A
Aixia Lu
Researcher at Jiangsu Normal University
Publications - 8
Citations - 67
Aixia Lu is an academic researcher from Jiangsu Normal University. The author has contributed to research in topics: Raman scattering & Surface-enhanced Raman spectroscopy. The author has an hindex of 3, co-authored 8 publications receiving 34 citations.
Papers
More filters
Journal ArticleDOI
Rapid identification of gutter oil by detecting the capsaicin using surface enhanced Raman spectroscopy
TL;DR: In this paper, surface enhanced Raman spectroscopy (SERS) with silver nanorod array substrates was used to detect the capsaicin, a marker of the gutter oil that is difficult to remove.
Journal ArticleDOI
Highly Sensitive Silver Nanorod Arrays for Rapid Surface Enhanced Raman Scattering Detection of Acetamiprid Pesticides
Caiqin Han,Yue Yao,Wen Wang,Liu-qian Tao,Wenxin Zhang,Whitney Ingram,Tian Kangzhen,Ying Liu,Aixia Lu,Ying Wu,Changchun Yan,Lu-Lu Qu,Haitao Li +12 more
TL;DR: In this paper, a surface-enhanced Raman scattering (SERS) based method was used to detect acetamiprid pesticide residue on a cucumber's surface, and the results confirmed possibility of utilizing the AgNRs SERS substrates as a new method for highly sensitive pesticide residue detection.
Journal ArticleDOI
Study on the components of isopropanol aqueous solution
TL;DR: In this paper, the components of the isopropanol aqueous solution based on Time-Correlated Single Photon Counting (TCSPC) and Density Functional Theory (DFT) were studied.
Journal ArticleDOI
L-shaped ITO structures fabricated by oblique angle deposition technique for mid-infrared circular dichroism.
TL;DR: This paper proposes a mid-infrared chiral structure, which consists of L-shaped indium tin oxide (ITO) films formed on self-assembled monolayer polystyrene microspheres in two orthogonal directions by oblique angle deposition technique and demonstrates that the structure exhibit circular dichroism (CD) responses in the range of 2.5 - 4 µm.
Proceedings ArticleDOI
Rapid Classification of Honey Varieties by Surface Enhanced Raman Scattering Combining with Deep Learning
TL;DR: In this paper, a new method based on surface enhanced Raman spectroscopy and deep learning is developed to classify the varieties of honey, which used the difference of honey SERS spectrum to classify.