Sahoo Anik, Baitalik Sujoy
Inorganic Chemistry Section, Department of Chemistry, Jadavpur University, Kolkata, India.
Front Chem. 2022 Mar 23;10:864363. doi: 10.3389/fchem.2022.864363. eCollection 2022.
Anion and cation sensing aspects of a terpyridyl-imidazole based receptor have been utilized in this work for the fabrication of multiply configurable Boolean and fuzzy logic systems. The terpyridine moiety of the receptor is used for cation sensing through coordination, whereas the imidazole motif is utilized for anion sensing hydrogen bonding interaction and/or anion-induced deprotonation, and the recognition event was monitored through absorption and emission spectroscopy. The receptor functions as a selective sensor for F and Fe among the studied anions and cations, respectively. Interestingly, the complexation of the receptor by Fe and its decomplexation by F and deprotonation of the receptor by F and restoration to its initial form by acid are reversible and can be recycled. The receptor can mimic various logic operations such as combinatorial logic gate and keypad lock using its spectral responses through the sequential use of ionic inputs. Conducting very detailed sensing studies by varying the concentration of the analytes within a wide domain is often very time-consuming, laborious, and expensive. To decrease the time and expenses of the investigations, soft computing approaches such as artificial neural networks (ANNs), fuzzy logic, or adaptive neuro-fuzzy inference system (ANFIS) can be recommended to predict the experimental spectral data. Soft computing approaches to artificial intelligence (AI) include neural networks, fuzzy systems, evolutionary computation, and other tools based on statistical and mathematical optimizations. This study compares fuzzy, ANN, and ANFIS outputs to model the protonation-deprotonation and complexation-decomplexation behaviors of the receptor. Triangular membership functions () are used to model the ANFIS methodology. A good correlation is observed between experimental and model output data. The testing root mean square error (RMSE) for the ANFIS model is 0.0023 for protonation-deprotonation and 0.0036 for complexation-decomplexation data.
在这项工作中，基于三联吡啶 - 咪唑的受体的阴离子和阳离子传感特性被用于构建多重可配置的布尔逻辑和模糊逻辑系统。受体的三联吡啶部分通过配位用于阳离子传感，而咪唑基序则通过氢键相互作用和/或阴离子诱导的去质子化用于阴离子传感，并且通过吸收光谱和发射光谱监测识别事件。在研究的阴离子和阳离子中，该受体分别作为F⁻和Fe³⁺的选择性传感器。有趣的是，受体与Fe³⁺的络合以及与F⁻的解络合，以及F⁻使受体去质子化并通过酸恢复到其初始形式都是可逆的，并且可以循环利用。该受体可以通过依次使用离子输入，利用其光谱响应来模拟各种逻辑操作，如组合逻辑门和键盘锁。在很宽的范围内改变分析物的浓度进行非常详细的传感研究通常非常耗时、费力且昂贵。为了减少研究的时间和费用，可以推荐使用软计算方法，如人工神经网络（ANN）、模糊逻辑或自适应神经模糊推理系统（ANFIS）来预测实验光谱数据。人工智能（AI）的软计算方法包括神经网络、模糊系统、进化计算以及其他基于统计和数学优化的工具。本研究比较了模糊、ANN和ANFIS的输出，以模拟受体的质子化 - 去质子化和络合 - 解络合行为。使用三角形隶属函数来模拟ANFIS方法。在实验数据和模型输出数据之间观察到良好的相关性。ANFIS模型的测试均方根误差（RMSE）对于质子化 - 去质子化数据为0.0023，对于络合 - 解络合数据为0.0036。
