A core–shell AuNRs@BUT-16 nanocomposite for enhancement SERS detection and efficient removal of deoxynivalenol

Abstract

IntroductionDeoxynivalenol (DON) is widely found in grains and poses a serious threat to human health, so there is an urgent need to develop methods for its simultaneous removal and detection. The novel metal organic framework (MOF) material BUT-16 has a high adsorption capacity (79.8%) for DON. Meanwhile, surface-enhanced Raman spectroscopy (SERS) has been widely used for rapid detection of analytes. ObjectivesThe aim of this work is to prepare a material that can be used for enhancement SERS detection and efficient removal of DON. MethodsAuNRs@BUT-16 was prepared by in-situ solvothermal method and characterized using a series of characterization tools. AuNRs@BUT-16 was used as an adsorbent and SERS substrate for the removal and detection of DON, and some factors affecting the adsorption and SERS detection were investigated. The adsorption mechanism between DON and AuNRs@BUT-16 was investigated using molecular docking. The proposed SERS method was used to detect DON contamination in real samples. ResultsThe prepared core–shell AuNRs@BUT-16 showed a synergistic effect in improving DON adsorption and SERS response. 97.6 % of DON was removed by AuNRs@BUT-16 in aqueous solution, and 70 % in 80 % methanol. The pre-enrichment effect of BUT-16 could trap more DON molecules in the “hot spots” of AuNRs, thus the proposed SERS sensor based on AuNRs@BUT-16 substrate displayed outstanding SERS response and the limit of detection of DON was 3.87 × 10−3 μg/mL. Molecular docking revealed that hydrogen bond and π-alkyl interaction were the main reasons for high affinity between BUT-16 and DON, and Au-O bonds facilitated the adsorption of DON on AuNRs. ConclusionsAuNRs@BUT-16 with superior enrichment and SERS detection capabilities has been used for simultaneous removal and SERS detection of DON, and it also has great potential to realize sensitive and selective detection and removal of DON in multiple disciplines.