Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning DOI
Yi-Rong Liu, Yan Jiang

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Experimental and theoretical studies on the compositional changes of new particle formation in nucleation initial growth stages acid–base systems (2 5 nm) are extremely challenging. This study proposes a machine learning method for predicting composition change sulfuric acid-dimethylamine system transformation from monomer to nanoparticle by structure information small-sized acid (SA)–dimethylamine (DMA) molecular clusters. Based this components, we found that was mainly through alternate adsorption (SA)1(DMA)1, (SA)1(DMA)2, (SA)1 clusters at early stage nucleation, which accounted about 70, 20, 10%, respectively. can explain nature possible cluster acidity during system. also predict base-stabilization mechanism without relying any experimental data, thereby yielding results consistent with those previous measurement.

Language: Английский

Predicting Composition Evolution for a Sulfuric Acid-Dimethylamine System from Monomer to Nanoparticle Using Machine Learning DOI
Yi-Rong Liu, Yan Jiang

The Journal of Physical Chemistry A, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 25, 2024

Experimental and theoretical studies on the compositional changes of new particle formation in nucleation initial growth stages acid–base systems (2 5 nm) are extremely challenging. This study proposes a machine learning method for predicting composition change sulfuric acid-dimethylamine system transformation from monomer to nanoparticle by structure information small-sized acid (SA)–dimethylamine (DMA) molecular clusters. Based this components, we found that was mainly through alternate adsorption (SA)1(DMA)1, (SA)1(DMA)2, (SA)1 clusters at early stage nucleation, which accounted about 70, 20, 10%, respectively. can explain nature possible cluster acidity during system. also predict base-stabilization mechanism without relying any experimental data, thereby yielding results consistent with those previous measurement.

Language: Английский

Citations

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