研究目的
To optimize the SERS performance of 3D substrates through tunable 3D plasmonic coupling for label-free liver cancer cell classification.
研究成果
The study demonstrated that tuning the plasmonic coupling at a 3D multiscale can significantly improve the SERS performance of 3D substrates, enabling their application in ultrasensitive analysis and label-free classification of living cells. The optimized 3D substrate showed a correlation between the redshift of the plasmonic resonance wavelength and the increase in SERS enhancement, achieving a nearly 10-fold improvement in ASEF. The substrate was successfully applied for the quantitative detection of biomolecules and the classification of liver cancer cells with high accuracy.
研究不足
The study acknowledges the challenges in preparing 3D nanostructures in a controllable manner and the limited penetration depth of light into the 3D substrates, which affects the SERS intensity saturation with increasing number of layers.
1：Experimental Design and Method Selection:
The study involved the preparation of 3D hierarchical SERS substrates with controlled nanostructures to tune 3D plasmonic coupling. The methodology included the synthesis of uniform Au nanooctahedra (AuNO) with varying side lengths, the fabrication of 3D SERS substrates through air-water interface-assisted self-assembly in a layer-by-layer fashion, and the characterization of these substrates using SEM, UV-Vis-NIR spectroscopy, and SERS spectroscopy.
2：Sample Selection and Data Sources:
The samples included 3D SERS substrates with varying structural elements (0D, 1D, and 2D) and living liver normal and cancer cells for label-free classification.
3：List of Experimental Equipment and Materials:
Instruments used included HITACH H-7650 TEM, HITACH S-4800 SEM, Agilent Cary 5000 UV-Vis-NIR spectrometer, and LabRAM Aramis Confocal Raman Microscopy. Materials included HAuCl4·3H2O, NaBH4, PVP, CTAB, MBA, 3-Butenoic acid, and others.
4：Experimental Procedures and Operational Workflow:
The experimental procedures involved the synthesis of AuNO, the fabrication of 3D SERS substrates, the measurement of UV-Vis-NIR extinction and SERS spectra, and the application of these substrates for the quantitative analysis of biomolecules and the classification of living cells.
5：Data Analysis Methods:
The data analysis included the decomposition of broad extinction peaks by multiple peak-fitting using a Gaussian lineshape, the calculation of the averaged surface enhancement factor (ASEF), and the use of principal component analysis (PCA) for the classification of cell types.
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