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Detecting volatile organic compounds (VOCs) in human breath is critical for early diagnosis of diseases. Good selectivity of VOCs sensors is crucial for accurate analysis of VOCs biomarkers in human...

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.

Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS 2 film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm 2 ) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS 2 after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO 2 concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO 2 concentrations for various applications, revealing its great potential in human−machine interactions and health monitoring of plants and human beings.

Physiological mechano-acoustic signals play a pivotal role in medical diagnosis and fitness monitoring. Mechanical waves generated by natural physiological activities such as myocardial contraction, and vocal fold vibration, propagate through the tissues and fluids of the body and reveal characteristic signals of these events. Conventional methods such as stethoscope and electrocardiography (ECG) are not suitable for wearable mode and continuous monitoring. In this paper, we propose a wearable physiological sounds sensing device to monitor heart sound and detect speech and voice with high accuracy. The device consists of a MEMS (microelectromechanical systems) acoustic sensor and a low-noise amplification circuit, and both of them are packaged by silicone polymers with an air cavity to achieve conformal contact with human skin. The proposed device has advantages of light weight, sweatproof capability, resistant to noise and good stability. The wearable device has great potential in clinical diagnosis, healthcare, human-machine interaction and many other applications.