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Bin Ren
Researcher at Xiamen University
Publications - 528
Citations - 30728
Bin Ren is an academic researcher from Xiamen University. The author has contributed to research in topics: Raman spectroscopy & Surface-enhanced Raman spectroscopy. The author has an hindex of 73, co-authored 470 publications receiving 23452 citations. Previous affiliations of Bin Ren include Pacific Northwest National Laboratory & Max Planck Society.
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Poaceae-specific cell wall-derived oligosaccharides activate plant immunity via OsCERK1 during Magnaporthe oryzae infection in rice
TL;DR: In this paper, a fungal pathogen Magnaporthe oryzae secretes the endoglucanases MoCel12A and MoCEL12B during infection of rice (Oryza sativa).
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Characterization of surface water on Au core Pt-group metal shell nanoparticles coated electrodes by surface-enhanced Raman spectroscopy
Yu-Xiong Jiang,Jian-Fen Li,De-Yin Wu,Zhilin Yang,Bin Ren,Jiawen Hu,Yuan L. Chow,Zhong-Qun Tian +7 more
TL;DR: This work utilized the strategy of 'borrowing SERS activity', by chemically coating several atomic layers of a Pt-group metal on highly SERS-active Au nanoparticles, to obtain the first SERS (also Raman) spectra of surface water on Pt and Pd metals.
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Uniform gold spherical particles for single-particle surface-enhanced Raman spectroscopy.
Haixin Lin,Jieming Li,Bi-Ju Liu,Deyu Liu,Jinxuan Liu,Andreas Terfort,Zhaoxiong Xie,Zhong-Qun Tian,Bin Ren +8 more
TL;DR: Single particle dark-field spectroscopy and SERS measurements show that particles with a larger roughness give a stronger SERS signal, but still retain a good reproducibility, which points to the promising future of the practical application of the single particle SERS technique for trace analysis.
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A grape-like N-doped carbon/CuO-Fe2O3 nanocomposite as a highly active heterogeneous Fenton-like catalyst in methylene blue degradation
TL;DR: An N-doped carbon/CuO-Fe2O3 (NC-CuFe) nanocomposite was successfully prepared via a simple, green one-pot annealing method.
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Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives.
TL;DR: This Feature focuses on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging.