Machine Learning-Assisted Volatile Organic Compound Gas Classification Based on Polarized Mixed-Potential Gas Sensors.

The performance of electrochemical gas sensors depends on the reactions at the three-phase boundary. In this work, a mixed-potential gas sensor containing a counter electrode, a reference electrode, and a sensitive electrode was constructed. By applying a bias voltage to the counter electrode, the three-phase boundary can be polarized. The polarization state of the three-phase boundary determined the gas-sensitive performance. Taking 100 ppm ethanol vapor as an example, by regulating the polarization state of the three-phase boundary, the response value of the sensor can be adjusted from -170 to 40 mV, and the sensitivity can be controlled from -126.4 to 42.6 mV/decade. The working temperature of the sensor can be reduced after polarizing the three-phase boundary, lowering the power consumption from 1.14 to 0.625 W. The sensor also showed good stability and short response-recovery time (3 s). Based on this sensor, the Random Forest algorithm reached 99% accuracy in identifying the kind of VOC vapors. This accuracy was made possible by the ability to generate several signals concurrently. The above gas-sensitive performance improvements were due to the polarized three-phase boundary.

[Structure design and testing of drug micro-jetting multifunctional system].

For the researches relating to the biomedical fields such as preparation of drug micro-particulates and biomedical materials coating, according to the modular design concept and combing the piezoelectric micro-jetting technology with electromechanical engineering and automatic control technology, the drug micro-jetting multifunctional system was designed, which included the spraying support subsystem, X - Y motion platform, Z -axis subsystem and rapid installation subsystem. The drug micro-jetting multifunctional system was run and adjusted. The versatility, rationality and feasibility of this system were validated by the experiments of amoxicillin microcapsule preparation, titanium alloy drug-loaded coating preparation and balloon electrode coating preparation. It was shown that the system can be used as basic platform in multi-disciplinary cross technology research such as biomedical engineering, pharmaceutical engineering and so on.

Research of amoxicillin microcapsules preparation playing micro-jetting technology.

With polylactic-co-glycolic acid(PLGA) as shell material of microcapsule, amoxicillin as the model, poly(vinyl alcohol) and twain as surfactant, amoxicillin-PLGA microcapsules were manufactured using digital micro-jetting technology and a glass nozzle of 40µm diameter. The influences of the parameters of micro-jetting system on the mean grain size and size distribution of amoxicillin-PLGA microcapsules were studied with single factor analysis and orthogonal experiment method, namely, PLGA solution concentration, driving voltage, jetting frequency, stirrer speed, etc. The optimal result was obtained; the form representation of microcapsule was analyzed as well. The results show that, under certain conditions of experimental drug prescription, driving voltage was proportional to the particle size; jetting frequency and stirrer speed were inversely proportional. When the PLGA concentration for 3%, driving voltage for 80V, the jetting frequency for 10000Hz and the stirrer speed for 750rpm, the particles were in an ideal state with the mean grain size of 60.246µm, the encapsulation efficiency reached 62.39％ and 2.1％ for drug loading.