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Behera P, De M. Surface-Engineered Nanomaterials for Optical Array Based Sensing. Chempluschem 2024; 89:e202300610. [PMID: 38109071 DOI: 10.1002/cplu.202300610] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/01/2023] [Indexed: 12/19/2023]
Abstract
Array based sensing governed by optical methods provides fast and economic way for detection of wide variety of analytes where the ideality of detection processes depends on the sensor element's versatile mode of interaction with multiple analytes in an unbiased manner. This can be achieved by either the receptor unit having multiple recognition moiety, or their surface property should possess tuning ability upon fabrication called surface engineering. Nanomaterials have a high surface to volume ratio, making them viable candidates for molecule recognition through surface adsorption phenomena, which makes it ideal to meet the above requirements. Most crucially, by engineering a nanomaterial's surface, one may produce cross-reactive responses for a variety of analytes while focusing solely on a single nanomaterial. Depending on the nature of receptor elements, in the last decade the array-based sensing has been considering as multimodal detection platform which operates through various pathway including single channel, multichannel, binding and indicator displacement assay, sequential ON-OFF sensing, enzyme amplified and nanozyme based sensing etc. In this review we will deliver the working principle for Array-based sensing by using various nanomaterials like nanoparticles, nanosheets, nanodots and self-assembled nanomaterials and their surface functionality for suitable molecular recognition.
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Affiliation(s)
- Pradipta Behera
- Department of Organic Chemistry, Indian Institute of Science, Bangalore, 560012, India
| | - Mrinmoy De
- Department of Organic Chemistry, Indian Institute of Science, Bangalore, 560012, India
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Oliveira BB, Ferreira D, Fernandes AR, Baptista PV. Engineering gold nanoparticles for molecular diagnostics and biosensing. WILEY INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2023; 15:e1836. [PMID: 35932114 DOI: 10.1002/wnan.1836] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 06/22/2022] [Accepted: 07/13/2022] [Indexed: 01/31/2023]
Abstract
Advances in nanotechnology and medical science have spurred the development of engineered nanomaterials and nanoparticles with particular focus on their applications in biomedicine. In particular, gold nanoparticles (AuNPs) have been the focus of great interest, due to their exquisite intrinsic properties, such as ease of synthesis and surface functionalization, tunable size and shape, lack of acute toxicity and favorable optical, electronic, and physicochemical features, which possess great value for application in biodetection and diagnostics purposes, including molecular sensing, photoimaging, and application under the form of portable and simple biosensors (e.g., lateral flow immunoassays that have been extensively exploited during the current COVID-19 pandemic). We shall discuss the main properties of AuNPs, their synthesis and conjugation to biorecognition moieties, and the current trends in sensing and detection in biomedicine and diagnostics. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > In Vitro Nanoparticle-Based Sensing Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.
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Affiliation(s)
- Beatriz B Oliveira
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal.,i4HB, Associate Laboratory-Institute for Health and Bioeconomy, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Daniela Ferreira
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal.,i4HB, Associate Laboratory-Institute for Health and Bioeconomy, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Alexandra R Fernandes
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal.,i4HB, Associate Laboratory-Institute for Health and Bioeconomy, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
| | - Pedro Viana Baptista
- UCIBIO, Departamento de Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal.,i4HB, Associate Laboratory-Institute for Health and Bioeconomy, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal
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Li Z, Jiang Y, Tang S, Zou H, Wang W, Qi G, Zhang H, Jin K, Wang Y, Chen H, Zhang L, Qu X. 2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification. Mikrochim Acta 2022; 189:273. [PMID: 35792975 PMCID: PMC9259531 DOI: 10.1007/s00604-022-05368-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/04/2022] [Indexed: 11/28/2022]
Abstract
An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 105 ~ 108 CFU/mL for Escherichia coli, 102 ~ 107 CFU/mL for E. coli-β, 103 ~ 108 CFU/mL for Staphylococcus aureus, 103 ~ 107 CFU/mL for MRSA, 102 ~ 108 CFU/mL for Pseudomonas aeruginosa, 103 ~ 108 CFU/mL for Enterococcus faecalis, 102 ~ 108 CFU/mL for Klebsiella pneumoniae, and 103 ~ 108 CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.
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Affiliation(s)
- Zhijun Li
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Yizhou Jiang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Shihuan Tang
- Department of Clinical Laboratory, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518017, Guangdong, China
| | - Haixia Zou
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Wentao Wang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Guangpei Qi
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Hongbo Zhang
- Pharmaceutical Sciences Laboratory, Åbo Akademi University, 20520, Turku, Finland.
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520, Turku, Finland.
| | - Kun Jin
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China
| | - Yuhe Wang
- School of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao, 266580, China
| | - Hong Chen
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, 361005, China
| | - Liyuan Zhang
- School of Petroleum Engineering, State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao, 266580, China.
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA.
| | - Xiangmeng Qu
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, 518017, China.
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