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新型富含的凝吸附剂,用于从废水中选择性提取稀土元素

Xuyi Wei¹, Xiaohui Mao², Junwei Han³

¹School of Minerals Processing & Bioengineering, Central South University, Changsha 410083, China.

Journal of hazardous materials

|September 2, 2024

概括

一种新的富含的水凝吸附剂PEI-ALG@KLN,有效地从废水中回收稀土元素 (REE). 这种吸附剂具有很高的选择性和稳定性,在REE提取中具有重要的工业应用潜力.

科学领域:

材料科学材料科学材料科学
环境化学环境化学
化学工程是化学工程的重要组成部分.

背景情况:

稀土元素的有效回收对于可持续的资源管理和环境保护至关重要.
工业过程中的废水往往含有有价值的可再生能源,需要有效的清除和回收方法.
开发选择性吸附剂是将可再生能源从复杂的废物流中分离出来的关键.

研究的目的:

为了合成和表征一种新的富含的凝吸附剂 (PEI-ALG@KLN) 用于选择性REE回收.
调查PEI-ALG@KLN吸附剂对特定REEs的吸附机制和吸附能力.
用机器学习评估吸附剂在真实废水中的性能,并评估各种因素对吸附的影响.

主要方法:

通过多电解质相互作用和希夫基反应合成PEI-ALG@KLN水凝.
吸附剂的结构和性能的表征.
吸附实验以确定REEs (Ho,Nd,Dy) 和Fe的容量和选择性.
吸附-脱吸周期测试用于稳定性评估.
机器学习 (随机森林) 用于预测吸附剂性能和识别关键因素 (pH,时间,度,电子阴性,离子半径).

主要成果:

PEI-ALG@KLN对Ho (155 mg/g) 和Nd (125 mg/g) 具有很高的选择性吸附能力.
吸附归因于REEs和的功能组之间的协调和静电相互作用.
吸附剂在五个循环后保持了超过85%的容量,显示出良好的稳定性.
在NdFeB磁铁废水中,Nd和Dy回收超过93%,低Fe回收 (5.04%).
随机森林模型准确地预测了性能,确定pH,时间和初始度作为重要因素.

结论:

PEI-ALG@KLN是一种高效和有选择性的吸附剂,用于从废水中回收REEs.
吸附剂表现出极好的稳定性和选择性,特别是在复杂的工业废水中的Nd和Dy.
机器学习分析为吸附机制和优化参数提供了宝贵的见解.
PEI-ALG@KLN显示了在REEs的选择性开采中实际应用的巨大潜力.

关键词:

吸附技术是一种吸附技术.机器学习模型的机器学习模型富含的水凝含有丰富的气.稀土元素稀土元素是一种稀土元素.污水处理污水处理污水处理

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