Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Dokumenttyp
- Forschungsbericht (3) (entfernen)
Sprache
- Englisch (3)
Referierte Publikation
- nein (3)
Schlagworte
- Particle size distribution (3) (entfernen)
Organisationseinheit der BAM
One aim of the EMPIR nPSize project 17NRM04 was to develop and validate three classes of candidate reference (test) materials (RTMs), with i) well-defined non-spherical shape, ii) relatively high polydispersity index, and iii) accurate particle concentrations.
To fulfil the requirements of the project, 11 different types of materials were prepared. Following the initial assessment of the materials suitability, nPSize5_PT_UNITO, nPSize6_AC_UNITO and nPSize7_GN_CEA materials were found unsuitable for the project, due to various reasons. PT material was deemed unsuitable due to its predominantly agglomerated nature. AC material contained relatively high amount of impurities (other particle forms). GN material was found too heterogeneous in both the length and width for the purpose of the project. The remaining 8 candidate RTMs were assessed for their homogeneity and stability and used for successful delivery of the associated activities within the nPSize project.
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 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.
In this research project, a new OECD Test Guideline (TG) for the determination of “Particle Size and Particle Size Distributions of Nanomaterials” was developed as the existing OECD TG 110 is considered to be outdated in terms of applicable size range (not covering sizes <200 nm) and methods. By its scope with an applicable size range from 1 to 1000 nm the new Test Guideline (TG PSD) covers the whole nanoscale. The TG PSD is applicable for particulate and fibrous nanomaterials. The prescribed, pairwise measurement of fibre diameter and length in the TG PSD allows for the first time to differen-tiate fibres with regard to their size-dependent hazard properties. Measurement instructions for each included method were validated within two separated interlaboratory comparisons, as a distinction between near spherical particles and fibres when applying the methods has to be made.
Besides information on content and structure of the TG PSD, this final report outlines essential steps, considerations and organisational aspects during the development of the TG. Insights into the selec-tion, preparation and prevalidation of test materials used in the interlaboratory comparison are given. Finally, main results of the interlaboratory comparisons and their impacts on the TG PSD are pre-sented.