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This paper investigates the effect of temperature on the sensitivity of a thin-film Polyvinyl alcohol (PVA) based SAW humidity sensor. A PVA coated 433.92 MHz SAW resonator-based humidity sensor was fabricated and tested with different levels of humidity (0.5 to 95% RH) at different operating temperatures (10°C to 70°C). The sensor response was recorded through in-house developed data acquisition software and it was observed that PVA thin film coated SAW sensor shows the maximum sensitivity for trace level moisture detection at a lower temperature (≤10 °C). The sensor sensitivity has been recorded >400Hz/% RH for trace level detection (0.5–30% RH). It has been observed that sensor sensitivity deteriorates when temperature increased to 40 °C from 10 °C. The sensor has a fast response (~1s) and recovery time (<3s) for trace level humidity detection. The proposed sensor can be used in many applications, including fabrication of electronic devices, IC fabrication, pharmaceutical, textile industries, food processing, semiconductor device fabrication, and packaging.

Nerve agents are often used at the military warfront, where diesel is a very common interferant. In the present work, a group of surface acoustic wave (SAW) sensors, called E-Nose with dissimilar sensing layers is developed for the recognition of the mixture of diesel and dimethyl methylphosphonate (DMMP) vapors. The exposure of DMMP and diesel vapors is kept at ppb and ppm levels respectively. Varied response patterns of DMMP and diesel vapors were obtained by SAW E-nose. Principal component analysis (PCA) has been used to extract features from the response curves of SAW sensors. Artificial Neural Network pattern recognition has been implemented to identify the precise detection of DMMP vapors in the binary mixture of DMMP and diesel. The effect of pre-processing (using PCA) the raw data before feeding it to artificial neural network is also studied.

Nerve agents are often used at the military warfront, where diesel is a very common interferant. In the present work, a group of surface acoustic wave (SAW) sensors, called E-Nose with dissimilar sensing layers is developed for the recognition of the mixture of diesel and dimethyl methylphosphonate (DMMP) vapors. The exposure of DMMP and diesel vapors is kept at ppb and ppm levels respectively. Varied response patterns of DMMP and diesel vapors were obtained by SAW E-nose. Principal component analysis (PCA) has been used to extract features from the response curves of SAW sensors. Artificial Neural Network pattern recognition has been implemented to identify the precise detection of DMMP vapors in the binary mixture of DMMP and diesel. The effect of pre-processing (using PCA) the raw data before feeding it to artificial neural network is also studied.