MoS2-Based Flexible LC Sensor With an FCNN Algorithm for Simultaneous Temperature and Humidity Detection

The development of a flexible passive inductance-capacitance (LC) sensor with simultaneous multiple parameters sensing capability is highly desired in practical applications due to the reduced volume, weight, and cost. Different from the integration of multiple LC sensing loops for responding to different stimuli, in this study, we propose a molybdenum disulfide (MoS2)-based flexible LC sensor with a fully connected neural network (FCNN) algorithm for simultaneous temperature and humidity detection. The LC sensor is prepared using a cost-effective and easy-to-operate process including shadow mask printing, chemical etching, and MoS2 drop-coating. The MoS2 is employed as the active material to enhance the temperature and humidity sensing ability of the device, with high sensitivities up to −107.2 kHz/%RH and 142.92 kHz/°C, respectively. Moreover, the FCNN algorithm is used for decoupling the sensing data to simultaneously measure humidity and temperature. Temperature and humidity values predicted by the FCNN are close to the experimental values, with errors less than 10%.