s}$. We also determine the branching fractions for the decay modes ${B}^{+}\ensuremath{\rightarrow}\frac{J}{\ensuremath{\psi}{K}^{+}}$, ${B}^{+}\ensuremath{\rightarrow}\frac{J}{\ensuremath{\psi}{K}^{*}{(892)}^{+}}$, ${B}^{0}\ensuremath{\rightarrow}\frac{J}{\ensuremath{\psi}{K}^{0}}$, ${B}^{0}\ensuremath{\rightarrow}\frac{J}{\ensuremath{\psi}{K}^{*}{(892)}^{0}}$, and ${B}_{s}^{0}\ensuremath{\rightarrow}\frac{J}{\ensuremath{\psi}\ensuremath{\varphi}(1020)}$. We discuss the implications of these measurements to $B$ meson decay models.

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.