科研成果

2006
Eom D, Jiang C-S, Yu H-B, Shi J, Niu Q, Ebert P, Shih C-K. Scanning tunneling spectroscopy of Ag films: The effect of periodic versus quasiperiodic modulation. Physical Review Letters. 2006;(20).
Eom D, Jiang C-S, Yu H-B, Shi J, Niu Q, Ebert P, Shih C-K. Scanning tunneling spectroscopy of Ag films: The effect of periodic versus quasiperiodic modulation. Physical Review Letters. 2006;(20).
H. ZY, H. ZX, J. HJ, Z. L, D. FY, T. GW, G. LX, X. GY, Moyo NM. Search for signature inversion in the pi i(13/2) circle times upsilon i1(3/2) bands in Au-182,Au-184,Au-186. International Journal of Modern Physics E-Nuclear Physics. 2006;15:1437.
Zhang Y-H, Zhou X-H, He J-J, Liu Z, Fang Y-D, Guo W-T, Lei X-G, Guo Y-X, Ma L. Search for signature inversion in the pi i(13/2)circle times nu i(13/2) bands in Au-182,Au-184,Au-186. High Energy Physics and Nuclear Physics-Chinese Edition. 2006;30:208.
Wang B, Fu P, Liu J, Wu B. Self-trapping of Bose-Einstein condensates in optical lattices. Physical Review A - Atomic, Molecular, and Optical Physics. 2006;(6).
Jing Z, Yu L, Jiuhu S, Jie Z. The semantic analysis about the spatial orientation expression of GIS in Chinese-case study of Beijing, in Proceedings of SPIE - The International Society for Optical Engineering.Vol 6420.; 2006. 访问链接
Xiao B, Zhong G, Obayashi M, Yang D, Chen K, Walsh MP, Shimoni Y, Cheng H, Keurs HT, Chen WSR. Ser-2030, but not Ser-2808, is the major phosphorylation site in cardiac ryanodine receptors responding to protein kinase A activation upon β-adrenergic stimulation in normal and failing hearts. Biochemical Journal [Internet]. 2006;(1):7-16. 访问链接
Shape coexistence in light Krypton isotopes. High Energy Physics and Nuclear Physics-Chinese Edition. 2006;30:121.
Shape of the K-pi=7(-) isomer in Ce-130. High Energy Physics and Nuclear Physics-Chinese Edition. 2006;30:100.
LH Z, ZL Z, XG W, FR X, GS L, ZM W, CY H, Y W, R M. Shape-driving effect of the proton 1/2[541] band in Ta-171. European Physical Journal A. 2006;27:137.
N. OJ, M. BA, A. E, P. BA, D. DG, T. K, M. C, H. E-M, J. PC. Shape-driving effects in the triaxial nucleus, Xe-128. Physical Review C. 2006;74:034318.
Shapes of neutron-rich A   190 odd-odd nuclei. Physical Review C. 2006;74:067303.
Shell-model calculations of beta decays in N-18. Chinese Physics Letters. 2006;23:2046.
Gong H-Q, Peng Y-B, Zou C, Wang D-H, Xu Z-H, Bai S-N. A simple treatment to significantly increase signal specificity in immunohistochemistry. Plant Molecular Biology Reporter. 2006;(1):93-101.
Sun D, Ku PC. Slow light in resonant Raman systems for high-speed applications. Optics InfoBase Conference Papers. 2006.
Bijoor N, Li W-J, Zhang Q, Huang G. Small-scale co-management for the sustainable use of Xilingol Biosphere Reserve,Inner Mongolia. AMBIO. 2006;(35):25-29.
Song Y, Zhang Y, Xie S, Zeng L, Zheng M, Salmon LG, Shao M, Slanina S. Source apportionment of PM2. 5 in Beijing by positive matrix factorization. Atmospheric Environment. 2006;40:1526–1537.
Song Y, Xie S, Zhang Y, Zeng L, Salmon LG, Zheng M. Source apportionment of PM2. 5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Science of the Total Environment [Internet]. 2006;372:278–286. 访问链接Abstract
Source apportionment of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burningindustrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.  
Song Y, Zhang Y*, Xie S, Zeng L, Zheng M*, Salmon LG, Shao M, Slanina S. Source apportionment of PM2.5 in Beijing by positive matrix factorization. Atmospheric Environment [Internet]. 2006;40:1526 - 1537. LINKAbstract
Air pollution associated with atmospheric fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5μm or less) is a serious problem in Beijing, China. To provide a better understanding of the sources contributing to PM2.5, 24-h samples were collected at 6-day intervals in January, April, July, and October in 2000 at five locations in the Beijing metropolitan area. Both backward trajectory and elemental analyses identified two dust storm events; the distinctly low value of Ca:Si (<0.2) and high Al:Ca (>1.7) in Beijing PM2.5 appear indicative of contributions from dust storms. Positive matrix factorization (PMF) was used to apportion sources of PM2.5, and eight sources were identified: biomass burning (11%), secondary sulfates (17%), secondary nitrates (14%), coal combustion (19%), industry (6%), motor vehicles (6%), road dust (9%), and yellow dust. The lower organic carbon (OC), elemental carbon (EC), SO42−, and Ca values of yellow dust enable it to be distinguished from road dust. The PMF method resolved 82% of PM2.5 mass concentrations and showed excellent agreement with a previous calculation using organic tracers in a chemical mass balance (CMB) model. The present study is the first reported comparison between a PMF source apportionment model and a molecular marker-based CMB in Beijing.
Song Y*, Xie S, Zhang Y, Zeng L, Salmon LG, Zheng M*. Source apportionment of PM2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Science of The Total Environment [Internet]. 2006;372:278 - 286. LINKAbstract
Source apportionment of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 μm or less) in Beijing, China, was determined using two eigenvector models, principal component analysis/absolute principal component scores (PCA/APCS) and UNMIX. The data used in this study were from the chemical analysis of 24-h samples, which were collected at 6-day intervals in January, April, July, and October 2000 in the Beijing metropolitan area. Both models identified five sources of PM2.5: secondary sulfate and secondary nitrate, a mixed source of coal combustion and biomass burning, industrial emission, motor vehicles exhaust, and road dust. On average, the PCA/APCS and UNMIX models resolved 73% and 85% of the PM2.5 mass concentrations, respectively. The results were comparable to previous estimate using the positive matrix factorization (PMF) and chemical mass balance (CMB) receptor models. Secondary products and the emissions from coal combustion and biomass burning dominated PM2.5. Such comparison among various receptor models, which contain different physical constraints, is important for better understanding PM2.5 sources.

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