{"title":"A Hybrid Particle Swarm Optimization-Tuning Algorithm for the Prediction of Nanoparticle Morphology from Microscopic Images","authors":"Abhishek Singh, T. Thajudeen","doi":"10.4209/aaqr.220453","DOIUrl":null,"url":null,"abstract":"Aggregated nanoparticle structures are quite ubiquitous in aerosol and colloidal science, specifically in nanoparticle synthesis systems such as combustion processes where coagulation results in the formation of fractal-like structures. In addition to their size, morphology of the particles also plays a key role in defining various physicochemical properties. Electron microscopy based images are the most commonly used tools in visualizing these aggregates, and prediction of the 3-dimensional structures from the microscopic images is quite complex. Typically, 2-dimensional features from the images are compared to available structures in a database or regression equations are used to predict 3-dimensional morphological parameters including fractal dimension and pre-exponential factor. In this study, we propose a combination of evolutionary algorithm and forward tuning model to predict the best fit 3-dimensional structures of aggregates from their projection images. 2-dimensional features from a projection image are compared to the candidate projections generated using FracVAL code and optimized using Particle Swarm Optimization to obtain the 3-dimensional structure of the aggregate. Various 3-dimensional properties including hydrodynamic diameter and mobility diameter of the retrieved structures are then compared with the properties of the aggregate used to form the candidate projection image, to test the suitability of the algorithm. Results show that the hybrid algorithm can closely predict the 3-dimensional structures from the projection images with less than 10% difference in the predicted 3-dimensional properties including mobility diameter and radius of gyration.","PeriodicalId":7402,"journal":{"name":"Aerosol and Air Quality Research","volume":"1 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerosol and Air Quality Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.4209/aaqr.220453","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 1
Abstract
Aggregated nanoparticle structures are quite ubiquitous in aerosol and colloidal science, specifically in nanoparticle synthesis systems such as combustion processes where coagulation results in the formation of fractal-like structures. In addition to their size, morphology of the particles also plays a key role in defining various physicochemical properties. Electron microscopy based images are the most commonly used tools in visualizing these aggregates, and prediction of the 3-dimensional structures from the microscopic images is quite complex. Typically, 2-dimensional features from the images are compared to available structures in a database or regression equations are used to predict 3-dimensional morphological parameters including fractal dimension and pre-exponential factor. In this study, we propose a combination of evolutionary algorithm and forward tuning model to predict the best fit 3-dimensional structures of aggregates from their projection images. 2-dimensional features from a projection image are compared to the candidate projections generated using FracVAL code and optimized using Particle Swarm Optimization to obtain the 3-dimensional structure of the aggregate. Various 3-dimensional properties including hydrodynamic diameter and mobility diameter of the retrieved structures are then compared with the properties of the aggregate used to form the candidate projection image, to test the suitability of the algorithm. Results show that the hybrid algorithm can closely predict the 3-dimensional structures from the projection images with less than 10% difference in the predicted 3-dimensional properties including mobility diameter and radius of gyration.
期刊介绍:
The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues. It encompasses a multi-disciplinary field, including:
- Aerosol, air quality, atmospheric chemistry and global change;
- Air toxics (hazardous air pollutants (HAPs), persistent organic pollutants (POPs)) - Sources, control, transport and fate, human exposure;
- Nanoparticle and nanotechnology;
- Sources, combustion, thermal decomposition, emission, properties, behavior, formation, transport, deposition, measurement and analysis;
- Effects on the environments;
- Air quality and human health;
- Bioaerosols;
- Indoor air quality;
- Energy and air pollution;
- Pollution control technologies;
- Invention and improvement of sampling instruments and technologies;
- Optical/radiative properties and remote sensing;
- Carbon dioxide emission, capture, storage and utilization; novel methods for the reduction of carbon dioxide emission;
- Other topics related to aerosol and air quality.