MoS2/MWCNTs porous nanohybrid network with oxidase-like characteristic as electrochemical nanozyme sensor coupled with machine learning for intelligent analysis of carbendazim

Zhu, Xiaoyu; Liu, Peng; Yu, Ge; Wu, Ruimei; Xue, Ting; Sheng, Yingying; Ai, Shirong; Tang, Kaijie; Wen, Yangping

doi:10.1016/j.jelechem.2020.113940

Cited by 62 publications

(28 citation statements)

References 37 publications

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“…Therefore, the Au@Pt-dsDNA-Fe 3 O 4 biosensor of this competitive assay holds the potential as a sensitive and reliable assay for residual trace detection of pesticides. Compared with other assays based on nanomaterials, such as Au/Fe 3 O 4 (6), UCNPs-MnO 2 (34), AuNPs (35,36), Nd 2 O 3 (37) , and MoS 2 /MWCNTs (38) for CBZ detection is compiled in competitive assay based on dual-mode Au@Pt-DNA biosensors acquires much lower LOD than other nanomaterials. Besides, the LOD of this assay is much lower than the MRL of CBZ set by China in various agro-products.…”

Section: Specificitymentioning

confidence: 98%

A Competitive Assay Based on Dual-Mode Au@Pt-DNA Biosensors for On-Site Sensitive Determination of Carbendazim Fungicide in Agricultural Products

Zhai

Liu

et al. 2022

Front. Nutr.

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Carbendazim (CBZ), a systemic, broad-spectrum benzimidazole fungicide, is widely used to control fungal diseases in agricultural products. Its residues might pose risks to human health and the environment. Therefore, it is warranted to establish a rapid and reliable method for its residual quantification. Herein, we proposed a competitive assay that combined aptamer (DNA) specific recognition and bimetallic nanozyme gold@platinum (Au@Pt) catalysis to trace the CBZ residue. The DNA was labeled onto bimetallic nanozyme Au@Pt surface to produce Au@Pt probes (Au@Pt-DNA). The magnetic Fe3O4 was functionalized with a complementary strand of DNA (C-DNA) to form Fe3O4 probes (Fe3O4-C-DNA). Subsequently, the CBZ and the Fe3O4 probes competitively react with Au@Pt probes to form two Au@Pt-DNA biosensors (Au@Pt-ssDNA-CBZ and Au@Pt-dsDNA-Fe3O4). The Au@Pt-ssDNA-CBZ biosensor was designed for qualitative analysis through a naked-eye visualization strategy in the presence of CBZ. Meanwhile, Au@Pt-dsDNA-Fe3O4 biosensor was developed to quantitatively analyze CBZ using a multifunctional microplate reader. A competitive assay based on the dual-mode Au@Pt-DNA biosensors was established for onsite sensitive determination of CBZ. The limit of detection (LOD) and recoveries of the developed assay were 0.038 ng/mg and 71.88-110.11%, with relative standard deviations (RSDs) ranging between 3.15 and 10.91%. The assay demonstrated a good correlation with data acquired from liquid chromatography coupled with mass spectrometry/mass spectrometry analysis. In summary, the proposed competitive assay based on dual-mode Au@Pt-DNA biosensors might have a great potential for onsite sensitive detection of pesticides in agro-products.

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Section: Specificitymentioning

confidence: 98%

A Competitive Assay Based on Dual-Mode Au@Pt-DNA Biosensors for On-Site Sensitive Determination of Carbendazim Fungicide in Agricultural Products

Zhai

Liu

et al. 2022

Front. Nutr.

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“…There have been studies that attempt to gain the advantages of both systems by creating artificial bioreceptors, notably nanomaterials with enzymatic properties referred to as nanozymes [ 168 , 169 ]. While exciting progress has been made in this field, current nanozyme-based biosensors have inferior catalytic activity and specificity to their biological alternatives [ 170 , 171 ].…”

Section: Considerations and Future Perspectivesmentioning

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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“…The application of machine learning for 2D TMD-based biosensors is extremely attractive to improve detection performance. Zhu and colleagues reported a platform that combined machine learning (ML) and MoS 2 /MWCNTs porous nanohybrid network with oxidase-like characteristics as electrochemical nanozymes sensor to analyze carbendazim (CBZ) residues in tea and rice samples [135]. The authors utilized an artificial neural network (ANN) to learn the relationship between the processing parameters (e.g., electrochemical signals and the CBZ concentrations) to build an accurate and predictive regression model for the platform.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Recent Advances in Two-Dimensional Transition Metal Dichalcogenide Nanocomposites Biosensors for Virus Detection before and during COVID-19 Outbreak

et al. 2021

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The deadly Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) outbreak has become one of the most challenging pandemics in the last century. Clinical diagnosis reports a high infection rate within a large population and a rapid mutation rate upon every individual infection. The polymerase chain reaction has been a powerful and gold standard molecular diagnostic technique over the past few decades and hence a promising tool to detect the SARS-CoV-2 nucleic acid sequences. However, it can be costly and involved in complicated processes with a high demand for on-site tests. This pandemic emphasizes the critical need for designing cost-effective and fast diagnosis strategies to prevent a potential viral source by ultrasensitive and selective biosensors. Two-dimensional (2D) transition metal dichalcogenide (TMD) nanocomposites have been developed with unique physical and chemical properties crucial for building up nucleic acid and protein biosensors. In this review, we cover various types of 2D TMD biosensors available for virus detection via the mechanisms of photoluminescence/optical, field-effect transistor, surface plasmon resonance, and electrochemical signals. We summarize the current state-of-the-art applications of 2D TMD nanocomposite systems for sensing proteins/nucleic acid from different types of lethal viruses. Finally, we identify and discuss the advantages and limitations of TMD-based nanocomposites biosensors for viral recognition.

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MoS2/MWCNTs porous nanohybrid network with oxidase-like characteristic as electrochemical nanozyme sensor coupled with machine learning for intelligent analysis of carbendazim

Cited by 62 publications

References 37 publications

A Competitive Assay Based on Dual-Mode Au@Pt-DNA Biosensors for On-Site Sensitive Determination of Carbendazim Fungicide in Agricultural Products

A Competitive Assay Based on Dual-Mode Au@Pt-DNA Biosensors for On-Site Sensitive Determination of Carbendazim Fungicide in Agricultural Products

Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors

Recent Advances in Two-Dimensional Transition Metal Dichalcogenide Nanocomposites Biosensors for Virus Detection before and during COVID-19 Outbreak

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