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Updated: Jan 17, 2026

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

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基于机器学习模型,通过泡分离从污染水中去除PFAS的动态分析

Xin Liu1, Yanyan Liang1, Libin Yang1

  • 1State Key Laboratory of Water Pollution Control and Green Resource Recycling, Tongji University, Shanghai, 200092, China.

Environmental research
|September 24, 2025
PubMed
概括

机器学习,特别是XGBoost,优化了泡分离,以去除持久的per-和多醇基物质 (PFAS). 该PFAS-XGB模型确定了空气化时间和PFAS属性等关键因素,以实现有效的环境修复.

关键词:
动态分析 动态分析泡分离器中的泡分离器机器学习 机器学习在PFAS中,我们可以使用PFAS.移除效率 移除效率 移除效率 移除效率

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