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In this work, we proposed the functionalization of a surface plasmon-polariton (SPP)-supported gold grating surface with the metal-organic framework (MOF-5) for sensitive, selective and reproducible surface-enhanced Raman scattering (SERS) detection of organophosphorus pesticides. Homogeneous distribution of plasmon intensity along the Au grating surface ensures the high reproducibility of SERS results (deviation of Raman peak intensity does not exceed the 4% along the sample). The surface-assisted growth of thin MOF-5 film was accomplished in two steps procedure: (i) covalent grafting by 4-carboxyphenyl groups and (ii) the immersion of samples in the mother liquid of MOF-5. Proposed SERS chip proved itself to be a perfect analytical probe for the detection of organophosphorus pesticides with high reliability and low detection limit up to 10−12 M. Moreover, selective detection and recognition of several relevant organic contaminants (azo-dye, mycotoxin, and pesticide) from the simulated soil was successfully demonstrated. All SERS measurements were performed using portable Raman spectrometer and can easily be expanded to environmental conditions. Our work combines the high affinity of organic contaminants to the MOF-5 with excellent plasmonic excitation on the surface plasmon-polariton supported structure and shows the way to the realization of closed-to-ideal analytical SERS chip. Image 1 • The functionalization of an SPP-supported gold grating surface with the metal-organic framework (MOF-5) for sensitive, selective and reproducible SERS detection of organophosphorus pesticides is proposed. • The growth of MOF film on 2D SERS active substrate was demonstrated for the first time. • Proposed Au/MOF-5 grating can be used as a perfect SERS probe for the detection of organophosphorus pesticides with the LODs down to 10−14 M and perfect SERS results reproducibility on the one substrate, as well as between the individual substrates. • Additionally, the proposed SERS substrates can efficiently be used for the analysis of pesticides, azo-dye, and mycotoxin mixture in the soil matrix. • All SERS measurements were performed using portable Raman spectrometer and can easily be expanded to environmental conditions. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of "minor" molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10−16 M) on the background of human blood plasma becomes possible. [Display omitted] • A superhydrophobic, biomimetic SERS-active substrates were proposed. • Analyte self-concentration by drop evaporation in a "flipped over" state was demonstrated. • Approach was used for semiquantitative miRNA-21 detection in human blood plasma. • SERS spectra interpretation was performed using machine learning approach. • A low detection limit of miRNA-21 (10−16 M) in human plasma was obtained using the SERS-ML combination. [ABSTRACT FROM AUTHOR]