A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning
The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefiel...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/12/9/710 |
| _version_ | 1827662886867042304 |
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| author | Jose Alberto Arano-Martinez Claudia Lizbeth Martínez-González Ma Isabel Salazar Carlos Torres-Torres |
| author_facet | Jose Alberto Arano-Martinez Claudia Lizbeth Martínez-González Ma Isabel Salazar Carlos Torres-Torres |
| author_sort | Jose Alberto Arano-Martinez |
| collection | DOAJ |
| description | The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells. |
| first_indexed | 2024-03-10T00:35:10Z |
| format | Article |
| id | doaj.art-2fda65cd569f4eebb94aed4539ebc6c2 |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-10T00:35:10Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-2fda65cd569f4eebb94aed4539ebc6c22023-11-23T15:17:49ZengMDPI AGBiosensors2079-63742022-09-0112971010.3390/bios12090710A Framework for Biosensors Assisted by Multiphoton Effects and Machine LearningJose Alberto Arano-Martinez0Claudia Lizbeth Martínez-González1Ma Isabel Salazar2Carlos Torres-Torres3Sección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, MexicoSección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, MexicoDepartamento de Microbiología, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Mexico City 11340, MexicoSección de Estudios de Posgrado e Investigación, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Zacatenco, Instituto Politécnico Nacional, Mexico City 07738, MexicoThe ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.https://www.mdpi.com/2079-6374/12/9/710optical biosensorsphotonicsmachine learningnonlinear opticsSARS-CoV-2 |
| spellingShingle | Jose Alberto Arano-Martinez Claudia Lizbeth Martínez-González Ma Isabel Salazar Carlos Torres-Torres A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning Biosensors optical biosensors photonics machine learning nonlinear optics SARS-CoV-2 |
| title | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
| title_full | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
| title_fullStr | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
| title_full_unstemmed | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
| title_short | A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning |
| title_sort | framework for biosensors assisted by multiphoton effects and machine learning |
| topic | optical biosensors photonics machine learning nonlinear optics SARS-CoV-2 |
| url | https://www.mdpi.com/2079-6374/12/9/710 |
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