Department of Environmental Health Engineering, School of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Environmental Health Engineering, Student Research Committee, Faculty of Public Health and safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Carbohydr Polym. 2019 Feb 15;206:844-853. doi: 10.1016/j.carbpol.2018.11.048. Epub 2018 Nov 19.
Recovery of crystal violet (CV) dye was investigated using magnetic chitosan nano-composites (MCNCs) and the effects of process variables (contact time, initial CV concentration, adsorbent dose, and pH) were optimized through response surface methodology. The reliability of the RSM models (first-order model, first model with interaction, the second-order model, and reduced model) was tested by fitting the data. A comparative analysis of the results derived from the models demonstrated that the reduced model was the best. According to modelling results, MCNCs dosage and contact time were found to be the most effective variables on the adsorption efficiency procedure, respectively. Also, pH had no significant effect on the adsorption uptake statistically. MCNC has the maximum adsorption efficiency (72%) when the contact time, adsorbent dosage, and initial concentration of CV were optimally set as 140 min, 1 g, and 77 mg/L, respectively. Quantity uptake of CV was evaluated using the Langmuir, Freundlich, and Temkin models. Based on findings, Freundlich isotherm fitted well with the experimental results. Kinetic studies showed that the pseudo-first-order model fitted the best the experimental data, which indicated that the adsorption rate of CV molecules onto MCNC was time-dependent. The studies on the well regenerability of MCNC in addition to its high potentiality of cationic dyes removal make it an attractive adsorbent in terms of dye-containing wastewaters treatment.
采用磁性壳聚糖纳米复合材料（MCNC）研究了结晶紫（CV）染料的回收，并通过响应面法优化了工艺变量（接触时间、初始 CV 浓度、吸附剂用量和 pH）。通过拟合数据测试了 RSM 模型（一级模型、带交互作用的一级模型、二级模型和简化模型）的可靠性。对模型得出的结果进行比较分析表明，简化模型是最佳模型。根据建模结果，发现 MCNC 用量和接触时间是影响吸附效率过程的最有效变量。此外，pH 对吸附量的影响在统计学上没有显著影响。当接触时间、吸附剂用量和 CV 的初始浓度最佳设置为 140 min、1 g 和 77 mg/L 时，MCNC 的最大吸附效率（72%）。使用 Langmuir、Freundlich 和 Temkin 模型评估了 CV 的吸附量。研究结果表明，Freundlich 等温线与实验结果拟合良好。动力学研究表明，准一级模型最能拟合实验数据，这表明 CV 分子在 MCNC 上的吸附速率是时间依赖性的。MCNC 具有良好的再生性能和去除阳离子染料的高潜力，这使其在处理含染料废水方面具有吸引力。
