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The development of portable volatile organic compound (VOC) sensors is essential for home healthcare and workplace safety because VOCs are environmental pollutants that may critically affect human health. Here, we report a compact and portable sensor platform based on a capacitive micromachined ultrasonic transducer (CMUT) array offering multiplex detection of various VOCs (toluene, acetone, ethanol, and methanol) using a single read-out system. Three CMUT resonant devices were functionalized with three different layers: (1) phenyl-selective peptide, (2) colloids of single-walled nanotubes and peptide, and (3) poly(styrene-co-allyl alcohol). As each device exhibited different sensitivities to the four VOCs, we performed principal component analysis to achieve selective detection of all four gases. For the simultaneous detection of VOCs using CMUT sensors, the changes in the resonant frequencies of three devices were monitored in real time, but using only a single oscillator through an electrically controlled relay to achieve compactness. In addition, by devising a wireless system, measurement results were transmitted to a smartphone to monitor the concentration of VOCs. We used multiple sensors to obtain a larger number of fingerprints for pattern recognition to enhance selectivity but interfaced these sensors with a single read-out circuit to minimize the footprint of the overall system. The compact CMUT-based sensor array based on a multiplex detection scheme is a promising sensor platform for portable VOC monitoring.

Chronic monitoring of bladder activity and urine volume is essential for patients suffering from urinary dysfunctions. However, due to the anatomy and dynamics of the bladder, chronic and precise monitoring of bladder activity remains a challenge. Here, we propose a new sensing mechanism that measures the bladder volume using a resistive ladder network with contact switches. Instead of measuring the impedance between the electrode continuously, the proposed sensor provides a digitized output (‘on’ or ‘off’) when the bladder volume reaches a certain threshold value. We present simple proof-of-concept sensors which compare the discrete-mode operation to the continuous-mode operation. In addition, by using multiple pairs of this contact-mode switch in a resistor ladder structure, we demonstrate monitoring of the bladder volume in four discrete steps using an idealized balloon and an ex vivo pig’s bladder. We implemented the resistive ladder network using a conductive polypyrrole/agarose hydrogel composite which exhibits a Young’s modulus comparable to that of the bladder wall. Compared to the continuous-mode operation, the proposed sensing mechanism is less susceptible to drift due to material degradation and environmental factors.

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Nowadays, the welding process is essential in various manufacturing industrial fields, such as aerospace, vehicle production, and shipbuilding. The welding defects caused in the process need to be monitored as they can cause serious accidents and losses. Traditional computer vision methods in an industrial application are inefficient when the detection targets have variations in shape, scale, and color because the detection performance depends on the hand-crafted features. To overcome this limitation, deep learning models, such as the convolutional neural network (CNN), are applied to industrial defect detection. These CNN-based models trained on static images, however, a low performance that cannot meet the industrial requirements. To deal with the challenge, bidirectional Convolutional Recurrent Reconstructive Network (bi-CRRN) is proposed for welding defect detection and localization based on welding video. Spatio-temporal data, especially the forward and backward sequences, are considered in our bi-CRRN to get high detection performance. Moreover, an automatic defect detection equipment is developed to weld a material and monitor the welding bead simultaneously. We demonstrate that the proposed bi-CRRN outperforms the other segmentation network models in welding defect detection.