Low-cost Au nanoparticle-decorated cicada wing as sensitive and recyclable substrates for surface enhanced Raman scattering

Lv, Mingyang; Teng, Hai Yan; Chen, Zhao Yang; Zhao, Yong; Zhang, Xin; Liu, Luo; Wu, Zhigang; Liu, Li Min; Xu, Hai Jun

doi:10.1016/j.snb.2014.12.061

Cited by 57 publications

(27 citation statements)

References 36 publications

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“…As an alternative, cicada and butterfly wings with the hydrophobicity nature and ordered nanostructures were evaluated as SERS substrates. 27 , 28 Yet, their poor mechanical strength and short storage time hamper its practical application.…”

Section: Introductionmentioning

confidence: 99%

Self-Assembly of Au@Ag Nanoparticles on Mussel Shell To Form Large-Scale 3D Supercrystals as Natural SERS Substrates for the Detection of Pathogenic Bacteria

et al. 2018

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Herein, we developed a natural surface-enhanced Raman scattering (SERS) substrate based on size-tunable Au@Ag nanoparticle-coated mussel shell to form large-scale three-dimensional (3D) supercrystals (up to 10 cm2) that exhibit surface-laminated structures and crossed nanoplates and nanochannels. The high content of CaCO3 in the mussel shell results in superior hydrophobicity for analyte enrichment, and the crossed nanoplates and nanochannels provided rich SERS hot spots, which together lead to high sensitivity. Finite-difference time-domain simulations showed that nanoparticles in the channels exhibit apparently a higher electromagnetic field enhancement than nanoparticles on the platelets. Thus, under optimized conditions (using Au@AgNPs with 5 nm shell thickness), highly sensitive SERS detection with a detection limit as low as 10–9 M for rhodamine 6G was obtained. Moreover, the maximum electromagnetic field enhancement of different types of 3D supercrystals shows no apparent difference, and Au@AgNPs were uniformly distributed such that reproducible SERS measurements with a 6.5% variation (613 cm–1 peak) over 20 spectra were achieved. More importantly, the as-prepared SERS substrates can be utilized for the fast discrimination of Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa by discriminant analysis. This novel Au@Ag self-assembled mussel shell template holds considerable promise as low-cost, durable, sensitive, and reproducible substrates for future SERS-based biosensors.

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Section: Introductionmentioning

confidence: 99%

Self-Assembly of Au@Ag Nanoparticles on Mussel Shell To Form Large-Scale 3D Supercrystals as Natural SERS Substrates for the Detection of Pathogenic Bacteria

et al. 2018

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“…Figure 1 d shows the quantitative relationship curve between the integrated SERS intensity of the 1361 cm −1 peak ( I ) and the concentration of the R6G solution ( C ). When logarithm-scale axes are used, a linear response between log I and log C is obtained, which indicated that this linear relationship allowed for the determination of unknown concentration of R6G in solutions [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Quantitative and Sensitive Detection of Chloramphenicol by Surface-Enhanced Raman Scattering

Ding

Zhang

Yin

et al. 2017

Sensors

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We used surface-enhanced Raman scattering (SERS) for the quantitative and sensitive detection of chloramphenicol (CAP). Using 30 nm colloidal Au nanoparticles (NPs), a low detection limit for CAP of 10−8 M was obtained. The characteristic Raman peak of CAP centered at 1344 cm−1 was used for the rapid quantitative detection of CAP in three different types of CAP eye drops, and the accuracy of the measurement result was verified by high-performance liquid chromatography (HPLC). The experimental results reveal that the SERS technique based on colloidal Au NPs is accurate and sensitive, and can be used for the rapid detection of various antibiotics.

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“…During the Raman detection no samples will be touched by chemical substance. It had already been used in quality control of pesticide formulations [15]. Tianfeng Xu developed a method to detect chlorpyrifos on apples through Raman spectroscopy technology [16].…”

Section: Introductionmentioning

confidence: 99%

A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra

Peng

Qin

et al. 2019

Applied Sciences

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In the study, a new correction method was applied to reduce error during Raman spectral detection on mixed pesticide residue in apples. Combined with self-built pesticide residues detection system by Raman spectroscopy and the application of surface enhancement technology, rapid real-time qualitative and quantitative analysis of deltamethrin and acetamiprid residues in apples could be applied effectively. In quantitative analysis, compared with the intensity value of characteristic peaks of single pesticide with same concentration, the intensity value of characteristic peaks of the two pesticides decreased after mixing the pesticides, which affected the results severely. By comparing the difference in the intensity of characteristic peaks of single and mixed pesticides, a correction method was proposed to eliminate the influence of pesticides mixture. Characteristic peak intensity values of gradient concentration pesticide from 100 mg·kg−1 to 10−3 mg·kg−1 and Lagrangian interpolation were applied in the correction method. And a smooth surface was applied to describe the correction coefficient of characteristic peak intensity. Through detecting the characteristic peak intensity values of the mixed pesticide, correction coefficient would be obtained. Then real values of the peak intensity of pesticides and the content of each component of the mixed pesticide would be acquired by the correction method. Correlation coefficient of model validation exceeded 0.88 generally and Root Mean Square Error also decreased obviously after correction, which proved the reliability of the method.

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Low-cost Au nanoparticle-decorated cicada wing as sensitive and recyclable substrates for surface enhanced Raman scattering

Cited by 57 publications

References 36 publications

Self-Assembly of Au@Ag Nanoparticles on Mussel Shell To Form Large-Scale 3D Supercrystals as Natural SERS Substrates for the Detection of Pathogenic Bacteria

Self-Assembly of Au@Ag Nanoparticles on Mussel Shell To Form Large-Scale 3D Supercrystals as Natural SERS Substrates for the Detection of Pathogenic Bacteria

Quantitative and Sensitive Detection of Chloramphenicol by Surface-Enhanced Raman Scattering

A Correction Method of Mixed Pesticide Content Prediction in Apple by Using Raman Spectra

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