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Report on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis and by machine learning

  • he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning. Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method. Machine Learning modelling: The aim of this part is to use machine learning to enable automatiche main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning. Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method. Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task. The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms.zeige mehrzeige weniger

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Autor*innen:Vasile-Dan HodoroabaORCiD, R. Fontanges, A. Delvallée, J. Deumer, C. Salzmann, L. Crouzier, C. Gollwitzer, T. Klein, R. Koops, N. Sebaihi, Brian Richard PauwORCiD, Glen Jacob SmalesORCiD, S. Artous, O. Taché, N. Feltin
Dokumenttyp:Forschungsbericht
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Community EMPIR Project 17NRM04 nPSize (Improved traceability chain of nanoparticle size measurements)
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:6 Materialchemie
6 Materialchemie / 6.1 Oberflächen- und Dünnschichtanalyse
6 Materialchemie / 6.5 Synthese und Streuverfahren nanostrukturierter Materialien
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Zenodo
Verlagsort:Geneva
Erste Seite:1
Letzte Seite:20
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:AFM; Nanoparticles; Particle size distribution; SAXS; SEM; TEM; TSEM
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Nano
DOI:10.5281/zenodo.5807864
URN:urn:nbn:de:kobv:b43-546531
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:14.04.2022
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:BAM Forschungsberichte ohne Nummerierung
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