结合塑料和氧化物颗粒的尺寸分布和形状来评估物理化学相互作用:聚合和附着
Hyojeong Nam1, Allan Gomez-Flores1, Hyunjung Kim1
1Department of Earth Resources and Environmental Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea.
Journal of hazardous materials
|February 1, 2025
概括
粒子大小分布和形状显著影响聚合和附着相互作用. 结合这些因素的机器学习模型可以改善预测,提供超越传统方法的新见解.
科学领域:
- 合体和表面科学科学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 物理化学理论经常将粒子简化为平均直径,忽视尺寸分布和形状.
- 这种简化忽略了影响粒子聚合和附着行为的关键因素.
- 准确的粒子相互作用建模对于理解材料特性和过程至关重要.
研究的目的:
- 将粒子大小分布和形状整合到物理化学相互作用模型中.
- 利用机器学习 (ML) 来研究控制粒子聚合和附着的参数.
- 为考虑多分散性的粒子相互作用开发一个预测框架.
主要方法:
- 使用球体,体和圆柱体的相互作用数据训练了一个深度神经网络.
- 测量了粒子大小分布,并纳入了ML模型,以预测相互作用概况.
- 实验验证包括使用球形颗粒的稳定性和聚合性研究和使用颗粒的附着性研究.
主要成果:
- 粒子大小分布为物理化学相互作用创造了一个"活跃区域",这种区域在传统的平均直径模型中不存在.
- 包含大小分布可以提高对有利和不利的聚合和附着的估计.
- 增加的粒子大小分布 (更高的多分散性) 会扩大这些相互作用区域.
结论:
- 开发的ML方法准确地模拟了球形,圆形和子弹形粒子的物理化学相互作用.
- 粒子大小分布是一个关键参数,必须考虑准确的相互作用能量估计.
- 对于碎片状颗粒,如微塑料,注意到该模型的局限性.
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