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Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations
[Image: see text] Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3–5 nm diamond nanoparticles synthesized through detonation process, have attrac...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288606/ https://www.ncbi.nlm.nih.gov/pubmed/37360847 http://dx.doi.org/10.1021/acsnanoscienceau.2c00055 |
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author | Kuschnerus, Inga C. Wen, Haotian Ruan, Juanfang Zeng, Xinrui Su, Chun-Jen Jeng, U-Ser Opletal, George Barnard, Amanda S. Liu, Ming Nishikawa, Masahiro Chang, Shery L. Y. |
author_facet | Kuschnerus, Inga C. Wen, Haotian Ruan, Juanfang Zeng, Xinrui Su, Chun-Jen Jeng, U-Ser Opletal, George Barnard, Amanda S. Liu, Ming Nishikawa, Masahiro Chang, Shery L. Y. |
author_sort | Kuschnerus, Inga C. |
collection | PubMed |
description | [Image: see text] Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3–5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery. |
format | Online Article Text |
id | pubmed-10288606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-102886062023-06-24 Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations Kuschnerus, Inga C. Wen, Haotian Ruan, Juanfang Zeng, Xinrui Su, Chun-Jen Jeng, U-Ser Opletal, George Barnard, Amanda S. Liu, Ming Nishikawa, Masahiro Chang, Shery L. Y. ACS Nanosci Au [Image: see text] Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3–5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery. American Chemical Society 2023-04-05 /pmc/articles/PMC10288606/ /pubmed/37360847 http://dx.doi.org/10.1021/acsnanoscienceau.2c00055 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Kuschnerus, Inga C. Wen, Haotian Ruan, Juanfang Zeng, Xinrui Su, Chun-Jen Jeng, U-Ser Opletal, George Barnard, Amanda S. Liu, Ming Nishikawa, Masahiro Chang, Shery L. Y. Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations |
title | Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations |
title_full | Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations |
title_fullStr | Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations |
title_full_unstemmed | Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations |
title_short | Complex
Dispersion of Detonation Nanodiamond Revealed
by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular
Dynamics Simulations |
title_sort | complex
dispersion of detonation nanodiamond revealed
by machine learning assisted cryo-tem and coarse-grained molecular
dynamics simulations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288606/ https://www.ncbi.nlm.nih.gov/pubmed/37360847 http://dx.doi.org/10.1021/acsnanoscienceau.2c00055 |
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