Modeling of phononic crystal cavity for sensing different biodiesel fuels with high sensitivity

The biodiesel production process requires accurate information about its physical properties for monitoring the production process. This work introduced a one-dimensional ternary phononic crystal (1D-TPnC) sensor capable of discriminating and sensing between the acoustic properties of different biodiesel fuels in the ultrasonic range. The sensor structure combines a liquid-filled defect layer into a perfect TPnC. The liquid-filled defect layer restores some of the incident acoustic energy and excites a transmitted resonant dip inside the reflection spectrum. The reflection spectrum of the layered TPnC structure is calculated based on the transfer matrix method (TMM). The simulations showed that the proposed sensor has the feature of being ultra-highly sensitive and selective between the Extra-virgin olive, Tesco sunflower, Methyl Oleate, and Soybean biofuels. The numerical results revealed that Soybean oil has the highest sensitivity (76589 m(-1)) and Q-factor (267) compared with the other three oils. For Methyl Oleate, the sensitivity and quality factor of the sensor reached the values of about 12680 m 1 and 204, respectively. With increasing the temperature from 30 o(C) to 50 o(C), the sensitivity increased from 33853Hz/ degrees C to 36096 Hz/ degrees C for Soybean oil while it decreased from 4064 Hz/ degrees C to 37243 Hz/ degrees C for Methyl Oleate oil. This is because the Soybean oil has a higher speed of sound than Methyl Oleate. The proposed sensor has higher performance than many previously reported PnC sensors. Also, it can be used to determine different types of biodiesel fuels.

Automated summary

This study introduced a one-dimensional ternary phononic crystal sensor for discriminating and sensing the acoustic properties of different biodiesel fuels in the ultrasonic range. By combining a liquid-filled defect layer, the sensor exhibited ultra-high sensitivity and selectivity towards Extra-virgin olive, Tesco sunflower, Methyl Oleate, and Soybean biofuels. Numerical results showed that Soybean oil had the highest sensitivity and quality factor compared to the other oils, while Methyl Oleate showed sensitivity and quality factor values that varied with temperature changes. The proposed sensor outperformed many previously reported sensors and could be utilized for differentiating biodiesel fuels.