Aobei Chen

Extreme environments such as low pressure, high temperature, and intense radiation pose severe challenges for humidity sensors, causing conventional hygroscopic materials to exhibit sluggish responses, drift, and instability. In response, recent research has adopted multi-level strategies involving material modification, structural engineering, and packaging optimization to enhance the adaptability of humidity-sensitive materials in extreme environments. This review examines humidity sensing from an environmental perspective, integrating sensing mechanisms, material classifications, and application scenarios. The performance, advantages, and limitations of six major categories of humidity-sensitive materials, including carbon-based, metal oxides, conductive and insulating polymers, two-dimensional (2D) materials, and composites, are systematically summarized under extreme conditions. Finally, emerging development trends are discussed, highlighting a shift from material-driven to system-driven approaches. Future progress will rely on multidisciplinary integration, including interface engineering, multiscale structural design, and intelligent algorithms, to achieve higher accuracy, stability, and durability in extreme-environment humidity sensing.

Extreme environments such as low pressure, high temperature, and intense radiation pose severe challenges for humidity sensors, causing conventional hygroscopic materials to exhibit sluggish responses, drift, and instability. In response, recent research has adopted multi-level strategies involving material modification, structural engineering, and packaging optimization to enhance the adaptability of humidity-sensitive materials in extreme environments. This review examines humidity sensing from an environmental perspective, integrating sensing mechanisms, material classifications, and application scenarios. The performance, advantages, and limitations of six major categories of humidity-sensitive materials, including carbon-based, metal oxides, conductive and insulating polymers, two-dimensional (2D) materials, and composites, are systematically summarized under extreme conditions. Finally, emerging development trends are discussed, highlighting a shift from material-driven to system-driven approaches. Future progress will rely on multidisciplinary integration, including interface engineering, multiscale structural design, and intelligent algorithms, to achieve higher accuracy, stability, and durability in extreme-environment humidity sensing.

Rydberg atom-based microwave measurement technology has gained attention for its self-calibration, high sensitivity, and wide frequency response. To enhance sensitivity, resonators are typically employed for localized electric field enhancement. With the introduction of AC/DC Stark tuning and laser scanning techniques, resonator designs have been developed to support extended frequency coverage. However, these require sequential scanning or switching to cover the target range, limiting their applicability in passband-wide, multi-frequency detection. To address this issue, this paper proposes a cascaded cavity resonator (CCR) that enables continuous field enhancement within the target S-band range. A theoretical model was established and the key dimensions were optimized via COMSOL-based simulation of the vapor cell's spatial field distribution. Subsequently, validation through Autler-Townes (AT) effect and heterodyne technology confirms that the proposed structure enhances sensitivity by $ \geq$27.2 dB within the 2.781-3.008 GHz bandwidth. This work supports systematic exploration of Rydberg-atom response across a broader spectral range and enables multi-frequency detection without stepwise tuning.

Surface Acoustic Wave (SAW) pressure sensors are widely applied for their strong environmental adaptability, ease of wireless integration, and low cost. With the extension of atmospheric observation into near-space environments, there is a growing demand for cross-scale pressure measurements ranging from Pa to hundreds of kPa. However, conventional SAW pressure sensors based on a single sensing mechanism face limitations in meeting this demand. Integrating diaphragm-type and Pirani-type sensing mechanisms offers a promising approach, yet balancing their sensitivities across pressure ranges remains challenging due to conflicting temperature coefficient of frequency (TCF) requirements. To address this issue, this study proposes a SAW pressure sensor based on a composite Diaphragm- Pirani sensing mechanism, incorporating a differential decoupling structure and suspended packaging to enable high sensitivity across the pressure scales. Based on the established composite sensing model, the sensor was designed, fabricated, and evaluated using a pressure–temperature integrated test system. Experimental results demonstrate that the sensor achieves sensitivities of 21.03 ppm/Pa (10–100 Pa), 1.01 ppm/Pa (100 Pa–1 kPa), and 3.17 ppm/kPa (1–200 kPa), effectively addressing the limitations of existing technologies in cross-scale pressure sensing. Furthermore, an adaptive range-switching strategy was implemented to enable smooth transition between different measurement modes.

This paper aims to enhance the efficiency of sensor structural optimization design and achieve rapid performance evaluation and automatic layout drawing. To this end, we propose a parameterized model of a resonant differential MEMS pressure sensor (PRDMP). It can complete sensitivity calculations within 1 ms, far faster than traditional finite element analysis (FEA) methods. Additionally, compared to FEA results, its accuracy exceeds 90 % . Furthermore, we reconstructed the uncertainty analysis part of PRDMP based on an error model. Compared to the Monte Carlo method used in existing studies, our method is faster and yields more stable results. Benefiting from the speed and accuracy of PRDMP, we achieved, for the first time, the multi-parameter collaborative automatic optimization of this sensor. Optimization results show that sensitivity increased by 36.2 % while uncertainty decreased by 15.8 % . Finally, we developed a sensor performance analysis and automatic layout drawing tool based on PRDMP, further enhancing design efficiency.

Humidity sensors are widely used in various fields of life. In meteorological detection, the sensor must have high sensitivity and fast dynamic response time due to extreme environmental interference. However, the sensitive mechanism of the humidity sensor determines that the dynamic response time will inevitably be increased while improving the sensitivity, which undoubtedly creates difficulties for sensor design. This article takes the interdigitated capacitive humidity sensor as the research object and proposes an optimal design scheme for the sensor that considers high dynamic response time and sensitivity. By constructing the sensor’s theoretical mathematical model, the influence of each structure is analyzed. The theoretical model has been verified by finite element simulation to have an accuracy higher than 95%. The article constructs the sensor optimization objective equation based on this model. Through analysis, within the range of structural parameters set in the article, to improve the sensitivity and reduce the dynamic response time of the sensor, the width and spacing of the interdigital electrodes should have a minimum value of 3 μm and a maximum value of 14 μm, respectively. The thickness of the electrode layer and the moisture-sensitive layer should be flexibly adjusted according to the application to ensure the lowest value of the optimization objective function. To further improve the sensor’s performance, the article optimizes the electrode structure and heating strategy of the sensor heating layer, which not only enhances the uniformity of heat transfer but also increases the optimal heat transfer area by 6% compared with the traditional scheme.

To meet the needs of a large number of high-altitude meteorological detections, we need to perform fast, high-precision, and high-reliability calibrations of the sensors in the atmospheric detection system (ADS). However, using the traditional method to calibrate the sensor with high precision often takes a lot of time and increases the cost of workforce and material resources. Therefore, a method for realizing fast sensor calibration under the current system hardware conditions is required. A physical field model of Tube–Air–ADS is proposed for the first time, and the transfer function is obtained by combining the system identification, which provides the possibility for dynamic analysis of the calibration system. A Multi-Criteria Adaptive (MCA) PID controller design method is proposed, which provides a new idea for the parameter design of the controller. It controls the amplitude and switching frequency of the controller’s output signal, ensuring the safe and stable operation of the calibration system. Combined with the hardware parameters of the system, we propose the Variable Precision Steady-State Discrimination (VPSSD) method, which can further shorten the calibration time. Comparing and analyzing the current simulation results under Matlab/Simulink, the proposed MCA method, compared with other PID controller design methods, ensures the stable operation of the calibration system. At the same time, compared with the original system, the calibration time is shortened to 47.7%. Combined with the VPSSD method, the calibration time further shortens to 38.7 s.

Humidity sensors have been widely used in various fields of life, among which sensitivity is one of the most important indicators for its evaluation. Quantitatively analyzing the effect of structural errors on sensitivity is a big challenge in sensor design. To address this problem, this paper proposes a structural uncertainty analysis method. By establishing the mathematical model and uncertainty propagation model, the influence of the interdigital capacitive humidity sensor’s structural uncertainty on the sensitivity is quantitatively analyzed. The accuracy and superiority of the proposed method are also verified by finite element simulation.