Yunfei Xie

Large-area unmodified superhydrophobic copper substrate can be prepared by an electroless replacement deposition.

Using an electroless replacement deposition method, large-area superhydrophobic metal substrate could be obtained. The superhydrophobic surfaces were prepared via a replacement reaction between copper substrate and HAuCl(4) solution. The roughness of the copper substrate increased much after the replacement reaction. X-ray powder diffraction (XRD) pattern and energy dispersive X-ray (EDX) spectroscopy have proved that gold, CuCl and Cu(2)O formed on the surface of copper substrate after the replacement reaction. The surface showed remarkable superhydrophobic properties with a contact angle higher than 150 degrees without any modification with a self-assembled monolayer (SAM) of long chain thiol or perfluoro molecules.

Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine

It is important to monitor quality of tobacco during the production of cigarette. Therefore, in order to scientifically control the tobacco raw material and guarantee the cigarette quality, fast and accurate determination routine chemical of constituents of tobacco, including the total sugar, reducing sugar, Nicotine, the total nitrogen and so on, is needed. In this study, 50 samples of tobacco from different cultivation areas were surveyed by near-infrared (NIR) spectroscopy, and the spectral differences provided enough quantitative analysis information for the tobacco. Partial least squares regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The quantitative analysis models of 50 tobacco samples were studied comparatively in this experiment using PLSR, ANN, radial basis function (RBF) SVM regression, and the parameters of the models were also discussed. The spectrum variables of 50 samples had been compressed through the wavelet transformation technology before the models were established. The best experimental results were obtained using the (RBF) SVM regression with gamma=1.5, 1.3, 0.9, and 0.1, separately corresponds to total sugar, reducing sugar, Nicotine, and total nitrogen, respectively. Finally, compared with the back propagation (BP-ANN) and PLSR approach, SVM algorithm showed its excellent generalization for quantitative analysis results, while the number of samples for establishing the model is smaller. The overall results show that NIR spectroscopy combined with SVM can be efficiently utilized for rapid and accurate analysis of routine chemical compositions in tobacco. Simultaneously, the research can serve as the technical support and the foundation of quantitative analysis of other NIR applications.

Highly sensitive protein concentration assay over a wide range via surface-enhanced Raman scattering of Coomassie brilliant blue.

In the Bradford protein assay, protein concentrations are determined by the absorbance at 595 nm due to the binding of Coomassie brilliant blue G-250 (CBBG) to proteins. In a protein-CBBG liquid mixture, surface-enhanced Raman scattering (SERS) is sensitive to the amount of unbound CBBG molecules adsorbed on silver surfaces, and the bound CBBG amount is directly related to the target protein concentration. Accordingly, a novel method for detecting total protein concentration in a solution has been developed based on SERS of unbound CBBG with an internal standard of silicon. Two obvious advantages of the proposed protein assay over conventional Bradford protein assay are its much wider linear concentration range (10(-5)-10(-9) g/mL) and 200 times lower limit of detection (1 ng/mL), which demonstrates its great potential in rapid, highly sensitive concentration determination of high and low-abundance proteins.

Label-Free Indirect Immunoassay Using an Avidin-Induced Surface-Enhanced Raman Scattering Substrate

Surface-enhanced Raman scattering (SERS) spectroscopy can facilitate the nondestructive and ultrasensitive characterization of biomolecules, and has thus attracted increasing interest in the fi eld of life sciences, [ 1–7 ] including in DNA-, protein-, cell-, and bacterial studies. Several studies have focused on the development of highly sensitive protocols using Raman-active labels, known as Raman dye-labeled immunoassays. [ 8–10 ] However, nonspecifi c adsorption of the Raman reporters often leads to false positive results, [ 11 ] thus necessitating the development of label-free approaches for probing immunoreactions. Two important aspects that should be considered before performing SERS-based, label-free immunoassays are that immunocomplexes normally have a low Raman cross-section, and that maintaining the bioactivity of the antibody or antigen after attaching it to metal nanoparticles (NPs) is crucial. Development of a label-free immunoassay using SERS remains an important challenge, because the SERS-active substrates currently used fail to combine biocompatibility and high sensitivity. Au and Ag NPs are commonly used SERS-active substrates that have wide applications in SERS-based research. [ 12 , 13 ] Colloidal Au with good biocompatibility has recently received increased attention for use in biosensors, in clinical diagnosis, and in drug detection. [ 6 , 14 ] However, the SERS enhancement factor provided by an Au surface in the visible region is much lower than that provided by an Ag surface. [ 15 , 16 ] Thus, protocols focusing on highly SERS-active and biocompatible substrates such as Ag/Au core/shell metallic NPs [ 16 ] have been developed for Raman dye-labeled immunoassays. To our knowledge, no such SERS-active substrate has been employed for label-free immunoassays. Two SERSbased studies involving label-free immunoassays have been reported, [ 17 , 18 ] both of which involved the direct attachment of proteins to Au or Ag surfaces, and the detection of antigen– antibody binding at a single, relatively high concentration of

Near-infrared spectroscopy quantitative determination of Pefloxacin mesylate concentration in pharmaceuticals by using partial least squares and principal component regression multivariate calibration

Pefloxacin mesylate, a broad-spectrum antibacterial fluoroquinolone, has been widely used in clinical practice. Therefore, it is very important to detect the concentration of Pefloxacin mesylate. In this research, the near-infrared spectroscopy (NIRS) has been applied to quantitatively analyze on 108 injection samples, which was divided into a calibration set containing 89 samples and a prediction set containing 19 samples randomly. In order to get a satisfying result, partial least square (PLS) regression and principal components regression (PCR) have been utilized to establish quantitative models. Also, the process of establishing the models, parameters of the models, and prediction results were discussed in detail. In the PLS regression, the values of the coefficient of determination (R(2)) and root mean square error of cross-validation (RMSECV) of PLS regression are 0.9263 and 0.00119, respectively. For comparison, though applying PCR method to get the values of R(2) and RMSECV we obtained are 0.9685 and 0.00108, respectively. And the values of the standard error of prediction set (SEP) of PLS and PCR models are 0.001480 and 0.001140. The result of the prediction set suggests that these two quantitative analysis models have excellent generalization ability and prediction precision. However, for this PFLX injection samples, the PCR quantitative analysis model achieved more accurate results than the PLS model. The experimental results showed that NIRS together with PCR method provide rapid and accurate quantitative analysis of PFLX injection samples. Moreover, this study supplied technical support for the further analysis of other injection samples in pharmaceuticals.

Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared(NIR) diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correction(MSC) according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares(PLS); the coefficient of determination (R2) of the prediction was 0.839, the root mean squared error of prediction(RMSEP) was 0.1422, and the mean relative error(RME) was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation(WT) technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network(ANN) instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.