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.

Hematite nanodiscs exposing (001) facets: synthesis, formation mechanism and application for Li-ion batteries

Abstract: 27 nm thick hematite nanodiscs have been prepared by a facile solvothermal method The growth of Fe2O3 nanodiscs follows the precipitation–dissolution–growth mechanism, and the (001) facets are preferentially exposed since (001) facets are the densest and therefore most stable facets In particular, outstanding rate and cycling capabilities have been demonstrated for the nanodiscs due to the reduced Li+ diffusion distance and enhanced reactivity of the nanosized structures

Organic-acid-assisted fabrication of low-cost Li-rich cathode material (Li[Li1/6Fe1/6Ni1/6Mn1/2]O2) for lithium-ion battery.

Abstract: A novel Li-rich cathode Li[Li1/6Fe1/6Ni1/6Mn1/2]O2 (0.4Li2MnO3—0.6LiFe1/3Ni1/3Mn1/3O2) was synthesized by a sol–gel method, which uses citric acid (SC), tartaric acid (ST), or adipic acid (SA) as a chelating agent. The structural, morphological, and electrochemical properties of the prepared samples were characterized by various methods. X-ray diffraction showed that single-phase materials are formed mainly with typical α-NaFeO2 layered structure (R3m), and the SC sample has the lowest Li/Ni cation disorder. The morphological study indicated homogeneous primary particles in good distribution size (100 nm) with small aggregates. The Fe, Ni, and Mn valences were determined by X-ray absorption near-edge structure analysis. In coin cell tests, the initial reversible discharge capacity of an SA electrode was 289.7 mAh g–1 at the 0.1C rate in the 1.5–4.8 V voltage range, while an SC electrode showed a better cycling stability with relatively high capacity retention. At the 2C rate, the SC electrode can deliver...

Phonon thermal conductivity of scandium nitride for thermoelectrics from first-principles calculations and thin-film growth

Abstract: The knowledge of lattice thermal conductivity of materials under realistic conditions is vitally important since many modern technologies require either high or low thermal conductivity. Here, we propose a theoretical model for determining lattice thermal conductivity, which takes into account the effect of microstructure. It is based on ab initio description that includes the temperature dependence of the interatomic force constants and treats anharmonic lattice vibrations. We choose ScN as a model system, comparing the computational predictions to the experimental data by time-domain thermoreflectance. Our experimental results show a trend of reduction in lattice thermal conductivity with decreasing domain size predicted by the theoretical model. These results suggest a possibility to control thermal conductivity by microstructural tailoring and provide a predictive tool for the effect of the microstructure on the lattice thermal conductivity of materials based on ab initio calculations.

Dye-sensitized Nanocrystalline Ti-oxide-based Solar Cells Prepared by Sputtering: Influence of the Substrate Temperature During Deposition

Abstract: Dye-sensitized Nanocrystalline Ti-oxide-based Solar Cells Prepared by Sputtering: Influence of the Substrate Temperature During Deposition

Effect of overexpression of LPAAT and GPD1 on lipid synthesis and composition in green microalga Chlamydomonas reinhardtii

Abstract: Biodiesel is an alternative energy source which has attracted increasing attention lately. Although algae-based biodiesel production has many benefits, it is still far from industrial application. Research suggests that improving lipid quality and production through genetic engineering of metabolic pathways will be the most promising way. To enhance lipid content, both lysophosphatidic acyltransferase gene (c-lpaat) and glycerol-3-phosphate dehydrogenase gene (c-gpd1), optimized according to the codon bias of Chlamydomonas reinhardtii, were inserted into the genomic DNA of model microalga C. reinhardtii by the glass bead method. Transgenic algae were screened by zeomycin resistance and RT-PCR. The transcription levels of inserted genes and the fatty acid content were significantly increased after intermittent heat shock. Most of all, the transcription levels of c-lpaat and c-gpd1 were increased 5.3 and 8.6 times after triple heat shocks, resulting in an increase of 44.5 and 67.5% lipid content, respectively. Furthermore, the content of long-chain saturated fatty acids and monounsaturated fatty acids, especially C18 and C18:1t, notably increased, while unsaturated fatty acids dramatically decreased. The results of this study offer a new strategy combining genetic manipulation and intermittent heat shock to enhance lipid production, especially the production of long-chain saturated fatty acids, using C. reinhardtii.

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.