In this paper, a new method for the rapid, economical and convenient detection of cyclic adenosine monophosphate (cAMP) in jujube is proposed and verified. Based on near-infrared (NIR) fiber spectroscopy combined with stoichio-metric analysis, the cAMP content in red jujube can be quickly detected. 68 red jujube samples were used for the NIR spectroscopy data acquisition and the corresponding chemical values were determined. The sample set was adjusted based on the joint XY distance (SPXY) to select the correction sample set. After different preprocessing on the spectra, the partial least squares (PLS) method was used to establish the model, and the smoothed and normalized PLS model result was obtained better. The model's correction correlation coefficient (Rc), correction set mean square error (RMSEC), prediction correlation coefficient (Rp), and prediction and mean square error (RMSEP) are 0.951 5, 25.793 7, 0.910 8 and 28.228 0, respectively. The results show that NIR combined with specific chemometric methods can achieve rapid de-tection of cAMP in red jujube.

A Raman spectroscopy method combined with neural network is used for the invasive and rapid detection of echinococcosis. The Raman spectroscopy measurements are performed on two groups of blood serum samples, which are from 28 echinococcosis patients and 38 healthy persons, respectively. The normalized Raman reflection spectra show that the reflectivity of the echinococcosis blood serum is higher than that of the normal human blood serum in the wavelength ranges of 101—175 nm and 1 801—2 701 nm. Then the principal component analysis (PCA) and back propagation neural network (BPNN) model are used to obtain the diagnosis results. The diagnosis rates for healthy persons and echinococcosis persons are 93.333 3% and 90.909 1%, respectively, so the average final diagnosis rate is 92.121 2%. The results demonstrate that the Raman spectroscopy analysis of blood serum combined with PCA-BPNN has considerable potential for the non-invasive and rapid detection of echinococcosis.

By the electrochemical anodization method, we achieve the single-layer macroporous silicon on the N-type silicon, and prepare gold nanoparticles with sodium citrate reduction method. Through injecting the gold nanoparticles into the porous silicon by immersion, the fluorescence quenching mechanism of porous silicon influenced by gold nanoparticles is analyzed. Then the macroporous silicon deposited with gold nanoparticles is utilized to enhance the fluorescence of rhodamine 6G (R6G). It is found that when the macroporous silicon is deposited with gold nanoparticles for 6 h, the maximum fluorescence enhancement of R6G (about ten times) can be realized. The N-type porous silicon deposited with gold nanoparticles can be an excellent substrate for fluorescence detection.

Fluorescent porous silicon was prepared as a stable biosensor chip substrate. The aminopropyltriethoxysilane (APTES) molecules are attached in the pores of the porous silicon with a crosslink method, and when the molecules are added into the chip, the fluorescence intensity is reduced according to the concentration of the APTES. Controlled experiments are also presented with the small molecule that cannot be covalently coupled, and the results show that this kind of sensor chip has better specificity. Compared with other conventional methods, this method is simple, quick and label- free.

This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition (EEMD) and the variable step size least mean square (VS-LMS) adaptive filter. The noise of the high frequency part of spectrum will be removed through EEMD, and then the VS-LMS algorithm is utilized for overall de-noising. The EEMD combined with VS-LMS algorithm can not only preserve the detail and envelope of the effective signal, but also improve the system stability. When the method is used on pure R6G, the signal-to-noise ratio (SNR) of Raman spectrum is lower than 10 dB. The de-noising superiority of the proposed method in Raman spectrum can be verified by three evaluation standards of SNR, root mean square error (RMSE) and the correlation coefficient ρ.

The silver (Ag) nanowire arrays with regular and uniform size were successfully fabricated inside the nanochannels of anodic aluminum oxide (AAO) template by a simple paired cell method. X-ray diffraction (XRD) and scanning electron microscopy (SEM) results indicate that the as-synthesized samples are composed of face-centered cubic structure, and the average diameter is about 60–70 nm. Transmission electron microscopy (TEM) and the corresponding fast Fourier transformation (FFT) results show that Ag nanowires have a preferred single-crystal structure. Ultraviolet- visible (UV-vis) spectrum of Ag nanowire arrays exhibits UV emission band at 383 nm which can be attributed to the transverse dipole resonance of Ag nanowire arrays. A good surface-enhanced Raman scattering (SERS) spectrum is observed by excitation with a 514.5 nm laser, and the intensity of the SERS peak is about 23 times higher than that of the normal Raman peak measured from an empty AAO template. The high enhancement factor suggests that this method can be used to fabricate SERS sensor with high efficiency.

Washboard belt-like zinc selenide (ZnSe) nanostructures are successfully prepared by a simple chemical vapor deposition (CVD) technology without catalyst. The phase compositions, morphologies and optical properties of the nanostructures are investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM) and photoluminescence (PL) spectroscop, respectively. A vapor-liquid mechanism is proposed for the formation of ZnSe belt-like structures. Strong PL from the ZnSe nanostructure can be tuned from 462 nm to 440 nm with temperature varying from 1000 °C to 1200 °C, and it is demonstrated that the washboard belt-like ZnSe nanostructures have potential applications in optical and sensory nanotechnology. This method is expected to be applied to the synthesis of other II-VI groups or other group’s semiconducting materials.

The enhanced sensitivity of a guided mode biosensor is analyzed by employing double-layered porous silicon grating structures. The grating-coupled waveguide structure consists of two porous silicon grating layers with different refractive indices. Simulations are carried out by changing the refractive index, which is due to the binding of biological molecules on the porous silicon pore can increase the refractive index of porous silicon. The numerical results show that this novel guided mode biosensor with a double-layered grating can provide not only a very high sensitivity but also a better reflectivity characteristic.