相关实验视频

Updated: Jul 29, 2026

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
10:31

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Published on: December 6, 2015

使用生物炭特性预测重金属吸附效率:对整体机器学习模型进行比较分析.

Zaher Mundher Yaseen1, Farah Loui Alhalimi2

  • 1Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia. z.yaseen@kfupm.edu.sa.

Scientific reports
|April 18, 2025
PubMed
概括

机器学习模型准确地预测使用生物炭的重金属去除效率. XGBoost模型显示出卓越的性能,确定了初始金属度和pH值作为有效环境修复的关键因素.

关键词:
生物炭的特性 生物炭的特性组合学习学习 组合学习环境应用 环境应用重金属吸附重金属的吸附重金属的吸附重金属的吸附重金属的吸附.预测建模的预测建模.

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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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...
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