A novel turn-on fluorescence assay was developed for the rapid detection of glutathione (GSH) based on the inner-filter effect (IFE) and redox reaction. Molybdenum disulfide quantum dots (MoS2 QDs), which have stable fluorescent properties, were synthesized with hydrothermal method. Manganese dioxide nanosheets (MnO2 NSs) were prepared by exfoliating the bulk δ-MnO2 material in bovine serum albumin (BSA) aqueous solution. The morphology structures of the prepared nanoparticles were characterized by transmission electron microscope (TEM). Studies have shown that the fluorescence of MoS2 QDs could be quenched in the presence of MnO2 NSs as a result of the IFE, and is recovered after the addition of GSH to dissolve the MnO2 NSs. The fluorescence intensity showed a good linear relationship with the GSH concentration in the range 20–2500 μM, the limit of detection was 1.0 μM. The detection method was applied to the analysis of GSH in human serum samples. This simple, rapid, and cost-effective method has great potential in analyzing GSH and in disease diagnosis.

It is a daunting challenge to measure the concentration of each component in natural gas, because different components in mixed gas have cross-sensitivity for a single sensor. We have developed a mixed gas identification device based on a neural network algorithm, which can be used for the online detection of natural gas. The neural network technology is used to eliminate the cross-sensitivity of mixed gases to each sensor, in order to accurately recognize the concentrations of methane, ethane and propane, respectively. The neural network algorithm is implemented by a Field-Programmable Gate Array (FPGA) in the device, which has the advantages of small size and fast response. FPGAs take advantage of parallel computing and greatly speed up the computational process of neural networks. Within the range of 0−100% of methane, the test error for methane and heavy alkanes such as ethane and propane is less than 0.5%, and the response speed is several seconds.