High Resolution and Fast Response of Humidity Sensor Based on AlN Cantilever with Two Groups of Segmented Electrodes

Resonant cantilever based on piezoelectric materials is one of the most promising platforms for real-time humidity sensing. In this letter, we propose a humidity sensor based on an AlN piezoelectric microcantilever with a high-order resonant mode and a sensing layer of MoS2. The top electrode of cantilever is designed into two groups of segmented electrodes in order to achieve a high intensity of the resonance peak of the cantilever resonator operated at a high-order mode. Compared with the humidity sensor based on a standard cantilever with the same dimension, the sensitivity of the newly proposed humidity sensor is increased from 5.99 to 778 Hz/%RH when the humidity is about 80%RH. The resolution is increased from 0.21%RH to 0.025%RH because of the improvement of the ratio of sensitivity to noise, which cannot be achieved simply by increasing the frequency. The sensor shows a low hysteresis (5.8%) in a wide humidity sensing range from 10%RH to 90%RH. Moreover, the proposed humidity sensor has good short-term repeatability, fast response (0.6 s) and recovery (8 s) to humidity changes, indicating its great potential for fast-response detection

Virtual sensor array based on MXene for selective detections of VOCs

Two-dimensional transition metal carbides/nitrides, known as MXenes, have recently received significant attention for gas sensing applications. However, MXenes have strong adsorption to many types of volatile organic compounds (VOCs), and therefore gas sensors based on MXenes generally have low selectivity and poor performance in mixtures of VOCs due to cross-sensitivity issues. Herein, we developed a Ti3C2Tx-based virtual sensor array (VSA) which allows both highly accurate detection and identification of different VOCs, as well as concentration prediction of the target VOC in variable backgrounds. The VSA’s responses from the broadband impedance spectra create a unique fingerprint of each VOC without a need for changing temperatures. Based on the methodologies of principal component analysis and linear discrimination analysis, we demonstrate highly accurate identifications for different types of VOCs and mixtures using this MXene based VSA. Furthermore, we demonstrate an accuracy of 93.2% for the prediction of ethanol concentrations in the presence of different concentrations of water and methanol. The high level of identification and concentration prediction shows a great potential of MXene based VSA for detection of VOCs of interest in the presence of known and unknown interferences