The 0.05 mm-thick 304 stainless steel foil was annealed within the temperature range from 950℃-1100℃ for 10 minutes to obtain different microstructures. And micro-deep drawing experiments of stainless steel foils with different tissue structures were conducted to obtain relevant material forming properties influenced by dimensional effects. On this basis, the influence of the microstructure characteristics on the forming performance of 304 stainless steel foil and the quality of the cup formed by using micro-drawing was studied, and its mechanism was discussed. It can be seen from the results that the stainless steel foil annealed at 950℃ exhibits poor forming performance, and the wrinkle phenomenon of the deep-drawn cup is obvious. At the annealing temperature of 1050℃, the quality of the deep drawing cup is significantly improved. When the annealing temperature reaches 1100℃, with the increase of the annealing temperature, the crystal grains size increase sharply, and the coarse-grain effect causes the uneven plastic deformation effect to be obvious. Besides, the drawing quality is obviously deteriorated. The observation of the microstructure of the deep drawing cup shows that the forming effect of the drawing cup is poor due to the rolling defects and the coarse grain effect. The 304 stainless steel drawing cup annealed at 1050℃ enjoys the best forming effect.

In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO₂ and NO₂. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas.