Comparative performance analysis of mixed metal oxide sensors for dual-sensing leveraging machine learning.

This paper presents the synthesis of mixed metal oxide (BaTiO: ZnO) (B: Z) sensors with various molar ratios using a low-temperature hydrothermal method for dual sensing applications (gas and acceleration). The sensor developed with an equal molar ratio of 1B:1Z, showcases superior performance compared to unmixed and alternative mixed metal oxide sensors. This equilibrium in ratios optimally enhances synergistic effects between elements B and Z, resulting in improved sensing properties. Furthermore, it contributes to structural stability, enhancing performance in gas and acceleration sensing. A decreased band gap of 2.82 eV and a rapid turn-on voltage of 0.18 V were achieved. The acceleration performance of 1B:1Z sensor exhibits a maximum voltage of 2.62 V at a 10 Hz resonant frequency and an output voltage of 2.52 V at 1 g acceleration, achieving an improved sensitivity of 3.889 V g. In addition, the proposed gas shows a notable sensor response of ∼63.45% (CO) and 58.29% (CH) at 10 ppm with a quick response time of 1.19 s (CO) and 8.69 s (CH) and recovery time of 2.09 s (CO) and 8.69 s (CH). Challenges in selectivity are addressed using machine learning, employing various classification algorithms. Linear discriminant analysis achieves superior accuracy in differentiating between CO and CHreaching 96.6% for CO and 74.6% for CHat 10 ppm. Understanding these concentration-dependent trends can guide the optimal use of the sensors in different current applications.