Anion- and pH-sensing behaviors of an imidazole-dicarboxylate-based Ru(II)–bipyridine complex possessing a number of dissociable protons in its secondary coordination sphere are employed here for the creation of multiple Boolean and fuzzy logic systems. The absorption, emission, and electrochemical behaviors of the metalloreceptor were significantly modulated upon the influence of basic anions (such as F^–, AcO^–, and H₂PO₄^–) as well as by altering the pH of the solution. Interestingly, the deprotonation of the metalloreceptor by selected anions or by alkaline pH, followed by its restoration to its original form by acid or acidic pH is reversible and could be repeated many times. The metalloreceptor is capable to demonstrate several advanced Boolean functions, namely, three-input OR gate, set–reset flip-flop logic, and traffic signal, by employing its electrochemical responses through proper use of different inputs. Administering exhaustive sensing experiments by changing the analyte concentration within a wide range is usually tedious as well as exorbitantly costly. To get rid of these difficulties, we employed here several soft computing approaches such as artificial neural networks (ANN), fuzzy logic systems (FLS), or adaptive neuro–fuzzy inference system (ANFIS) to foresee the experimental sensing data and to appropriately model the protonation–deprotonation behaviors of the metalloreceptor. Reasonably good correlation between the experimental and model output data is also reflected in their tested root-mean-square error values (0.115961 and 0.118894 for the ANFIS model).

中文翻译：

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用于模拟基于咪唑-二羧酸盐的 Ru(II)-联吡啶配合物的电化学离子传感数据的神经模糊化架构

基于咪唑-二羧酸盐的 Ru(II)-联吡啶配合物在其二级配位域中具有许多可离解质子的阴离子和 pH 传感行为被用于创建多个布尔和模糊逻辑系统。金属受体的吸收、发射和电化学行为在碱性阴离子（如 F ^–、AcO ^–和 H ₂ PO ₄ ^–) 以及通过改变溶液的 pH 值。有趣的是，金属受体通过选定的阴离子或碱性 pH 去质子化，然后通过酸性或酸性 pH 恢复其原始形式是可逆的，并且可以重复多次。通过正确使用不同的输入来利用其电化学响应，金属感受器能够展示几种高级布尔函数，即三输入或门、置位-复位触发器逻辑和交通信号。通过在大范围内改变分析物浓度来进行详尽的传感实验通常是乏味的，而且成本过高。为了摆脱这些困难，我们在这里采用了几种软计算方法，例如人工神经网络（ANN）、模糊逻辑系统（FLS）、或自适应神经模糊推理系统（ANFIS）来预测实验传感数据并适当地模拟金属受体的质子化 - 去质子化行为。实验和模型输出数据之间相当好的相关性也反映在它们测试的均方根误差值（ANFIS 模型为 0.115961 和 0.118894）中。