Recently virtual sensor arrays (VSAs)
have been developed to improve
the selectivity of volatile organic compound (VOC) sensors. However,
most reported VSAs rely on detecting single property change of the
sensing material after their exposure to VOCs, thus resulting in a
loss of much valuable information. In this work, we propose a VSA
with the high dimensionality of outputs based on a quartz crystal
microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical
and electrical properties of the MXene film are utilized in the detection
of the VOCs. We take the changes of parameters of the Butterworth–van
Dyke model for the QCM-based sensor operated at multiple harmonics
as the responses of the VSA to various VOCs. The dimensionality of
the VSA’s responses has been expanded to four independent outputs,
and the responses to the VOCs have shown good linearity in multidimensional
space. The response and recovery times are 16 and 54 s, respectively.
Based on machine learning algorithms, the proposed VSA accurately
identifies different VOCs and mixtures, as well as quantifies the
targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover,
we demonstrate the capacity of the VSA to identify “patients
with diabetic ketosis” from volunteers with an accuracy of
95%, based on the detection of their exhaled breath. The QCM-based
VSA shows great potential for detecting VOC biomarkers in human breath
for disease diagnosis.
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.
Piezoelectric Laterally Vibrating Resonators (LVRs) have attracted significant attention as a potential technology for next-generation wafer-level multi-band filters. Piezoelectric bilayer structures such as Thin-film Piezoelectric-on-Silicon (TPoS) LVRs which aim to increase the quality factor (Q) or aluminum nitride and silicon dioxide (AlN/SiO2) composite membrane for thermal compensation have been proposed. However, limited studies have investigated the detailed behaviors of the electromechanical coupling factor (K2) of these piezoelectric bilayer LVRs. Herein, AlN/Si bilayer LVRs are selected as an example, we observed notable degenerative valleys in K2 at specific normalized thicknesses using two-dimensional finite element analysis (FEA), which has not been reported in the previous studies of bilayer LVRs. Moreover, the bilayer LVRs should be designed away from the valleys to minimize the reduction in K2. Modal-transition-induced mismatch between electric and strain fields of AlN/Si bilayer LVRs are investigated to interpret the valleys from energy considerations. Furthermore, the impact of various factors, including electrode configurations, AlN/Si thickness ratios, the Number of Interdigitated Electrode (IDT) Fingers (NFs), and IDT Duty Factors (DFs), on the observed valleys and K2 are analyzed. These results can provide guidance for the designs of piezoelectric LVRs with bilayer structure, especially for LVRs with a moderate K2 and low thickness ratio.
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