Ma Fangfang, Su Lihao, Tang Weihao, Zhang Rongjie, Zhao Qiaojing, Chen Jingwen, Xie Hong-Bin
Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-Environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, China.
Environ Sci Technol. 2024 Dec 17;58(50):22278-22287. doi: 10.1021/acs.est.4c06578. Epub 2024 Dec 5.
The sulfuric acid (SA)-amine nucleation mechanism gained increasing attention due to its important role in atmospheric secondary particle formation. However, the intrinsic enhancing potential (IEP) of various amines remains largely unknown, restraining the assessment on the role of the SA-amines mechanism at various locations. Herein, machine learning (ML) models were constructed for high-throughput prediction of IEP of amines, and the nucleation mechanism of specific amines with high IEP was investigated. The formation free energy (Δ) of SA-amines dimer clusters, a key parameter for assessing IEP, was calculated for 58 amines. Based on the calculated Δ values, seven ML models were constructed and the best one was further utilized to predict the Δ values of the remaining 153 amines. Diethylamine (DEA), mainly emitted from ethanol gasoline vehicles, was found to be one of the amines with the highest IEP for SA-driven nucleation. By studying larger SA-DEA clusters, it was found that the nucleation rate of DEA with SA is 3-7 times higher than that of dimethylamine, a well-known key base for SA-driven nucleation. The study provides a powerful tool for evaluating the actual role of amines on SA-driven nucleation and revealed that the mechanism could be particularly important in areas where ethanol gasoline vehicles are widely used.
硫酸（SA）-胺成核机制因其在大气二次颗粒物形成中的重要作用而受到越来越多的关注。然而，各种胺的内在增强潜力（IEP）在很大程度上仍然未知，这限制了对SA-胺机制在不同地点所起作用的评估。在此，构建了机器学习（ML）模型用于高通量预测胺的IEP，并研究了具有高IEP的特定胺的成核机制。计算了58种胺的SA-胺二聚体簇的形成自由能（Δ），这是评估IEP的关键参数。基于计算得到的Δ值，构建了七个ML模型，并进一步利用最佳模型预测其余153种胺的Δ值。发现主要由乙醇汽油车辆排放的二乙胺（DEA）是SA驱动成核中IEP最高的胺之一。通过研究更大的SA-DEA簇，发现DEA与SA的成核速率比二甲胺（SA驱动成核的著名关键碱）高3至7倍。该研究为评估胺对SA驱动成核的实际作用提供了有力工具，并表明该机制在乙醇汽油车辆广泛使用的地区可能特别重要。
