Disposable Capacitive Biosensor for Dopamine Sensing

Dhanjai,; Yu, Nancy; Mugo, Samuel M.

doi:10.1002/slct.202002865

Cited by 18 publications

(15 citation statements)

References 41 publications

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“…[ 73 ] A case in point is the tyrosinase biosensors for rapid detection of dopamine, in which tyrosinase catalyzed dopamine oxidation to o ‐dopaquinone was analyzed by CV. [ 74 ] Moreover, CV was used to evaluate the electrochemical behaviors of stretchable biosensors under different strains. [ 59,75 ] As shown in Figure 3d, the CV curves under different strains showed little difference, indicating the sensor was strain‐insensitive.…”

Section: Noninvasive Biosensors and Collected Physiological Informationmentioning

confidence: 99%

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang

Wang

Liu

et al. 2021

Adv Healthcare Materials

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The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health‐related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning‐reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning‐reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning‐reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real‐time monitoring, out‐of‐clinic diagnosis, and onsite food safety detection are proposed.

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Section: Noninvasive Biosensors and Collected Physiological Informationmentioning

confidence: 99%

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang

Wang

Liu

et al. 2021

Adv Healthcare Materials

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“…As shown in Table 1, hydrogels can be divided into four categories based on the crosslinking mechanisms of gelling, and these categories include physical hydrogels, chemical hydrogels, enzymatic hydrogels, and multicrosslinked hydrogels. [98][99][100]…”

Section: Crosslinking Types and Mechanismsmentioning

confidence: 99%

Hydrogel: Diversity of Structures and Applications in Food Science

Jia

Luo

2021

Food Reviews International

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Hydrogels are a series of soft and wet materials with three dimensions of crosslinked networks. Hydrogels have attracted great attention due to their diverse functional properties, and their wide range of applications, such as in soft robots and actuators, stretched electronic devices, tissue engineering materials, controlled-release drug delivery vehicles, biomedicine materials, food science, and (bio)sensors. In general, there are four core concerns in hydrogel science, including the polymer source, structure fabrication, gel function, and gel applications. According to the logic that the "structure determines function", it is believed that rational design of structures can effectively regulate the functions and applications of hydrogels. Hence, in the current review, "structure" as the core topic will be highly regarded, and the crosslinking mechanisms and structural diversity of hydrogels are comprehensively summarized. Additionally, hydrogels also show their great application potential in food science. Hence, the current review also pays more attention to the application of hydrogels in food nutrition and health, food engineering and processing, and food safety. It is whished that this review not only serves as a reference for improving the comprehensive understanding of the structural design of hydrogels but also provides a forward-looking idea for hydrogel applications in food science.

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“…Voltammetry sensors apply electric potential to a "working" electrode and measure the current response, which is affected by analyte oxidation or reduction [100]. Cyclic voltammetry (CV) is a specific voltammetry technique in which the potential is swept across a range of values, and current response is recorded.…”

Section: Cyclic Voltammetry (Cv)mentioning

confidence: 99%

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Schackart

Yoon

2021

Sensors

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Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor’s signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.

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Disposable Capacitive Biosensor for Dopamine Sensing

Cited by 18 publications

References 41 publications

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Hydrogel: Diversity of Structures and Applications in Food Science

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

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