A Broad-Range Disposable Electrochemical Biosensor Based on Screen-Printed Carbon Electrodes for Detection of Human Noroviruses.

Human noroviruses (HuNoVs) are the major non-bacterial pathogens causing acute gastroenteritis in people of all ages worldwide. No stable culture system in vitro is available for routing the detection of multiple strains of HuNoVs. A simple and rapid method for detection of HuNoVs is of great significance for preventing and controlling this pathogen. In this work, an electrochemical biosensor for sensitive and fast detection of HuNoVs was constructed based on a screen-printed carbon electrode (SPCE). Gold nanoparticles and protein-A were applied on the SPCE surface for enhancement of the electrical signals and the linkage of antibodies with a fixed orientation, respectively. A monoclonal antibody (MAb) against the S domain protein of the viral capsid (VP1) was further immobilized on the SPCE to bind HuNoVs specifically. The binding of VP1 to the coated MAbs resulted in the reduction of conductivity (current) measured by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The reduction in the current was correlated to the concentration of VP1/HuNoVs. The detection limitation of Genogroup I.1 (GI.1) VP1 and Genogroup II.4 (GII.4) VP1 was 0.37 ng/ml (≈1.93×107 HuNoVs/mL) and 0.22 ng/ml (≈1.15×107 HuNoVs/mL), respectively. The detection limitation of both GI and GII HuNoVs in clinical fecal samples was 104 genomic copies/mL. The results could be obtained in 1 h. We demonstrated that this disposable electrochemical biosensor was a good candidate for rapid detection of different genogroup and genotype HuNoVs.

Advances and Future Perspective on Detection Technology of Human Norovirus.

Human norovirus (HuNoV) is a food-borne pathogen that causes acute gastroenteritis in people of all ages worldwide. However, no approved vaccines and antiviral drugs are available at present. Therefore, the development of accurate and rapid detection technologies is important in controlling the outbreak of HuNoVs. This paper reviewed the research progress on HuNoV detection, including immunological methods, molecular detection and biosensor technology. Immunological methods and molecular detection technologies are still widely used for HuNoV detection. Furthermore, biosensors will become an emerging developmental direction for the rapid detection of HuNoVs because of their high sensitivity, low cost, easy operation and suitability for onsite detection.

Theoretical studies on thermal degradation reaction mechanism of model compound of bisphenol A polycarbonate.

Density functional theory methods (DFT) M062X have been used to investigate the thermal degradation processes of model compound of bisphenol A polycarbonate (MPC) and to identify the optimal reaction paths in the thermal decomposition of bisphenol A polycarbonate (PC). The bond dissociation energies of main bonds in MPC were calculated, and it is found that the weakest bond in MPC is the single bond between the methylic carbon and carbon atom and the second weakest bond in MPC is the single bond between oxygen atom and the carbonyl carbon. On the basis of computational results of kinetic parameters, a mechanism is proposed where the hydrolysis (or alcoholysis) reaction is the main degradation pathways for the formation of the evolved products, and the homolytic cleavage and rearrangement reactions are the competitive reaction pathways in the thermal degradation of PC. The proposed mechanism is consistent with experimental observations of CO2, bisphenol A and 1,1-bis(4-hydroxyphenyl)-ethane as the main degradation products, together with a small amount of CO, alkyl phenol and diphenyl carbonate.

[Characteristics of thermal infrared hyperspectra and prediction of sand content of sandy soil].

To explore the potential of thermal infrared hyperspecra for retrieving sand content in soil, the sandy soil was measured using a 102F Fourier Transform Infrared Spectroradiometer (FTIR), and the characteristics of sandy soil's emissivity spectra were discussed based on correlation analysis and principal component analysis. Moreover, the sand contents were predicted using two modeling methods: Partial least squares regression (PLSR) and principal component regression (PCR). The results show that the Reststrahlen feature (RF) of SiO2 is obvious in the emissivity spectra of sandy soil with two large asymmetrical absorption troughs near 8.13 and 9.17 microm and two small troughs in the region of 12-13 microm. Soil emissivity becomes lower when sand content increases, this trend is more evident especially in the regions of 8-9.5 microm and 9.5-10.4 microm of which correlation coefficients are above 0.65 and 0.5 respectively, and these two regions can account for 84.07% of total emissivity variance. Predictive precision varies significantly when sand content is predicted by different modeling methods or spectral variables. The PLSR model can achieve the highest predictive precision by using first-order derivative spectra, and it's RMSE of modeling and prediction is 0.45 and 0.53 respectively, and the R2, 0.9907 and 0.9836, which means that the thermal hyperspectra has promising potential for retrieving sand content in soil.