A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning.
A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning.
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DOI：
10.3390/bios12090710
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发表时间：
2022-09-01
期刊：
影响因子：
5.4
通讯作者：
Torres-Torres, Carlos
中科院分区：
文献类型：
--
作者：
Alberto Arano-Martinez, Jose;Lizbeth Martinez-Gonzalez, Claudia;Isabel Salazar, Ma;Torres-Torres, Carlos
通过自动传感器解读信息的能力是现代技术最重要的支柱之一。特别是，生物传感器的潜力被用于评估生物体的生物信息，并在新冠病毒入侵等战场上发现危险或预测紧急情况。这项工作致力于描述光学生物传感器的全景概述，可以通过非线性光学和机器学习方法的帮助来改进。光学生物传感器已经证明了它们在检测各种病毒方面的有效性。特别是新冠病毒在世界范围内引起了混乱，生物传感器成为了根据物理和化学现象进行分析的关键。从这个角度来看，我们强调了多光子相互作用是如何提高生物传感器的灵敏度的。非线性光学效应为扩展光学生物传感器的应用提供了一系列选择。非线性与计算机工具相结合适用于复杂低维介质的识别。机器学习方法可以近似函数来揭示人体动态物体检测中的模式，并确定细胞中的病毒，有害实体或奇怪动力学。
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.
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影响因子：
4.7
作者：
通讯作者：
--
DOI：
10.1007/s44164-022-00022-z
发表时间：
2022
期刊：
In vitro models
影响因子：
--
作者：
通讯作者：
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影响因子：
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作者：
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通讯作者：
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作者：
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通讯作者：
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影响因子：
12.6
作者：
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