光谱学与光谱分析 |
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Preparation, Characterization and Upconversion Fluorescence of NaYF4∶Yb, Er /Graphene Oxide Nanocomposites |
JI Tian-hao1, QIE Nan1, WANG Ji-mei2, HUA Yong-yong1, JI Zhi-jiang2 |
1. College of Science, Beijing Technology and Business University, Beijing 100048, China 2. State Key Lab of Green Building Materials, China Building Materials Acaclemy, Beijing 100024, China |
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Abstract NaYF4∶Yb, Er/rGO and SiO2-coated NaYF4∶Yb, Er/rGO nanocomposites can be prepared through “one-pot” and directly mixing preparation routes. Various measurement results show that the NaYF4∶Yb, Er in the nanocomposites exhibits a cubic α-type structure and nanoparticle-like morphology with a diameter range of 30~70 nm; the rGO layers are well-dispersed in the nanocomposites, and whereas the rGO obtained from “one-pot” preparation renders relatively better dispersion. Raman spectra demonstrate that there exists a surface coupling action between the two kinds of nanomaterials, and with the increase in the relative rGO content, such action becomes stronger. UC fluorescence measurement results reveal that the rGO has significantly quenching effect and optical-limiting performance on the UC fluorescence, particularly on the red-emission of the NaYF4∶Yb, Er or SiO2-coated NaYF4∶Yb, Er nanoparticles. The red-emission intensity gradually decreases with an increase in the rGO content, but the green-emission shows less change. It should be stressed that in comparison with NaYF4∶Yb, Er/rGO, with a similar rGO content, the red-emission intensity of SiO2-coated NaYF4∶Yb, Er/rGO decreases much obviously due to a stronger light-absorption caused by part rGO aggregation.
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Received: 2012-08-13
Accepted: 2012-11-08
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Corresponding Authors:
JI Tian-hao
E-mail: jitianhao@th.btbu.edu.cn
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[1] Pei S F, Cheng H M. Carbon, 2012, 50: 3210. [2] Eda G, Lin Y Y, Mattevi C, et al. Adv. Mater., 2010, 22: 505. [3] Lu Z, Guo C X, Yang H B, et al. J. Colloid Interface Sci., 2011, 353: 588. [4] WANG Xiao-dan, ZHOU Ning-lin, WANG Wei-yan, et al(王晓丹,周宁琳,汪炜燕,等). J. Funct. Mater.(功能材料), 2011, 1: 104. [5] Zhu S J, Tang S J, Zhang J H, et al. Chem. Commun., 2012, 48: 4527. [6] MIN Shi-xiong, Lü Gong-xuan(敏世雄, 吕功煊). Acta Phys. Chim. Sin.(物理化学学报), 2011, 27: 2178. [7] Wang Y, Yao H B, Wang X H, et al. J. Mater. Chem., 2011, 21: 562. [8] Wei W, He T C, Teng X, et al. Small, 2012, 8: 2271. [9] Xiong L, Yang T, Yang Y, et al. Biomaterial, 2010, 31: 7078. [10] Chen S, Wang S W, Zhang J, et al. J. Nanosci. Nanotechnol., 2009, 9: 1942. [11] CHEN Shi, ZHOU Guo-hong, ZHANG Hai-long, et al(陈 实,周国红,张海龙,等). J. Inorg. Mater.(无机材料学报), 2010, 25: 1128. [12] Masia M, Forbert H, Marx D. J. Phys. Chem. A, 2007, 111: 12181. [13] Narula R, Reich S. Phys. Rev. B, 2008, 78: 165422. [14] Pollnau M, Gamelin D R, Luthi S R, et al. Phys. Rev. B, 2001, 61: 3337. [15] Lim G K, Chen Z L, Clark J, et al. Nat. Photonics, 2011, 5: 554. [16] Nair R R, Blake P, Grigorenko A N, et al. Science, 2008, 320: 1308. |
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