We demonstrate a new approach to chemosensors suited for analysis of phosphate mixtures. Here, a combination of a cross-reactive receptor and an environmentally sensitive fluorophore allows for binding various phosphates and obtaining analyte-specific fluorescence response. We have investigated a flexible and cross-reactive imine-pyrrole macrocycle comprising both hydrogen-bond donors and acceptors, one that is capable of self-organizing in response to phosphate-type anions. This cross-reactive binding was elucidated by theoretical and nuclear magnetic resonance (NMR) studies. Incorporating an environmentally sensitive dansyl fluorophore, which responds to the presence of analytes with a varying degree of fluorescence amplification, resulted in translating the cross-reactive binding into variable analyte response and enabled differentiation of oxyanions. The trend of anion affinities is H₂PO₄⁻>AMP>H₂P₂O7²⁻>ADP>ATP. We show that only one sensor can differentiate among various phosphates including their mixtures. By applying machine learning algorithms, we were able to differentiate between 12 anions and accurately quantify ATP, ADP, AMP, PPi, Pi, and their mixtures.

我们展示了一种适用于分析磷酸盐混合物的化学传感器的新方法。在这里，交叉反应受体和环境敏感荧光团的组合允许结合各种磷酸盐并获得分析物特异性荧光响应。我们研究了一种灵活且具有交叉反应性的亚胺-吡咯大环化合物，它包含氢键供体和受体，它能够响应磷酸盐型阴离子而自组织。通过理论和核磁共振 (NMR) 研究阐明了这种交叉反应结合。包含对环境敏感的丹磺酰荧光团，它对存在的分析物做出不同程度的荧光放大反应，导致将交叉反应性结合转化为可变的分析物响应并能够区分氧阴离子。阴离子亲和力的趋势是 H₂ PO ₄ ^- >AMP>H ₂ P ₂ O7 ^2- >ADP>ATP。我们表明，只有一种传感器可以区分各种磷酸盐，包括它们的混合物。通过应用机器学习算法，我们能够区分 12 种阴离子并准确量化 ATP、ADP、AMP、PPi、Pi 及其混合物。