Jiaqing Mo

Angle-resolved diffraction grating biosensor based on porous silicon

In this study, an optical biosensor based on a porous silicon composite structure was fabricated using a simple method. This structure consists of a thin, porous silicon surface diffraction grating and a one-dimensional porous silicon photonic crystal. An angle-resolved diffraction efficiency spectrum was obtained by measuring the diffraction efficiency at a range of incident angles. The angle-resolved diffraction efficiency of the 2nd and 3rd orders was studied experimentally and theoretically. The device was sensitive to the change of refractive index in the presence of a biomolecule indicated by the shift of the diffraction efficiency spectrum. The sensitivity of this sensor was investigated through use of an 8 base pair antifreeze protein DNA hybridization. The shifts of the angle-resolved diffraction efficiency spectrum showed a relationship with the change of the refractive index, and the detection limit of the biosensor reached 41.7 nM. This optical device is highly sensitive, inexpensive, and simp...

Fabrication of porous silicon-based silicon-on-insulator photonic crystal by electrochemical etching method

We present a fast, novel method for building porous silicon-based silicon-on-insulator photonic crystals in which a periodic modulation of the refractive index is built by alternating different electrochemical etching currents. The morphology and reflectance spectra of the photonic crystals, prepared by the proposed method, are investigated. The scanning electron micrograph and atomic force microscopy images show a very uniform structure and the porous silicon demonstrates an 829 nm wide photonic band gap.

Porous silicon biosensor for Echinococcosis detection based on fluorescence spectroscopy

Agriculture and animal husbandry area, such as Xinjiang, has high rates of hydatid disease. Protein P38 of Echinococcus granulosus has practical value in diagnosis of hydatid disease, and it may be used as a diagnostic marker and a prognostic index. In recent years, the development of biosensors based on porous silicon has been developed rapidly. In this experiment, the protein P38 detection based on fluorescence changes of porous silicon following protein P38 molecule adsorption. The results of the tests indicated that, with the increase of antigen concentration, the fluorescence decrease of porous silicon is also increasing. It is provided the foundation for the basic research of the molecular mechanism of P38, and diagnosis and treatment of cystic echinococcosis.

Biochemical sensing application based on optical fiber evanescent wave sensor

We have designed a novel evanescent field fiber optic biosensors with porous silicon dioxide cladding. The pore size of porous silicon dioxide cladding is about 100 nm in diameter. Biological molecules were immobilized to the porous silicon dioxide cladding used APTES and glutaraldehyde. Refractive index of cladding used Bruggemanns effective medium theory. We carried out simulations of changing in light intensity in optical fiber before and after chemical coupling of biomolecules. This novel optical fiber evanescent wave biosensor has a great potential in clinical chemistry for rapid and convenient determination of biological molecule.

Study on pattern recognition of Raman spectrum based on fuzzy neural network

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Interferometer immunosensor based on porous silicon for determining alpha-fetoprotein

An increased level of alpha-fetoprotein ( AFP) in the blood may be a sign of liver cancer. Porous silicon based optical microcavities structure is prepared as a label-free immunosensor platform for detecting AFP. After the antigen-antibody reaction, it is monitored that the red shift of the reflection spectrum of the immunosensor increases

Porous silicon-based two-dimensional photonic crystal for biochemical sensing applications

Various porous silicon-based photonic device structures has attracted more attention for use as biochemical optical sensors. In this study, we have designed and characterized porous silicon-based two-dimensional photonic crystal waveguide structure as an optical biosensor. Field intensity distribution of two-dimensional photonic crystal waveguide was simulated using COMSOL Multiphysics. When the refractive index changes, the field strength changes greatly. This study lays the theoretical foundation for further work.

A biosensor platform based on Raman spectroscopy of porous silicon

Porous silicon suitable for optical detection as a biosensor platform is fabricated. The morphology, structure and Raman properties of porous silicon have been studied and protein P38 of Echinococcus granulosus was determined by the porous silicon Raman intensity changes following protein P38 of Echinococcus granulosus molecule adsportion. The results of the tests indicated that, when antigen is added into the porous silicon, the Fourier transform Raman intensity decrease of porous silicon is also increasing.

A study of infrared spectroscopy de-noising based on LMS adaptive filter

Infrared spectroscopy has been widely used, but which often contains a lot of noise, so the spectral characteristic of the sample is seriously affected. Therefore the de-noising is very important in the spectrum analysis and processing. In the study of infrared spectroscopy, the least mean square (LMS) adaptive filter was applied in the field firstly. LMS adaptive filter algorithm can reserve the detail and envelope of the effective signal when the method was applied to infrared spectroscopy of breast cancer which signal-to-noise ratio (SNR) is lower than 10 dB, contrast and analysis the result with result of wavelet transform and ensemble empirical mode decomposition (EEMD). The three evaluation standards (SNR, root mean square error (RMSE) and the correlation coefficient (ρ)) fully proved de-noising advantages of LMS adaptive filter in infrared spectroscopy of breast cancer.

A novel de-noising method for B ultrasound images

B ultrasound as a kind of ultrasonic imaging, which has become the indispensable diagnosis method in clinical medicine. However, the presence of speckle noise in ultrasound image greatly reduces the image quality and interferes with the accuracy of the diagnosis. Therefore, how to construct a method which can eliminate the speckle noise effectively, and at the same time keep the image details effectively is the research target of the current ultrasonic image de-noising. This paper is intended to remove the inherent speckle noise of B ultrasound image. The novel algorithm proposed is based on both wavelet transformation of B ultrasound images and data fusion of B ultrasound images, with a smaller mean squared error (MSE) and greater signal to noise ratio (SNR) compared with other algorithms. The results of this study can effectively remove speckle noise from B ultrasound images, and can well preserved the details and edge information which will produce better visual effects.