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.

Relating Catalysis between Fuel Cell and Metal-Air Batteries

Abstract: Summary With the ever-increasing demand for higher-performing energy-storage systems, electrocatalysis has become a major topic of interest in an attempt to enhance the electrochemical performance of many electrochemical technologies. Discoveries pertaining to the oxygen reduction reaction catalyst helped enable the commercialization of fuel-cell-based electric vehicles. However, a closely related technology, the metal-air battery, has yet to find commercial application. Much like the Li-ion battery, metal-air batteries can potentially utilize the electrical grid network for charging, bypassing the need for establishing a hydrogen infrastructure. Among the metal-air batteries, Li-air and Zn-air batteries have drawn much interest in the past decade. Unfortunately, state-of-the art metal-air batteries still produce performances that are well below practical levels. In this brief perspective, we hope to bridge some of the ideas from fuel cell to that of metal-air batteries with the aim of inspiring new ideas and directions for future research.

Present Advances and Future Perspectives of Molecular Targeted Therapy for Osteosarcoma

Abstract: Osteosarcoma (OS) is a bone cancer mostly occurring in pediatric population. Current treatment regime of surgery and intensive chemotherapy could cure about 60%–75% patients with primary osteosarcoma, however only 15% to 30% can be cured when pulmonary metastasis or relapse has taken place. Hence, novel precise OS-targeting therapies are being developed with the hope of addressing this issue. This review summarizes the current development of molecular mechanisms and targets for osteosarcoma. Therapies that target these mechanisms with updated information on clinical trials are also reviewed. Meanwhile, we further discuss novel therapeutic targets and OS-targeting drug delivery systems. In conclusion, a full insight in OS pathogenesis and OS-targeting strategies would help us explore novel targeted therapies for metastatic osteosarcoma.

Crystallization characteristics and chemical bonding properties of nickel carbide thin film nanocomposites

Abstract: The crystal structure and chemical bonding of magnetron-sputtering deposited nickel carbide Ni$_{1-x}$C$_{x}$ (0.05$\leq$x$\leq$0.62) thin films have been investigated by high-resolution X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy, and soft X-ray absorption spectroscopy. By using X-ray as well as electron diffraction, we found carbon-containing hcp-Ni (hcp-NiC$_{y}$ phase), instead of the expected rhombohedral-Ni$_{3}$C. At low carbon content (4.9 at\%) the thin film consists of hcp-NiC$_{y}$ nanocrystallites mixed with a smaller amount of fcc-NiC$_{x}$. The average grain size is about 10-20 nm. With the increase of carbon content to 16.3 at\%, the film contains single-phase hcp-NiC$_{y}$ nanocrystallites with expanded lattice parameters. With further increase of carbon content to 38 at\%, and 62 at\%, the films transform to X-ray amorphous materials with hcp-NiC$_{y}$ and fcc-NiC$_{x }$ nanodomain structures in an amorphous carbon-rich matrix. Raman spectra of carbon indicate dominant $sp^{2}$ hybridization, consistent with photoelectron spectra that show a decreasing amount of C-Ni phase with increasing carbon content. The Ni $3d$ - C $2p$ hybridization in the hexagonal structure gives rise to the salient double-peak structure in Ni $2p$ soft X-ray absorption spectra at 16.3 at\% that changes with carbon content. We also show that the resistivity is not only governed by the amount of carbon, but increases by more than a factor of two when the samples transform from crystalline to amorphous.

Thermodynamic and kinetic destabilization of magnesium hydride using Mg-In solid solution alloys.

Abstract: Efforts to thermodynamically destabilize magnesium hydride (MgH2), so that it can be used for practical hydrogen storage applications, have been a difficult challenge that has eluded scientists for decades. This letter reports that MgH2 can indeed be destabilized by forming solid solution alloys of magnesium with group III and IVB elements, such as indium. Results of this research showed that the equilibrium hydrogen pressure of a Mg–0.1In alloy is 70% higher than that of pure MgH2. The temperature at 1 bar hydrogen pressure (T1bar) of Mg–0.1In alloy was reduced to 262.9 °C from 278.9 °C, which is the T1bar of pure MgH2. Furthermore, the kinetic rates of dehydrogenation of Mg–0.1In alloy hydride doped with a titanium intermetallic (TiMn2) catalyst were also significantly improved compared with those of MgH2.

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.