• 关键词：神经网络模型  绿色合成  纳米氧化锰（MONPs）  砷  氧化

• 基金项目：国家自然科学基金（No.41977106，52174158）

• 郭书睿
• 福建师范大学环境科学与工程学院，福建 350007

• 张振军
• 福建师范大学环境科学与工程学院，福建 350007

• 蒋明琴
• 福建省产品质量检验研究院，福建 350002

• 湛雅倩
• 福建师范大学环境科学与工程学院，福建 350007

• 林加奖
• 福建师范大学环境科学与工程学院，福建 350007

• 陈祖亮
• 福建师范大学环境科学与工程学院，福建 350007

• 摘要：近年来，基于对纳米材料生物毒性和合成成本的考虑，纳米材料的绿色合成和应用在环境领域受到了广泛关注，但迄今仍面临纳米材料的绿色合成机制尚未明确及去除效率不理想两个问题.本研究选择As（III）和As（V）作为目标污染物，通过神经网络模型对纳米氧化锰（MONPs）的合成条件进行优化，发现当污染物浓度和材料投加量分别为0.1 mg?L^-1和5 mg?L^-1时，优化后的MONPs对As（III）和As（V）的去除 效率分别从43.9%、80.0%提高到90.2%、92.2%.从SEM的结果中发现优化后的材料粒径更小，根据EDS和FTIR结果，可以证明优化后材料中的Mn元素比例显著增加.另外，XRD和XPS结果则证明优化后材料从原来的Mn（II）变为Mn（IV），提高了材料的氧化能力.Zeta电位结果表明优化后材料表面的负电荷减少，进而循环伏安法结果证实了材料电子转移能力的提高，均有利于As的去除.最后，优化后MONPs在经过5次重复利用后仍具高的性能，同时对多种重金属具有一定的吸附能力.显然，基于神经网络模型绿色合成氧化锰纳米材料对砷污染修复具有 较强的针对性和实用性.

• Abstract：In recent years， the green synthesis and application of nanomaterials have received extensive attention based on the biotoxicity and cost of nanomaterials. Up to now， the green synthesis mechanism of nanomaterials is not clear and the removal efficiency is not ideal. In this study， As（III） and As（V） were selected as the target pollutants， and the synthesis conditions of manganese oxide nanoparticles （MONPs） were optimized by neural network model. It was found that when the pollutant concentration and material dosage were 0.1 mg?L^-1 and 5 mg?L^-1， the removal efficiency of As（III） and As（V） by the optimized MONPs increased from 43.9% and 80.0% to 90.2% and 92.2% respectively. The results of SEM showed that the particle size of the optimized MONPs is smaller. According to the results of EDS and FTIR， it can be proved that the proportion of Mn in the optimized MONPs was significantly increased. In addition， XRD and XPS results also showed that Mn（IV） in the optimized MONPs may improve the oxidation capacity of the material. Zeta potential results showed that the negative charge on the surface of the material is reduced after optimization， and the results of cyclic voltammetry confirm the improvement of the electron transfer ability of the material， which is beneficial to the removal of As. Finally， the optimized MONPs still have high performance after 5 times of reuse， and have certain adsorption capacity for a variety of heavy metals. Thus overall， the green synthetic MONPs based on neural network model has strong pertinence and practicability for the remediation of As pollution.

• Key words：neural network model  green synthesis  manganese oxide nanoparticles  arsenic  oxidation

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