In this paper, the static and dynamic characteristics of the MXene-based IDE capacitive humidity sensor are investigated through theoretical modeling and simulation. It is found that the capacitance increases according to the thickness of the sensing film within a certain range while stopping increases along with the growth of the thickness when the thickness is over a threshold. When the thickness is at a tiny level, a larger thickness does not lead to a significant increase in the response time due to the diffusion mechanism of water molecules. When the thickness increases to certain extent, there is an evident relationship between the response time and the change of thickness. For the humidity-sensitive film, under the same relative humidity conditions, the capacitance has a positive correlation with temperature, and the response time shows the opposite trend. Subsequent studies on the sensitive mechanisms of MXene materials explain these phenomena and demonstrate the accuracy of the model. This provides a more accurate method for sensor design. The properties of the MXene capacitive humidity sensor can be optimized by changing its structure and adjusting material parameters.

A novel humidity sensor based on graphene foam combined with MoS₂ was prepared in this paper. MoS₂/graphene foam (MGF) composite materials were synthesized by the atmospheric pressure CVD method, then transferred onto a glass substrate, and had electrodes prepared to complete sensor fabrication. Due to the high surface - to - volume ratio and network structure of sensing materials, humidity sensors exhibit shorter response and recovery times by allowing water molecules to be absorbed and released more quickly. Besides, this type of sensor also shows relatively linear sensitivity (0.04%/%RH between 10% and 90% RH) and good repeatability (the maximum offset within 0.41% at 60% RH), so that it can be used for respiratory monitoring. This provides a unique method for respiratory monitoring that can be applied in medical monitoring, with great potential for use. Moreover, to further enhance the dynamic performance of humidity sensors, a dynamic compensation method combined with system identification and particle swarm optimization (PSO) is proposed. By employing this approach, the identified dynamic mathematical model agrees well with the actual measurement results (95.67% in absorption and 91.09% in desorption). In the process of moisture absorption, the sensor component's operational bandwidth is enhanced from 0.06 Hz to 1.91 Hz, and the response time is shortened from 2.06s to 0.29s. During desorption, the sensor component's operational bandwidth is enhanced from 0.02 Hz to 1.75 Hz, and the recovery time is shortened from 3.74s to 0.48s. This dynamic compensation method offers a practical approach to enhance these sensors' performance.