This work proposes and demonstrates a biaxial sensing inclinometer based on two FBGs surfacemounted on two separate thin cantilevers with a diminishable cross-axis sensitivity. The measured sensitivities for the inclination angles in the x-z plane and y-z plane are 34.87 and 33.49 pm/deg, respectively. To enhance the protection of the delicate FBGs and minimize axis-to-axis cross-sensitivity, the carbon-steel cantilevers are strategically arranged in a perpendicular configuration, resulting in an impressively low cross-sensitivity value of just 0.9275%. This alignment not only offers mechanical shielding but also ensures optimal performance and accuracy for the FBGs.
A novel technology for the simultaneous and independent measurement of dual parameters is proposed and experimented. By using a single fiber Bragg grating half-pasted by 1C-LV epoxy under different curing conditions, the sensor structure is designed such that the reflective single-peak spectrum splits into a twin-peak spectrum, which makes the FBG spectrum form a natural spectral peak splitting bias. A measurement limitation exists in the FBG sensor packaging at room temperature, which can be solved by the high-temperature cured packaging method. To verify the validity of the theory and methodology, the experimental system is used. In the range from –1000 to +1000 με and from 35 to 75°C, the Bragg wavelength change is relative linear to the strain and temperature. The temperature and strain variations can be independently and simultaneously measured using the split peak, and the deviations of the FBG sensor are ±1°C and ±5 με, respectively. This single FBG sensor can realize dual-parameter measurement, which is valuable for narrow-space health monitoring.
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.
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