Recent progress in Arduino- and smartphone-based sensors for biochemical and environmental analysis
Recent SARS-CoV-2 pandemic and environmental disasters have emphasized the importance of rapid and efficient sensing platforms. Scientists have focused on developing portable sensing platforms to avoid using bulky and high-cost analytical equipment. Smartphones are the most accessible gadgets with i...
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| Vydáno v: | TrAC, Trends in analytical chemistry (Regular ed.) Ročník 183; s. 118103 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.02.2025
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| Témata: | |
| ISSN: | 0165-9936 |
| On-line přístup: | Získat plný text |
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| Abstract | Recent SARS-CoV-2 pandemic and environmental disasters have emphasized the importance of rapid and efficient sensing platforms. Scientists have focused on developing portable sensing platforms to avoid using bulky and high-cost analytical equipment. Smartphones are the most accessible gadgets with integrated sensors to perform analytical analysis. However, the constant advances and competition in smartphones result in upgrades to smartphones, including their sensors, every year. Moreover, differences in smartphones’ sensor and image processing algorithms that vary from model to model complicate the standardization of readers. Arduino microcontroller-based readers have been investigated as an alternative to the smartphone data reader. Although Arduino-based sensors are not as portable as smartphones and cannot perform other tasks than they are developed and programmed for, they are usually built from a limited number of components, thus simplifying their standardization. In this review, we outline the different approaches used to develop smartphone-based analytical sensors and the development of Arduino-based sensors. These approaches will mainly focus on developing novel sensing platforms, the choice of smartphone sensors, and data processing methods. We also discuss the application of smartphone- and Arduino-based sensors for biochemical and environmental monitoring.
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•Providing an overview of current developments and applications of smartphones and Arduino in analytical chemistry.•The advantages and limitations of various smartphone and Arduino-based sensors have been discussed.•Combining smartphone and Arduino-based sensors addresses each other's limitations.•Integrating AI and neural engines enhances future diagnostics. |
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| AbstractList | Recent SARS-CoV-2 pandemic and environmental disasters have emphasized the importance of rapid and efficient sensing platforms. Scientists have focused on developing portable sensing platforms to avoid using bulky and high-cost analytical equipment. Smartphones are the most accessible gadgets with integrated sensors to perform analytical analysis. However, the constant advances and competition in smartphones result in upgrades to smartphones, including their sensors, every year. Moreover, differences in smartphones’ sensor and image processing algorithms that vary from model to model complicate the standardization of readers. Arduino microcontroller-based readers have been investigated as an alternative to the smartphone data reader. Although Arduino-based sensors are not as portable as smartphones and cannot perform other tasks than they are developed and programmed for, they are usually built from a limited number of components, thus simplifying their standardization. In this review, we outline the different approaches used to develop smartphone-based analytical sensors and the development of Arduino-based sensors. These approaches will mainly focus on developing novel sensing platforms, the choice of smartphone sensors, and data processing methods. We also discuss the application of smartphone- and Arduino-based sensors for biochemical and environmental monitoring.
[Display omitted]
•Providing an overview of current developments and applications of smartphones and Arduino in analytical chemistry.•The advantages and limitations of various smartphone and Arduino-based sensors have been discussed.•Combining smartphone and Arduino-based sensors addresses each other's limitations.•Integrating AI and neural engines enhances future diagnostics. |
| ArticleNumber | 118103 |
| Author | Tawfik, Salah M. Sharipov, Mirkomil Ryu, WonHyoung Azizov, Shavkatjon Uzokboev, Shakhzodjon Nghia, Nguyen Ngoc Lee, Yong-Ill |
| Author_xml | – sequence: 1 givenname: Mirkomil surname: Sharipov fullname: Sharipov, Mirkomil organization: Anastro Laboratory, Institute of Basic Science, Changwon National University, Changwon, 51140, Republic of Korea – sequence: 2 givenname: Shakhzodjon surname: Uzokboev fullname: Uzokboev, Shakhzodjon organization: Department of Pharmaceutical Sciences, Pharmaceutical Technical University, Tashkent, 100084, Republic of Uzbekistan – sequence: 3 givenname: Nguyen Ngoc surname: Nghia fullname: Nghia, Nguyen Ngoc organization: Institute of Applied Materials Science, Vietnam Academy of Science and Technology, Viet Nam – sequence: 4 givenname: Shavkatjon surname: Azizov fullname: Azizov, Shavkatjon organization: Department of Pharmaceutical Sciences, Pharmaceutical Technical University, Tashkent, 100084, Republic of Uzbekistan – sequence: 5 givenname: WonHyoung surname: Ryu fullname: Ryu, WonHyoung organization: School of Mechanical Engineering, Yonsei University, Seoul, 03722, Republic of Korea – sequence: 6 givenname: Salah M. surname: Tawfik fullname: Tawfik, Salah M. organization: Department of Petrochemicals, Egyptian Petroleum Research Institute, Cairo, 11727, Egypt – sequence: 7 givenname: Yong-Ill orcidid: 0000-0001-5383-9801 surname: Lee fullname: Lee, Yong-Ill email: yilee@changwon.ac.kr organization: Anastro Laboratory, Institute of Basic Science, Changwon National University, Changwon, 51140, Republic of Korea |
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