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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
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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

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.