Advances in Machine‐Learning Enhanced Nanosensors: From Cloud Artificial Intelligence Toward Future Edge Computing at Chip Level
Zixuan Zhang,
Xinmiao Liu,
Hong Zhou
et al.
Abstract:Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of t… Show more
“…Complete transparency in the investigation of the safety of nanosensors, coupled with an education programme on the benefits and promise of nanosensors, can help confront these concerns. Possible developments in this technology would be linked to increased computational power for analysis and improved networking between nanosensor devices [219]. Another possibility involves the generation of multiple sensing systems into single devices aided by improved manufacturing techniques and computational tools that can aid the analysis of multiple data streams [95].…”
Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.
“…Complete transparency in the investigation of the safety of nanosensors, coupled with an education programme on the benefits and promise of nanosensors, can help confront these concerns. Possible developments in this technology would be linked to increased computational power for analysis and improved networking between nanosensor devices [219]. Another possibility involves the generation of multiple sensing systems into single devices aided by improved manufacturing techniques and computational tools that can aid the analysis of multiple data streams [95].…”
Nanotechnology, nanosensors in particular, has increasingly drawn researchers’ attention in recent years since it has been shown to be a powerful tool for several fields like mining, robotics, medicine and agriculture amongst others. Challenges ahead, such as food availability, climate change and sustainability, have promoted such attention and pushed forward the use of nanosensors in agroindustry and environmental applications. However, issues with noise and confounding signals make the use of these tools a non-trivial technical challenge. Great advances in artificial intelligence, and more particularly machine learning, have provided new tools that have allowed researchers to improve the quality and functionality of nanosensor systems. This short review presents the latest work in the analysis of data from nanosensors using machine learning for agroenvironmental applications. It consists of an introduction to the topics of nanosensors and machine learning and the application of machine learning to the field of nanosensors. The rest of the paper consists of examples of the application of machine learning techniques to the utilisation of electrochemical, luminescent, SERS and colourimetric nanosensor classes. The final section consists of a short discussion and conclusion concerning the relevance of the material discussed in the review to the future of the agroenvironmental sector.
“…Intelligent robots 1 – 3 and other smart devices 4 , 5 , as powerful helpers for human work, have been widely used in military reconnaissance 6 , rescue 7 , unknown environment exploration 8 , and some assistant equipment 9 – 12 . Environmental reconnaissance provides timely, accurate, and reliable information that strongly supports the development and implementation of strategic plans.…”
Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions.
Molecular identification of volatile organic compounds (VOCs) plays an important role in various applications including environmental monitoring and smart farming. Mid‐infrared (MIR) fingerprint absorption spectroscopy is a powerful tool to extract chemical‐specific features for gas identification. However, the detection and recognition of trace VOC gas mixtures remain challenging due to their intrinsic weak light–matter interaction and highly overlapped absorption spectra. Here, an artificial intelligence‐enhanced “photonic nose” for MIR spectroscopic analysis of trace VOC gas mixtures is proposed. To enhance the sensing performance by increasing bandwidth and sensitivity, the “photonic nose” is designed to employ coupled multi‐resonant plasmonic nanoantennas to cover MIR molecular fingerprints, coated with metal–organic frameworks as the gas enrichment layer. Low limits of detection are achieved (IPA: 1.99 ppm, ethanol: 3.43 ppm, and acetone: 9.82 ppm). With machine learning, a high classification accuracy of 100% is realized for 125 mixing ratios (IPA, ethanol: both 5 concentrations, 0–130 ppm; acetone: 5 concentrations, 0–201 ppm), and low‐deviation component concentration predictions of root‐mean‐squared error within 10 ppm are achieved for IPA and ethanol (both 0–130 ppm) under interference from 50 ppm acetone. The work paves the way for intelligent sensing platforms for environmental monitoring and smart framing.
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