DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Byoungsang | ko |
dc.contributor.author | Yoon, Seokyoung | ko |
dc.contributor.author | Lee, Jin Woong | ko |
dc.contributor.author | Kim, Yunchul | ko |
dc.contributor.author | Chang, Junhyuck | ko |
dc.contributor.author | Yun, Jaesub | ko |
dc.contributor.author | Ro, Jae Chul | ko |
dc.contributor.author | Lee, Jong-Seok | ko |
dc.contributor.author | Lee, Jung Heon | ko |
dc.date.accessioned | 2024-09-06T03:00:06Z | - |
dc.date.available | 2024-09-06T03:00:06Z | - |
dc.date.created | 2024-09-04 | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | ACS NANO, v.14, no.12, pp.17125 - 17133 | - |
dc.identifier.issn | 1936-0851 | - |
dc.identifier.uri | http://hdl.handle.net/10203/322772 | - |
dc.description.abstract | Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis. | - |
dc.language | English | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis | - |
dc.type | Article | - |
dc.identifier.wosid | 000603308800073 | - |
dc.identifier.scopusid | 2-s2.0-85097866361 | - |
dc.type.rims | ART | - |
dc.citation.volume | 14 | - |
dc.citation.issue | 12 | - |
dc.citation.beginningpage | 17125 | - |
dc.citation.endingpage | 17133 | - |
dc.citation.publicationname | ACS NANO | - |
dc.identifier.doi | 10.1021/acsnano.0c06809 | - |
dc.contributor.localauthor | Lee, Jong-Seok | - |
dc.contributor.nonIdAuthor | Lee, Byoungsang | - |
dc.contributor.nonIdAuthor | Yoon, Seokyoung | - |
dc.contributor.nonIdAuthor | Lee, Jin Woong | - |
dc.contributor.nonIdAuthor | Kim, Yunchul | - |
dc.contributor.nonIdAuthor | Chang, Junhyuck | - |
dc.contributor.nonIdAuthor | Yun, Jaesub | - |
dc.contributor.nonIdAuthor | Ro, Jae Chul | - |
dc.contributor.nonIdAuthor | Lee, Jung Heon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | transmission electron microscope (TEM) | - |
dc.subject.keywordAuthor | image analysis | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | morphological properties | - |
dc.subject.keywordAuthor | statistics | - |
dc.subject.keywordAuthor | big data | - |
dc.subject.keywordPlus | HIGH-THROUGHPUT | - |
dc.subject.keywordPlus | GOLD NANOPARTICLES | - |
dc.subject.keywordPlus | SIZE DISTRIBUTION | - |
dc.subject.keywordPlus | PARTICLE-SIZE | - |
dc.subject.keywordPlus | SHAPE CONTROL | - |
dc.subject.keywordPlus | SCATTERING | - |
dc.subject.keywordPlus | NANORODS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
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