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相关概念视频

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

433
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
433

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Updated: Jun 14, 2025

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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精炼水凝基吸附剂设计,以使用机器学习-贝叶斯优化优化来有效去除有毒金属.

Jing Zhang1, Kaixing Fu1, Dawei Wang2

  • 1State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.

Journal of hazardous materials
|September 5, 2024
PubMed
概括
此摘要是机器生成的。

机器学习优化了水凝吸附剂制造,提高了从水中去除有毒金属的效果. 这种方法显著提高了吸附能力,为净化水提供了具有成本效益和高效的解决方案.

关键词:
贝叶斯的优化是贝叶斯的优化.制造条件 制造条件水凝设计设计 凝设计机器学习是机器学习.删除金属的去除方法

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相关实验视频

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科学领域:

  • 材料科学 材料科学 材料科学
  • 环境科学 环境科学
  • 计算化学的计算化学

背景情况:

  • 水凝吸附剂有效地去除水中的有毒金属.
  • 由于复杂的参数空间,传统的优化方法是昂贵和耗时的.

研究的目的:

  • 利用机器学习 (ML) 根据制造条件预测水凝吸附剂的性能.
  • 为了优化水凝合成,以获得卓越的有毒金属吸附.

主要方法:

  • 开发了XGBoost模型来预测水凝吸附系数 (Kd).
  • 采用贝叶斯优化来确定最佳的制造参数.
  • 使用优化条件制造了十个水凝.

主要成果:

  • XGBoost模型准确地预测了Kd值.
  • 确定了影响吸附的关键因素:反应温度,时间,启动剂和交联剂度.
  • 优化的水凝显示铜 (logKd: 3.06) 和 (logKd: 3.37) 的吸附显著增强.
  • 实验验证显示了最小的预测错误 (logKd (Pb):0.025-0.172).

结论:

  • 基于ML的贝叶斯优化有效地为特定污染物量身定制水凝设计.
  • 这种方法为传统的水资源整治方法提供了实用和有效的替代方案.
  • 该研究为为环境应用开发定制水凝吸附剂提供了框架.