Neuroprostanes are bioactive oxylipins from docosahexaenoic acid oxidation, serving as oxidative stress biomarkers. In this work, the enantioselective synthesis of 4-A4-NeuroP-1,4-lactone and 4-deoxy-Δ4,6-A4-Neuro...
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Pyrrole‐Imine Macrocycle: Self‐Organizing Cross‐Reactive Anion Receptor and Sensor
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An imine macrocycle comprising pyrroles as hydrogen-bond donors and an environmentally sensitive fluorophore displays a wide cross-reactive binding, which, in combination with an analyte-specific fluorescence response allows differentiation between ten analytes and their concentrations. Thus, one cross-reactive sensor performs the work of multiple, essentially acting as a “sensor array of one molecule”.
Abstract
Self-organizing macrocyclic receptor-sensors for phosphorus oxyanions, phosphates, and phosphonates comprising imine moieties were prepared by condensation of dipyrrolylmethane dicarbaldehyde with diethylene triamine. The incorporation of flexible ethylene moieties endows the macrocycle with unprecedented flexibility and ability to accommodate numerous phosphorus oxyanions from orthophosphate to large anions such as ATP or phosphonate glyphosate. The anion binding was elucidated by NMR titrations, low-temperature NMR, and NOESY NMR. The incorporation of dansyl fluorophore enables sensing of anions using the fluorescence signal, whereas the changes in fluorescence intensity, width of the fluorescence band, and position of the maxima are analyte-specific and useful in recognition and identification of eleven different P-oxyanions in water. The affinity (K assoc) for Na+ salts was H2PO4 − ≈ Methylphosphonate > H2P2O7 2− > Phenylphosphonate- > Glyphosate2- > AMP2- > ADP2− > ATP2−. Interestingly, phosphonates, including methylphosphonate and glyphosate anions, were also found to display a strong affinity (Kassoc ∼106 M−1) while halides, nitrate, carbonates, or hydrogen sulfate did not show a significant affinity. The determined fluorescence spectral parameters were used to classify the 12 analytes (11 anions and water) using Linear Discriminant Analysis (LDA). Quantification was performed using LDA and Support Vector Machine (SVM), and the phosphonate concentrations in unknown samples were determined with an error of 3.5% or lower.
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