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Fe(III)掺杂β-环糊精接枝壳聚糖水凝胶微球的合成与表征及其对水溶液中双氯芬酸的吸附:吸附实验和深度学习模拟。

Synthesis and characterization of Fe(III)-doped beta-cyclodextrin-grafted chitosan cryogel beads for adsorption of diclofenac in aqueous solutions: Adsorption experiments and deep-learning modeling.

机构信息

Water Environmental Systems and Deep Learning Laboratory, Department of Rural Systems Engineering, Seoul National University, Seoul, Republic of Korea.

Water Environmental Systems and Deep Learning Laboratory, Department of Rural Systems Engineering, Seoul National University, Seoul, Republic of Korea; Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.

出版信息

Int J Biol Macromol. 2024 Nov;279(Pt 1):135161. doi: 10.1016/j.ijbiomac.2024.135161. Epub 2024 Aug 28.

Abstract

Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing DCF in aqueous solutions. The beads exhibited an average size of 2.94 ± 0.66 mm and a point of zero charge of 8.03. Adsorption experiments demonstrated that the Langmuir kinetic model provided the most accurate description of the kinetic data, while the Redlich-Peterson isotherm offered the best fit for the equilibrium data. The beads showcased a theoretical maximum adsorption capacity of 712.3 mg/g for DCF, with the adsorption process being identified as exothermic. DCF adsorption on the beads was attributed to hydrogen bonding, metal cation-π interactions, and electrostatic interactions. Reusability tests exhibited that the beads could be regenerated using 0.1 M NaOH. To perform deep learning modeling, adsorption experiments (n = 17), designed utilizing central composite design (CCD), were conducted in duplicate. The CCD framework incorporated input variables such as initial DCF concentration, adsorbent dosage, and solution pH, while the output variable was the DCF removal rate. Utilizing the adsorption data, an artificial neural network (ANN) model was constructed with a topology of 3: 7:10:1, featuring 3 input variables, 7 neurons in the first hidden layer, 10 neurons in the second layer, and 1 output variable. Employing the ANN model data, 3-D response surface plots were generated to elucidate the relationship between input variables and DCF removal rate. Additional adsorption tests were conducted to evaluate the developed ANN model, affirming its reliable predictability for the DCF removal rate. Analysis of the relative importance of the input variables revealed the following order of importance: solution pH (100 %) > adsorbent dosage (75.2 %) > initial DCF concentration (57.7 %).

摘要

双氯芬酸(DCF)在水环境中经常被检测到,这强调了在全球范围内有效去除它的迫切需求。在这里,我们介绍了用于在水溶液中吸附 DCF 的 Fe(III)掺杂β-CD 接枝壳聚糖(Fe/β-CD@CS)水凝胶珠的合成。这些珠子的平均粒径为 2.94±0.66mm,零电荷点为 8.03。吸附实验表明,Langmuir 动力学模型最准确地描述了动力学数据,而 Redlich-Peterson 等温线最适合平衡数据。这些珠子对 DCF 的理论最大吸附容量为 712.3mg/g,吸附过程是放热的。DCF 在珠子上的吸附归因于氢键、金属阳离子-π 相互作用和静电相互作用。重复使用测试表明,珠子可以用 0.1M NaOH 再生。为了进行深度学习建模,在重复条件下进行了利用中心复合设计(CCD)设计的吸附实验(n=17)。CCD 框架纳入了初始 DCF 浓度、吸附剂用量和溶液 pH 等输入变量,而输出变量为 DCF 去除率。利用吸附数据,构建了一个拓扑结构为 3:7:10:1 的人工神经网络(ANN)模型,具有 3 个输入变量、第一层 7 个神经元、第二层 10 个神经元和 1 个输出变量。利用 ANN 模型数据生成了 3D 响应面图,以阐明输入变量与 DCF 去除率之间的关系。进行了额外的吸附实验来评估所开发的 ANN 模型,证实了其对 DCF 去除率的可靠预测能力。对输入变量相对重要性的分析表明,重要性的顺序如下:溶液 pH(100%)>吸附剂用量(75.2%)>初始 DCF 浓度(57.7%)。

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