ABSTRACT
Light-driven asymmetric photocatalysis represents a straightforward approach in modern organic chemistry. In comparison to the homogeneous one, heterogeneous asymmetric photocatalysis has the advantages of easy catalyst separation, recovery, and reuse, thus being cost- and time-effective. Here, we demonstrate how plasmon-active centers (gold nanoparticles - AuNPs) allow visible light triggering of chiral catalyst (proline) in model aldol reaction between acetone and benzaldehyde. The metal-organic framework UiO-66-NH2 was used as an advanced host platform for the loading of proline and AuNPs and their stabilization in spatial proximity. Aldol reactions were carried out at a low temperature (-20 °C) under light illumination which resulted in 91% ee with a closed-to-quantitative yield, 4.5 times higher than that without light (i.e. in the absence of plasmon triggering). A set of control experiments and quantum chemical modeling revealed that the plasmon assistance proceeds through hot electron excitation followed by an interaction with an enamine with the formation of anion radical species. We also demonstrated the high stability of the proposed system in multiple catalytic cycles without leaching metal ions, which makes our approach especially promising for heterogeneous asymmetric photocatalysis.
ABSTRACT
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.
