Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/101427
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Biomedical Engineering | en_US |
| dc.contributor | Department of Applied Physics | en_US |
| dc.creator | Zhang, Q | en_US |
| dc.creator | Yin, B | en_US |
| dc.creator | Hao, J | en_US |
| dc.creator | Ma, L | en_US |
| dc.creator | Huang, Y | en_US |
| dc.creator | Shao, X | en_US |
| dc.creator | Li, C | en_US |
| dc.creator | Chu, Z | en_US |
| dc.creator | Yi, C | en_US |
| dc.creator | Wong, SHD | en_US |
| dc.creator | Yang, M | en_US |
| dc.date.accessioned | 2023-09-18T02:25:42Z | - |
| dc.date.available | 2023-09-18T02:25:42Z | - |
| dc.identifier.issn | 2766-8541 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/101427 | - |
| dc.language.iso | en | en_US |
| dc.publisher | John Wiley & Sons, Inc. | en_US |
| dc.rights | © 2022 The Authors. Aggregate published by SCUT, AIEI, and John Wiley & Sons Australia, Ltd. | en_US |
| dc.rights | This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.rights | The following publication Zhang, Q., Yin, B., Hao, J., Ma, L., Huang, Y., Shao, X., ... & Yang, M. (2023). An AIEgen/graphene oxide nanocomposite (AIEgen@ GO)‐based two‐stage “turn‐on” nucleic acid biosensor for rapid detection of SARS‐CoV‐2 viral sequence. Aggregate, 4(1), e195 is available at https://doi.org/10.1002/agt2.195. | en_US |
| dc.subject | Aggregation-induced emission (AIE) luminogen | en_US |
| dc.subject | Graphene oxide | en_US |
| dc.subject | SARS-CoV-2 detection | en_US |
| dc.title | An AIEgen/graphene oxide nanocomposite (AIEgen@GO)-based two-stage “turn-on” nucleic acid biosensor for rapid detection of SARS-CoV-2 viral sequence | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 4 | en_US |
| dc.identifier.issue | 1 | en_US |
| dc.identifier.doi | 10.1002/agt2.195 | en_US |
| dcterms.abstract | The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point-of-care SARS-CoV-2 detection. In this work, we report a highly sequence-specific biosensor based on nanocomposites with aggregation-induced emission luminogens (AIEgen)-labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step-detection of SARS-CoV-2-specific nucleic acid sequences (Orf1ab or N genes). A dual “turn-on” mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first-stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second-stage enhancement of AIE-based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS-CoV-2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS-CoV-2 nucleic acid sequence before utilizing quantitative reverse transcription-polymerase chain reaction for second confirmation. | en_US |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Aggregate, Feb. 2023, v. 4, no. 1, e195 | en_US |
| dcterms.isPartOf | Aggregate | en_US |
| dcterms.issued | 2023-02 | - |
| dc.identifier.scopus | 2-s2.0-85153743735 | - |
| dc.identifier.ros | 2022002031 | - |
| dc.identifier.eissn | 2692-4560 | en_US |
| dc.identifier.artn | e195 | en_US |
| dc.description.validate | 202309 bckw | en_US |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | CDCF_2022-2023 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | Shenzhen-Hong Kong-Macao Science and Technology Plan Project; Hong Kong Polytechnic University | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.description.oaCategory | CC | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Aggregate - 2022 - Zhang.pdf | 6.05 MB | Adobe PDF | View/Open |
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