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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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Potential dependence of the orientation of thiocyanate adsorbed on an iron electrode as probed by surface-enhanced Raman Spectroscopy

TL;DR: In this paper, surface-enhanced Raman spectroscopy has been extended to the study of a bare iron electrode (without any deposition of other noble metals) exposed to the neutral solution containing thiocyanate over a wide potential range from −1.8 to −0.4 V. The applied potential has a significant influence on the Raman frequency shift of SCN- as a result of the electrochemical Stark effect.
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Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy (SHINERS) Based on Gold-Core Silica-Shell Nanorods

TL;DR: In this article, a dog bone-shaped gold nanorod was used to detect parathion pesticide on an orange peel by shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS).
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Enhanced-Raman scattering from silicon nanoparticle substrates

TL;DR: In this paper, two kinds of Raman enhancement effects contributing to the overall Raman signal on the roughened silicon surfaces were found: the electromagnetic cavity resonance effect occurring on the particles at the submicron scale, and the resonant Raman effect occurring in the nanometer scale.
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Green and sustainable carbon aerogels from starch for supercapacitors and oil-water separation

TL;DR: In this paper, green and sustainable carbon aerogels have been prepared using starch as raw material, and they have been further employed as the electrode material for supercapacitors.
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NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

TL;DR: A general category of fine-grained structured pruning applicable to various DNN layers is proposed, and a comprehensive, compiler automatic code generation framework supporting different DNNs and different pruning schemes are proposed, which bridge the gap of model compression and NAS.