Djup multi-skala modellering av pH-effekt på upplösningen av metalloxid nanopartiklar
Tidsperiod: 2020-01-01 till 2023-12-31
Projektledare: Chao Zhang
Bidragstyp: Bidrag för anställning eller stipendier
Budget: 3 360 000 SEK
Several nanosafety projects have been funded by the EU (e.g. EU Nanosafety Cluster and SmartNanoTox), however, the standardized process to assess and to predict the toxicity of metal oxide nanoparticles (NPs) still missing. One of the bottlenecks is to build a predictive model that can cover key features of nanoparticles in physiological conditions.The dissolution of ZnO NPs, as the most toxic one among many other metal oxides, is considered as the major pathway regarding the toxicity. Therefore, the purpose and aim of this 4-year project is to develop a deep learning empowered multi-scale model to answer one central scientific question: What is the microscopic origin of the pH effect in the dissolution of ZnO NPs ?The project consists of three interconnected sub-projects by combining finite-field density functional theory based molecular dynamics simulations we developed, the deep learning code for molecules and materials we are developing and free energy calculations that we have experience with. Predictions from the multi-scale model will be compared to experiments from our collaborator.The outcomes of this project will not only provide new mechanistic insights of ZnO NPs dissolution but will also suggest rational design principles to control the stability. Moreover, the deep learning code we are developing will benefit materials modeling community in Sweden and open the door for a number of applications in other fields.