Bridging molecular-scale interfacial science with continuum-scale models

Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real...

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Veröffentlicht in:Nature communications 2024-06, Vol.15 (1), p.5326-14, Article 5326
Hauptverfasser: Ilgen, Anastasia G., Borguet, Eric, Geiger, Franz M., Gibbs, Julianne M., Grassian, Vicki H., Jun, Young-Shin, Kabengi, Nadine, Kubicki, James D.
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Sprache:eng
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Zusammenfassung:Solid–water interfaces are crucial for clean water, conventional and renewable energy, and effective nuclear waste management. However, reflecting the complexity of reactive interfaces in continuum-scale models is a challenge, leading to oversimplified representations that often fail to predict real-world behavior. This is because these models use fixed parameters derived by averaging across a wide physicochemical range observed at the molecular scale. Recent studies have revealed the stochastic nature of molecular-level surface sites that define a variety of reaction mechanisms, rates, and products even across a single surface. To bridge the molecular knowledge and predictive continuum-scale models, we propose to represent surface properties with probability distributions rather than with discrete constant values derived by averaging across a heterogeneous surface. This conceptual shift in continuum-scale modeling requires exponentially rising computational power. By incorporating our molecular-scale understanding of solid–water interfaces into continuum-scale models we can pave the way for next generation critical technologies and novel environmental solutions. Chemistry at solid-water interfaces is crucial for all aspects of human life. Here, authors propose to use a probability-based paradigm for formalizing chemical reactions at solid-water interfaces in continuum scale models.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-49598-y