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O-Vanillin scaffold as selective chemosensor of PO43- and the application of neural network based soft computing to predict machine learning outcome
New Journal of Chemistry ( IF 2.7 ) Pub Date : 2024-07-24 , DOI: 10.1039/d4nj02462a
Ajay Misra , Naren Mudi , Shashanka Shekhar Samanta , Sourav Mandal , Suraj Barman , Hasibul Beg

O-Vaniline derived Schiff base 1-[(E)-(2-Hydroxy-3-methoxybenzylidene) amino]-4-methylthiosemicarbazone (VCOH) has been synthesized for colorimetric and fluorescence chemosensor towards PO43- ion. Fluorescence ‘turn-on’ sensing knack of VCOH towards PO43- ion has been explained due to emission from the VCO– ion formed upon transfer of the phenolic proton of VCOH to PO43- ion. The 1:1 stoichiometry between the probe VCOH and PO43- ion is confirmed by Job’s plot based on UV-Vis titration. Limit of detection (LOD) of VCOH towards PO43- ion is found to be 0.49 nM. The PO43- ion sensing property of probe VCOH has been applied to prepare portable paper strips and the analysis of real water samples. Fluorescence ‘turn-on’ and ‘turn-off’ responses of VCOH towards PO43-and H+ respectively have been used to construct molecular logic gate. Fluorescence based sensing study by adjusting the concentration of analytes over a broad range can be both laborious and expensive. In order to address these challenges, we have utilized various soft computing methods, including artificial neural networks (ANN), fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS), to appropriately model the ‘turn-on’ and ‘turn-off’ behaviors of the VCOH probe upon addition of PO43- and H+ respectively as well as to predict the experimental sensing data.

中文翻译:


O-香草醛支架作为 PO43- 的选择性化学传感器以及基于神经网络的软计算在预测机器学习结果中的应用



邻香草醛衍生的希夫碱 1-[(E)-(2-羟基-3-甲氧基亚苯亚甲基)氨基]-4-甲硫缩氨基脲 (VCOH) 已被合成,用于 PO43- 离子的比色和荧光化学传感器。 VCOH 对 PO43- 离子的荧光“开启”传感技巧已被解释为由于 VCOH 的酚质子转移到 PO43- 离子时形成的 VCO- 离子发射。基于紫外-可见滴定的乔布图证实了探针 VCOH 和 PO43- 离子之间的 1:1 化学计量。 VCOH 对 PO43- 离子的检测限 (LOD) 为 0.49 nM。 VCOH探针的PO43-离子传感特性已应用于便携式纸条的制备和实际水样的分析。 VCOH 对 PO43- 和 H+ 的荧光“开启”和“关闭”响应分别用于构建分子逻辑门。通过在较宽范围内调整分析物浓度来进行基于荧光的传感研究可能既费力又昂贵。为了应对这些挑战,我们利用了各种软计算方法,包括人工神经网络(ANN)、模糊逻辑(FL)和自适应神经模糊推理系统(ANFIS),以适当地模拟“开启”和分别添加 PO43- 和 H+ 后 VCOH 探针的“关闭”行为,并预测实验传感数据。
更新日期:2024-07-24
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