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Human olfactory sensors have a large variety of receptor cells that generate signature responses to various gaseous molecules. Ideally, artificial olfactory sensors should have arrays of diverse sensors. However, it is challenging to monolithically integrate large-scale arrays of different high-performance gas sensors. Here we report biomimetic olfactory chips that integrate nanotube sensor arrays on nanoporous substrates with up to 10,000 individually addressable sensors per chip. The range of sensors is achieved using an engineered material composition gradient. Supported by artificial intelligence, the chips offer a high sensitivity to various gases with excellent distinguishability for mixed gases and 24 distinct odours. We also show that the olfactory chips can be combined with vision sensors on a robot dog to create a system that can identify an object in a blind box. © 2024, The Author(s), under exclusive licence to Springer Nature Limited.

Semiconductor chemiresistive gas sensors play critical roles in a smart and sustainable city where a safe and healthy environment is the foundation. However, the poor limits of detection and selectivity are the two bottleneck issues limiting their broad applications. Herein, a unique sensor design with a 3D tin oxide (SnO2) nanotube array as the sensing layer and platinum (Pt) nanocluster decoration as the catalytic layer, is demonstrated. The Pt/SnO2 sensor significantly enhances the sensitivity and selectivity of NO2 detection by strengthening the adsorption energy and lowering the activation energy toward NO2. It not only leads to ultrahigh sensitivity to NO2 with a record limit of detection of 107 parts per trillion, but also enables selective NO2 sensing while suppressing the responses to interfering gases. Furthermore, a wireless sensor system integrated with sensors, a microcontroller, and a Bluetooth unit is developed for the practical indoor and on-road NO2 detection applications. The rational design of the sensors and their successful demonstration pave the way for future real-time gas monitoring in smart home and smart city applications.

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city. ©

With the growing demand for high-speed data transmission, multiple-input multiple-output (MIMO) technology has been widely used in 5G wireless communication systems. Practically, analog-to-digital converters (ADCs) are employed to reduce the high power consumption problem caused by using a large number of antennas. However, one significant bottleneck restricting the performance of such MIMO systems is the infeasibility to jointly design the transceiver and the ADCs. To address this problem, this paper proposes an autoencoder solution driven by deep learning methods, for MIMO Rayleigh fading channels. Specifically, by viewing the entire MIMO system as an auto encoder, the encoding-decoding pair and the ADCs can be trained simultaneously and optimized in an end-to-end manner, which can also be easily adapted for different information transmission rates. Moreover, in order to enable the gradient passing for ADCs, a smooth function (i.e., arctan(.)) is used to finely approximate the quantization layers of original ADCs. Finally, simulation results reveal the improved performance on both accuracy and stability of the proposed autoencoder based framework in MIMO systems. © 2021 IEEE.

The fast development of technological platforms including Internet of Things (IoT) and the 5th generation (5G) mobile network paves the way to establish intelligent gas sensor networks for monitoring the chemical safety and air quality of ambient environment. However, this application scenario requires a very challenging combination of characteristics of gas sensors including small size for highly-integrated system design, ultra-low power, high sensitivity, high repeatability, and low cost for massive deployment and long-term operation of intelligent gas sensor network in both indoor and outdoor environments. Herein, a wireless self-powered high-performance nanostructure-based gas sensor network is demonstrated. The integrated solar cell combined with ultra-low power gas sensor array enables deployment flexibility and self-powered functionality of the sensor node because of small size of gas sensor array and micro watt level power consumption, achieving ‘permanent operation’ under dim light condition of the indoor environment. Powered by artificial intelligence and gas leakage localization algorithm, the sensor network has successfully achieved real-time indoor air quality monitoring, and leakage localization of flammable gas. With the merits of high sensitivity, desirable selectivity, and self-powered functionality, the as-developed gas sensor network will pose significant impacts on the existing markets of industrial gas leakage detection and indoor air quality monitoring, as well as the emerging markets of smart homes, and smart building applications.

Mobile robots behaving as humans should possess multifunctional flexible sensing systems including vision, hearing, touch, smell, and taste. A gas sensor array (GSA), also known as electronic nose, is a possible solution for a robotic olfactory system that can detect and discriminate a wide variety of gas molecules. Artificial intelligence (AI) applied to an electronic nose involves a diverse set of machine learning algorithms which can generate a smell print by analyzing the signal pattern from the GSA. A combination of GSA and AI algorithms can empower intelligent robots with great capabilities in many areas such as environmental monitoring, gas leakage detection, food and beverage production and storage, and especially disease diagnosis through detection of different types and concentrations of target gases with the advantages of portability, low-power-consumption and ease-of-operation. It is exciting to envisage robots equipped with a "nose" acting as family doctor who will guard every family member's health and keep their home safe. In this review, we give a summary of the state-of the-art research progress in the fabrication techniques for GSAs and typical algorithms employed in artificial olfactory systems, exploring their potential applications in disease diagnosis, environmental monitoring, and explosive detection. We also discuss the key limitations of gas sensor units and their possible solutions. Finally, we present the outlook of GSAs over the horizon of smart homes and cities. © 2019 Chinese Institute of Electronics.