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A facile AuNP@aptamer modified mercaptosiloxane-based hybrid affinity monolith with an unusually high coverage density of aptamer for on-column selective extraction of ochratoxin A

Abstract

A convenient and high-performance AuNPs@aptamer modified mercaptosiloxane-based hybrid affinity monolithic column was facilely prepared with an unusually high coverage density of aptamers and used for on-column selective recognition of ochratoxin A (OTA). Attributing to the high surface-to-volume ratio of AuNPs, the robust conjugation of Au-SH and large specific surface area of hybrid-silica monolith, a high coverage density of 5’-SH-aptamers up to 3494 pmol/μL was achieved, which was 2.5~10 folds more than that of other affinity monoliths modified with AuNPs@Apt reported previously. By using OTA as model analyte, a highly selective recognition of OTA was carried out via the online coupling with HPLC, and the cross-reactivity towards the analogues such as OTB and aflatoxin B1 were weak. High recovery yields of OTA were achieved more than 92% (n = 3) even if OTB was added with a high concentration level up to 50 ng/mL. Applied to sample analysis, an efficient discrimination of OTA was successfully obtained with a sensitive detection limit as low as 25 pg/mL. The recovery yields of OTA with different fortified levels were achieved in 88.6% ~ 94.1% and 88.2% ~ 94.3% for beer and wine samples respectively. This protocol provided an attractive access to fabricating an attractive affinity monolith modified with abundant of aptamers for highly selective and sensitive on-column extraction of target analyte (OTA).

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Publication details

The article was received on 08 Aug 2018, accepted on 14 Sep 2018 and first published on 14 Sep 2018


Article type: Paper
DOI: 10.1039/C8AN01531G
Citation: Analyst, 2018, Accepted Manuscript
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    A facile AuNP@aptamer modified mercaptosiloxane-based hybrid affinity monolith with an unusually high coverage density of aptamer for on-column selective extraction of ochratoxin A

    J. Chi, M. Chen, L. Deng, X. Lin and Z. Xie, Analyst, 2018, Accepted Manuscript , DOI: 10.1039/C8AN01531G

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