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- 摘要
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- 实验方案
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The Antibody-Free Recognition of Cancer Cells Using Plasmonic Biosensor Platforms with the Anisotropic Resonant Metasurfaces
摘要： It is vital and promising for portable and disposable biosensing devices to achieve on-site detection and analysis of cancer cells. Although traditional labelling techniques provide an accurate quantitative measurement, the complicated cell staining and high-cost measurements limit its further development. Here, we demonstrate a non-immune biosensing technology. The plasmonic biosensors which is based on anisotropic resonant split ring resonators in terahertz range successfully realize the antibody-free recognition of cancer cells. The dependences of Δf and fitted phase slope (FPS) on the cancer cell concentration at different polarizations give new perspective in hexagonal radar maps. The results indicate that the lung cancer cell A549 and liver cancer cell HepG2 can be distinguished and determined simply based on the enclosed shapes in the radar maps without any antibody introduction. The minimum concentration of identification reduces as low as 1×104 cells/ml and such identification can be kept valid in a large range of cell concentration, ranging from 104 to 105. The construction of two-dimensional extinction intensity cards of corresponding cancer cells based on the wavelet transform method also supplies corresponding information for the antibody-free recognition and determination of two cancer cells. Our plasmonic MBs show a great potential in the determination and recognition of label-free cancer cells, being an alternative to non-immune biosensing technology.
关键词： terahertz,antibody-free biosensing,cancer cells,metasurfaces,continuous wavelet transform
更新于2025-09-23 15:19:57
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Quantitative diagnosis method of beam defects based on laser Doppler non-contact random vibration measurement
摘要： The beam structure is prone to defect damage during its use, and the rapid quantitative diagnosis of the beam structure can detect the defects of the beam in real time and quantitatively. In this article, the method of obtaining the vibration time-domain signal under random excitation of beam structure is proposed by using random vibration excitation and Laser Doppler principle. Based on this, the defect quantitative identification algorithm of beam structure is proposed based on fast Fourier, continuous wavelet transform and convolutional neural network. The random vibration of different parts of steel beams with artificial defects is measured by Laser Doppler method. The experimental results show that the defect size of the beam structure can be effectively identified only by the random vibration signal of the finite point. The method is expected to help to develop an online real-time assessment instrument for beam structure defects in service state.
关键词： Continuous wavelet transform,Beam defects,Laser Doppler,Non-contact random excitation,Quantitative diagnosis,Convolutional neural network
更新于2025-09-11 14:15:04
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Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis
摘要： In research and development laboratories, chemical or pharmaceutical analysis has been carried out by evaluating sample signals obtained from instruments. However, the qualitative and quantitative determination based on raw signals may not be always possible due to sample complexity. In such cases, there is a need for powerful signal processing methodologies that can effectively process raw signals to get correct results. Wavelet transform is one of the most indispensable and popular signal processing methods currently used for noise removal, background correction, differentiation, data smoothing and filtering, data compression and separation of overlapping signals etc. This review article describes the theoretical aspects of wavelet transform (i.e., discrete, continuous and fractional) and its characteristic applications in UV spectroscopic analysis of pharmaceuticals.
关键词： UV spectroscopy,continuous wavelet transform,discrete wavelet transform,pharmaceutical analysis,fractional wavelet transform
更新于2025-09-10 09:29:36
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Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods
摘要： Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. We explored reflectance spectroscopy as an alternative method for assessing heavy metals. Four spectral transformation methods, first-order differential (FDR), second-order differential (SDR), continuum removal (CR) and continuous wavelet transform (CWT), are used for the original spectral data. Spectral preprocessing effectively eliminated the noise and baseline drifting and also highlighted the locations of the spectral feature bands. Partial least squares regression (PLSR) and radial basis function neural network (RBF) were used to study the hyperspectral inversion of four heavy metals (Cr, As, Ni, Cd). The inversion models of four heavy metals were established in the bands with the highest correlation coefficient. The inversion effects were evaluated by the coefficient of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) indexes. The R values of the correlation coefficient were significantly improved after smoothing and spectral transformation compared to the original waveband. The method combining continuous wavelet transform (CWT) with radial basis function neural network (RBF) had the best inversion effect on the four heavy metals. When compared to partial least squares regression (PLSR), the RMSE values were reduced by approximately 2. The CWT-RBF method can be used as a means of inversion of heavy metals in mining wasteland reclaimed land.
关键词： Continuous Wavelet Transform,Heavy metal,Spectral analysis,Radial Basis Function Neural Network,Reclamation soil
更新于2025-09-04 15:30:14