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Liu M, Zhang Y, Wang J, Qin N, Yang H, Sun K, Hao J, Shu L, Liu J, Chen Q, Zhang P, Tao TH. A star-nose-like tactile-olfactory bionic sensing array for robust object recognition in non-visual environments. Nat Commun 2022; 13:79. [PMID: 35013205 PMCID: PMC8748716 DOI: 10.1038/s41467-021-27672-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 11/24/2021] [Indexed: 12/18/2022] Open
Abstract
Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.
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Affiliation(s)
- Mengwei Liu
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yujia Zhang
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jiachuang Wang
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Nan Qin
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
| | - Heng Yang
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ke Sun
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Hao
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China
| | - Lin Shu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China
| | - Jiarui Liu
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100049, China
| | - Qiang Chen
- Shanghai Fire Research Institute of MEM, Shanghai, 200003, China
| | - Pingping Zhang
- Suzhou Huiwen Nanotechnology Co., Ltd, Suzhou, 215004, China
| | - Tiger H Tao
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China.
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 200031, China.
- Institute of Brain-Intelligence Technology, Zhangjiang Laboratory, Shanghai, 200031, China.
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai, 200031, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China.
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Boroujerdi R, Abdelkader A, Paul R. State of the Art in Alcohol Sensing with 2D Materials. NANO-MICRO LETTERS 2020; 12:33. [PMID: 34138082 PMCID: PMC7770777 DOI: 10.1007/s40820-019-0363-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 12/05/2019] [Indexed: 05/17/2023]
Abstract
Since the discovery of graphene, the star among new materials, there has been a surge of attention focused on the monatomic and monomolecular sheets which can be obtained by exfoliation of layered compounds. Such materials are known as two-dimensional (2D) materials and offer enormous versatility and potential. The ultimate single atom, or molecule, thickness of the 2D materials sheets provides the highest surface to weight ratio of all the nanomaterials, which opens the door to the design of more sensitive and reliable chemical sensors. The variety of properties and the possibility of tuning the chemical and surface properties of the 2D materials increase their potential as selective sensors, targeting chemical species that were previously difficult to detect. The planar structure and the mechanical flexibility of the sheets allow new sensor designs and put 2D materials at the forefront of all the candidates for wearable applications. When developing sensors for alcohol, the response time is an essential factor for many industrial and forensic applications, particularly when it comes to hand-held devices. Here, we review recent developments in the applications of 2D materials in sensing alcohols along with a study on parameters that affect the sensing capabilities. The review also discusses the strategies used to develop the sensor along with their mechanisms of sensing and provides a critique of the current limitations of 2D materials-based alcohol sensors and an outlook for the future research required to overcome the challenges.
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Affiliation(s)
- Ramin Boroujerdi
- Faculty of Science and Technology, Bournemouth University, Talbot Campus, Fern Barrow, Poole, BH12 5BB, UK.
| | - Amor Abdelkader
- Faculty of Science and Technology, Bournemouth University, Talbot Campus, Fern Barrow, Poole, BH12 5BB, UK.
- Department of Engineering, University of Cambridge, Cambridge, CB3 0FS, UK.
| | - Richard Paul
- Faculty of Science and Technology, Bournemouth University, Talbot Campus, Fern Barrow, Poole, BH12 5BB, UK.
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