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Chemistry, Ames Laboratory

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Accepted Manuscript

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Surface Science



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Metal nanoparticles (NPs) loaded on oxides have been widely used as multifunctional nanomaterials in various fields such as optical imaging, sensors, and heterogeneous catalysis. However, the deposition of metal NPs on oxide supports with high efficiency and homogeneous dispersion still remains elusive, especially when silica is used as the support. Amino-functionalization of silica can improve loading efficiency, but metal NPs often aggregate on the surface. Herein, we report that a facial annealing of amino-functionalized silica can significantly improve the dispersion and enhance the loading efficiency of various metal NPs, such as Pt, Rh, and Ru, on the silica surface. A series of characterization techniques, such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), Zeta potential analysis, UV–Vis spectroscopy, thermogravimetric analysis coupled with infrared analysis (TGA–IR), and nitrogen physisorption, were employed to study the changes of surface properties of the amino-functionalized silica before and after annealing. We found that the annealed amino-functionalized silica surface has more cross-linked silanol groups and relatively lesser amount of amino groups, and less positively charges, which could be the key to the uniform deposition of metal NPs during the loading process. These results could contribute to the preparation of metal/oxide hybrid NPs for the applications that require uniform dispersion.


This is a manuscript of an article published as Pei, Yuchen, Chaoxian Xiao, Tian-Wei Goh, Qianhui Zhang, Shannon Goes, Weijun Sun, and Wenyu Huang. "Tuning surface properties of amino-functionalized silica for metal nanoparticle loading: The vital role of an annealing process." Surface Science 648 (2016): 299-306. doi: 10.1016/j.susc.2015.10.019. Posted with permission.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Elsevier B.V.



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