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Due to the significant advantages of methane sensitivity and area-type leakage detection, tunable diode laser absorption spectroscopy (TDLAS) gas detection has been promoted as an effective method for microleakage monitoring in oil and gas stations. However, the output of the TDLAS detector is the integral concentration (IC). Based on the relevant research, the alarm threshold and risk are assessed via the gas concentration rather than the IC. How to evaluate the concentration of leaked gas clouds based on IC from TDLAS detectors is still a challenge. To address this problem, the characteristics of IC and the influence of the laser path, wind speed and leakage rate were studied via computational fluid dynamics (CFD). A neural network classification model (NNCM) was proposed to obtain the probability distribution of the maximal concentration along the laser path (Cmax). The results indicated that the IC is strongly correlated with the Cmax. Considering the accuracy and operability, the NNCM with input features of IC, wind speed and angle of laser path was selected. Field tests showed that the developed model achieved the concentration evaluation of leaked gas clouds. Additionally, the NNCM can also quantify the uncertainty of the results, which avoids misjudgments caused by deviations. [ABSTRACT FROM AUTHOR]

We developed a high-efficiency microfluidic chip for extracting exosomes from human plasma. We collected peripheral blood from normal human, designed and fabricated a microfluidic chip based on nanoporous membrane and agarose gel electrophoresis to isolate exosomes. The extracted exosomes were characterized by transmission electron microscopy, nanosight and Western blotting, the morphology, concentration and particle size of exosomes were identified and analyzed. Meanwhile, we used ultracentrifugation and microfluidic chip to isolate exosomes separately. The particle size and concentration of the exosomes extracted by two methods were compared and analyzed, and their respective extraction efficiency was discussed. Finally, the expression level of miRNA-21 in exosomes was analyzed by RT-PCR. The microfluidic chip isolated (in 1 hour) high-purity exosomes with size ranging from 30-200 nm directly from human plasma, allowing downstream exosomal miRNA analysis. By comparing with ultracentrifugation, the isolation yield of microfluidic chip was 3.80 times higher than ultracentrifugation when the volume of plasma sample less than 100 μL. The optimized parameters for exosome isolation by gel electrophoresis microfluidic chip were: voltage: 100 V; concentration of agarose gel: 1.0%; flow rate of injection pump: 0.1 mL/h. The gel electrophoresis microfluidic chips could rapidly and efficiently isolate the exosomes, showing great potential in the research of exosomes and cancer biomarkers.