Artificial olfaction, i.e., e‐nose, plays a critical function in robotics by mimicking the human olfactory organ that can recognize different smells that correlate with a range of fields, including environment monitoring, disease diagnosis, public security affairs, agricultural production, food industry, etc. The advances in the artificial olfaction (electronic nose) technology and its applications are concisely reviewed herein. Three main elements are investigated and presented, with an emphasis on the emerging sensors and algorithm of the artificial neural network in the relevant fields. The first element is the diverse applications of e‐nose in medical care, food industry, environment monitoring, public security affairs, and agricultural production. The second element is the investigation of the sensors in e‐nose and representative and promising advances, which is the building block of e‐nose through mimicking the olfactory receptors. The third element is the introduction to the algorithm of the artificial neural network to serve in the recognition of the pattern of odors (i.e., their chemical profiles). Promises and challenges of the separately reviewed parts and the combined parts are presented and discussed. Ideas regarding further orientation and development of the e‐nose system are also considered.
HIGHLIGHTS• This review gives a thinking based on the generic mechanisms rather than simply dividing them as different types of combination of materials, which is unique and valuable for understanding and developing the novel hybrid materials in the future.• The hybrid materials, their sensing mechanism, and their applications are systematically reviewed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are also discussed.ABSTRACT Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed.Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.
Deep learning achieves unprecedented success involves many fields, whereas the high requirement of memory and time efficiency tolerance have been the intractable challenges for a long time. On the other hand, quantum computing shows its superiorities in some computation problems owing to its intrinsic properties of superposition and entanglement, which may provide a new path to settle these issues. In this paper, a quantum deep convolutional neural network (QDCNN) model based on the quantum parameterized circuit for image recognition is investigated. In analogy to the classical deep convolutional neural network (DCNN), the architecture that a sequence of quantum convolutional layers followed by a quantum classified layer is illustrated. Inspired by the variational quantum algorithms, a quantum-classical hybrid training scheme is demonstrated for the parameter updating in the QDCNN. The network complexity analysis indicates the proposed model provides the exponential acceleration comparing with the classical counterpart. Furthermore, the MNIST and GTSRB datasets are employed to numerical simulation and the quantitative experimental results verify the feasibility and validity.
Achieving highly accurate responses to external stimuli during human motion is a considerable challenge for wearable devices. The present study leverages the intrinsically high surface‐to‐volume ratio as well as the mechanical robustness of nanostructures for obtaining highly‐sensitive detection of motion. To do so, highly‐aligned nanowires covering a large area were prepared by capillarity‐based mechanism. The nanowires exhibit a strain sensor with excellent gauge factor (≈35.8), capable of high responses to various subtle external stimuli (≤200 µm deformation). The wearable strain sensor exhibits also a rapid response rate (≈230 ms), mechanical stability (1000 cycles) and reproducibility, low hysteresis (<8.1%), and low power consumption (<35 µW). Moreover, it achieves a gauge factor almost five times that of microwire‐based sensors. The nanowire‐based strain sensor can be used to monitor and discriminate subtle movements of fingers, wrist, and throat swallowing accurately, enabling such movements to be integrated further into a miniaturized analyzer to create a wearable motion monitoring system for mobile healthcare.
Based on asymmetric contact, we present a contact charge model of high-energy trapped holes to predict the contact charging and explain the net charge transfer between identical silica glass surfaces in a single normal collision. Furthermore, the contact charging measurements are investigated on normal collisions of glassy particle-glassy particle-steel plane and glassy particle-glassy plane. The predicted results agree well with our experiments qualitatively and quantitatively: the impacting velocity and the particle size are two important factors affecting the magnitude of the contact charging.
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