使用机器学习模型设计黄金提取溶剂的设计
Takuto Tsunemi1, Tatsuya Oshima2, Hiroki Yokota2
1Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
ACS omega
|February 9, 2026
概括
研究人员开发了预测模型,以寻找用于金矿开采的新溶剂. 这些模型确定了 (-) -fenchone和4-phenyl-2-butanone作为Dibutyl carbitol (DBC) 的有效替代品,具有较低的水溶性,减少水污染.
科学领域:
- * 计算化学和材料科学.
- *专注于溶剂提取和化学过程优化.
背景情况:
- *二甲基碳醇 (DBC) 是化后提取黄金 (Au-III) 的常规溶剂,但其高水溶性导致水相污染.
- * 需要有效的黄金提取溶剂,对环境产生最小的影响,在金炼中至关重要.
研究的目的:
- * 根据分子描述器开发黄金提取性和溶剂水溶性的预测模型.
- * 识别具有高金提取效率和低水溶性的新型溶剂,克服DBC的局限性.
主要方法:
- *使用分子描述器 (COSMO-RS电荷密度配置文件) 和实验数据构建定量结构-属性关系 (QSPR) 模型.
- *在预测模型中应用基于遗传算法的波长选择来进行变量选择.
- *对已建立的模型进行反向分析,以选最佳溶剂候选物.
主要成果:
- * 预测黄金提取性和水溶性模型的准确性很高 (相关系数>0.9).
- * (-) -芬和4--2-butanone被确定为有前途的溶剂.
- *这些新型溶剂具有与DBC相似的黄金提取能力,但水溶性明显较低.
结论:
- *预测建模是设计高效,环保的黄金开采溶剂的强大工具.
- * (-) -芬和4--2-butanone是DBC的可行的替代品,可减少水污染.
- * 该研究为在水力金应用中发现新溶剂提供了框架.
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