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      Three‐Terminal Artificial Olfactory Sensors based on Emerging Materials: Mechanism and Application

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          Abstract

          Over the past several years, a variety of hardware‐based artificial sensory systems, including artificial skin, electronic noses, and artificial retinas, have attracted considerable research interest in advanced artificial intelligence systems. The integration of sensing and computing functions in single or multiple connected self‐adaptive field‐effect transistor (FET)‐structured sensory devices to implement artificial olfactory systems for in‐sensor computing has recently attracted increasing attention. In this review, the development status of FET‐based gas sensory devices is focused on. The mechanisms of sensory FET devices, gas‐recognition materials, strategies for improving sensing performance, and the integration of sensory devices into the artificial olfactory system are discussed. Finally, the further development of FET‐based sensory devices for artificial olfactory systems and their great potential for next‐generation intelligent sensory systems are discussed in broad fields such as environmental monitoring, health care, and military industries.

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          Mastering the game of Go with deep neural networks and tree search.

          David Silver, Aja Huang, Chris J Maddison … (2016)
          The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
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            Recent Advances in Ultrathin Two-Dimensional Nanomaterials.

            Chaoliang Tan, Xiehong Cao, Xue-Jun Wu … (2017)
            Since the discovery of mechanically exfoliated graphene in 2004, research on ultrathin two-dimensional (2D) nanomaterials has grown exponentially in the fields of condensed matter physics, material science, chemistry, and nanotechnology. Highlighting their compelling physical, chemical, electronic, and optical properties, as well as their various potential applications, in this Review, we summarize the state-of-art progress on the ultrathin 2D nanomaterials with a particular emphasis on their recent advances. First, we introduce the unique advances on ultrathin 2D nanomaterials, followed by the description of their composition and crystal structures. The assortments of their synthetic methods are then summarized, including insights on their advantages and limitations, alongside some recommendations on suitable characterization techniques. We also discuss in detail the utilization of these ultrathin 2D nanomaterials for wide ranges of potential applications among the electronics/optoelectronics, electrocatalysis, batteries, supercapacitors, solar cells, photocatalysis, and sensing platforms. Finally, the challenges and outlooks in this promising field are featured on the basis of its current development.
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              Detection of individual gas molecules adsorbed on graphene

              F Schedin, A. K. Geim, S. V. Morozov … (2007)
              The ultimate aim of any detection method is to achieve such a level of sensitivity that individual quanta of a measured entity can be resolved. In the case of chemical sensors, the quantum is one atom or molecule. Such resolution has so far been beyond the reach of any detection technique, including solid-state gas sensors hailed for their exceptional sensitivity. The fundamental reason limiting the resolution of such sensors is fluctuations due to thermal motion of charges and defects, which lead to intrinsic noise exceeding the sought-after signal from individual molecules, usually by many orders of magnitude. Here, we show that micrometre-size sensors made from graphene are capable of detecting individual events when a gas molecule attaches to or detaches from graphene's surface. The adsorbed molecules change the local carrier concentration in graphene one by one electron, which leads to step-like changes in resistance. The achieved sensitivity is due to the fact that graphene is an exceptionally low-noise material electronically, which makes it a promising candidate not only for chemical detectors but also for other applications where local probes sensitive to external charge, magnetic field or mechanical strain are required.
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                Author and article information

                Contributors
                Su‐Ting Han: (View ORCID Profile)
                Journal
                Title: Advanced Functional Materials
                Abbreviated Title: Adv Funct Materials
                Publisher: Wiley
                ISSN (Print): 1616-301X
                ISSN (Electronic): 1616-3028
                Publication date Created: March 2023
                Publication date (Electronic): January 15 2023
                Publication date (Print): March 2023
                Volume: 33
                Issue: 10
                Affiliations
                [1 ] College of Civil and Transportation Engineering and Institute for Advanced Study Shenzhen University Shenzhen 518060 P. R. China
                [2 ] College of Electronics and Information Engineering Shenzhen University Shenzhen 518060 P. R. China
                [3 ] Institute of Microscale Optoelectronics Shenzhen University Shenzhen 518060 P. R. China
                Article
                DOI: 10.1002/adfm.202209969
                SO-VID: efc432a3-1003-430a-a82b-56eaf089680a
                Copyright © © 2023
                License:

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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