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.

Amorphous MoS3 as the sulfur-equivalent cathode material for room-temperature Li-S and Na-S batteries.

Abstract: Many problems associated with Li–S and Na–S batteries essentially root in the generation of their soluble polysulfide intermediates. While conventional wisdom mainly focuses on trapping polysulfides at the cathode using various functional materials, few strategies are available at present to fully resolve or circumvent this long-standing issue. In this study, we propose the concept of sulfur-equivalent cathode materials, and demonstrate the great potential of amorphous MoS 3 as such a material for room-temperature Li–S and Na–S batteries. In Li–S batteries, MoS 3 exhibits sulfur-like behavior with large reversible specific capacity, excellent cycle life, and the possibility to achieve high areal capacity. Most remarkably, it is also fully cyclable in the carbonate electrolyte under a relatively high temperature of 55 °C. MoS 3 can also be used as the cathode material of even more challenging Na–S batteries to enable decent capacity and good cycle life. Operando X-ray absorption spectroscopy (XAS) experiments are carried out to track the structural evolution of MoS 3 . It largely preserves its chain-like structure during repetitive battery cycling without generating any free polysulfide intermediates.

High Capacity of Hard Carbon Anode in Na-Ion Batteries Unlocked by POx Doping

Abstract: The capacity of hard carbon anodes in Na-ion batteries rarely reaches values beyond 300 mAh/g. We report that doping POx into local structures of hard carbon increases its reversible capacity from 283 to 359 mAh/g. We confirm that the doped POx is redox inactive by X-ray adsorption near edge structure measurements, thus not contributing to the higher capacity. We observe two significant changes of hard carbon’s local structures caused by doping. First, the (002) d-spacing inside the turbostratic nanodomains is increased, revealed by both laboratory and synchrotron X-ray diffraction. Second, doping turns turbostratic nanodomains more defective along ab planes, indicated by neutron total scattering and the associated pair distribution function studies. The local structural changes of hard carbon are correlated to the higher capacity, where both the plateau and slope regions in the potential profiles are enhanced. Our study demonstrates that Na-ion storage in hard carbon heavily depends on carbon local struc...

Synthesis and Characterization of an Alumina Forming Nanolaminated Boride: MoAlB.

Abstract: The ‘MAlB’ phases are nanolaminated, ternary transition metal borides that consist of a transition metal boride sublattice interleaved by monolayers or bilayers of pure aluminum. However, their synthesis and properties remain largely unexplored. Herein, we synthesized dense, predominantly single-phase samples of one such compound, MoAlB, using a reactive hot pressing method. High-resolution scanning transmission electron microscopy confirmed the presence of two Al layers in between a Mo-B sublattice. Unique among the transition metal borides, MoAlB forms a dense, alumina scale when heated in air. Like other alumina formers, the oxidation kinetics follow a cubic time-dependence. At room temperature, its resistivity is low (0.36–0.49 μΩm) and – like a metal – drops linearly with decreasing temperatures. It is also a good thermal conductor (35 Wm−1K−1 at 26 °C). In the 25–1300 °C temperature range, its thermal expansion coefficient is 9.5 × 10−6K−1. Preliminary results suggest the compound is stable to at least 1400 °C in inert atmospheres. Moderately low Vickers hardness values of 10.6 ± 0.3 GPa, compared to other transition metal borides, and ultimate compressive strengths up to 1940 ± 103 MPa were measured at room temperature. These results are encouraging and warrant further study of this compound for potential use at high temperatures.

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.