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Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM
The objective of this study is to optimize the process parameters for preparing polystyrene (PS) PM2.5 particles by supercritical antisolvent (SAS) method. Toluene was selected as the solvent and supercritical carbon dioxide (SC-CO(2)) was used as the antisolvent. The Box–Behnken design-response sur...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341738/ https://www.ncbi.nlm.nih.gov/pubmed/32636462 http://dx.doi.org/10.1038/s41598-020-67994-4 |
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author | Zhang, Zhuo Li, Qingling Guo, Bo Zhang, Shouzhong Zhang, Sen Hu, Dedong |
author_facet | Zhang, Zhuo Li, Qingling Guo, Bo Zhang, Shouzhong Zhang, Sen Hu, Dedong |
author_sort | Zhang, Zhuo |
collection | PubMed |
description | The objective of this study is to optimize the process parameters for preparing polystyrene (PS) PM2.5 particles by supercritical antisolvent (SAS) method. Toluene was selected as the solvent and supercritical carbon dioxide (SC-CO(2)) was used as the antisolvent. The Box–Behnken design-response surface method was applied to investigate the effect of crystallizer pressure, PS massic concentration, flow ratio of CO(2)/solution and crystallizer temperature on the size and the distribution of PS particles, systematically. It is found that crystallizer temperature is the most significant variable on the size and the distribution of PS particles, followed by flow ratio of CO(2)/solution and PS massic concentration, and crystallizer pressure is the slightest significant factor. The particle size increases with the increase of crystallizer temperature. The optimum conditions are obtained as crystallizer pressure 9.8 MPa, PS massic concentration 1.6 wt%, flow ratio of CO(2)/solution 140 g/g and crystallizer temperature 309 K. Under these conditions, the PS particle with the size of 2.78 μm and a narrow size distribution has been prepared, meeting PM2.5 standard aerosols. The results suggest that it is feasible to produce PM2.5 standard aerosols by SAS. |
format | Online Article Text |
id | pubmed-7341738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73417382020-07-09 Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM Zhang, Zhuo Li, Qingling Guo, Bo Zhang, Shouzhong Zhang, Sen Hu, Dedong Sci Rep Article The objective of this study is to optimize the process parameters for preparing polystyrene (PS) PM2.5 particles by supercritical antisolvent (SAS) method. Toluene was selected as the solvent and supercritical carbon dioxide (SC-CO(2)) was used as the antisolvent. The Box–Behnken design-response surface method was applied to investigate the effect of crystallizer pressure, PS massic concentration, flow ratio of CO(2)/solution and crystallizer temperature on the size and the distribution of PS particles, systematically. It is found that crystallizer temperature is the most significant variable on the size and the distribution of PS particles, followed by flow ratio of CO(2)/solution and PS massic concentration, and crystallizer pressure is the slightest significant factor. The particle size increases with the increase of crystallizer temperature. The optimum conditions are obtained as crystallizer pressure 9.8 MPa, PS massic concentration 1.6 wt%, flow ratio of CO(2)/solution 140 g/g and crystallizer temperature 309 K. Under these conditions, the PS particle with the size of 2.78 μm and a narrow size distribution has been prepared, meeting PM2.5 standard aerosols. The results suggest that it is feasible to produce PM2.5 standard aerosols by SAS. Nature Publishing Group UK 2020-07-07 /pmc/articles/PMC7341738/ /pubmed/32636462 http://dx.doi.org/10.1038/s41598-020-67994-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhang, Zhuo Li, Qingling Guo, Bo Zhang, Shouzhong Zhang, Sen Hu, Dedong Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title | Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title_full | Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title_fullStr | Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title_full_unstemmed | Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title_short | Optimization of process parameters for preparation of polystyrene PM2.5 particles by supercritical antisolvent method using BBD-RSM |
title_sort | optimization of process parameters for preparation of polystyrene pm2.5 particles by supercritical antisolvent method using bbd-rsm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341738/ https://www.ncbi.nlm.nih.gov/pubmed/32636462 http://dx.doi.org/10.1038/s41598-020-67994-4 |
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