TY - RPRT A1 - Hodoroaba, Vasile-Dan A1 - Fontanges, R. A1 - Delvallée, A. A1 - Deumer, J. A1 - Salzmann, C. A1 - Crouzier, L. A1 - Gollwitzer, C. A1 - Klein, T. A1 - Koops, R. A1 - Sebaihi, N. A1 - Pauw, Brian Richard A1 - Smales, Glen Jacob A1 - Artous, S. A1 - Taché, O. A1 - Feltin, N. T1 - Report on full algorithm sequences for nanoparticle detection and size measurement as developed on both a physical basis and by machine learning T2 - Community EMPIR Project 17NRM04 nPSize (Improved traceability chain of nanoparticle size measurements) N2 - 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. KW - Nanoparticles KW - Particle size distribution KW - SEM KW - TSEM KW - TEM KW - SAXS KW - AFM PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546531 DO - https://doi.org/10.5281/zenodo.5807864 SP - 1 EP - 20 PB - Zenodo CY - Geneva AN - OPUS4-54653 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Deumer, J. A1 - Pauw, Brian Richard A1 - Marguet, S. A1 - Skroblin, D. A1 - Taché, O. A1 - Krumrey, M. A1 - Gollwitzer, C. T1 - Small-angle X-ray scattering: characterization of cubic Au nanoparticles using Debye’s scattering formula JF - Journal of Applied Crystallography N2 - A versatile software package in the form of a Python extension, named CDEF (computing Debye’s scattering formula for extraordinary form factors), is proposed to calculate approximate scattering profiles of arbitrarily shaped nanoparticles for small-angle X-ray scattering (SAXS). CDEF generates a quasi-randomly distributed point cloud in the desired particle shape and then applies the open-source software DEBYER for efficient evaluation of Debye’s scattering formula to calculate the SAXS pattern (https://github.com/j-from-b/CDEF). If self-correlation of the scattering signal is not omitted, the quasi-random distribution provides faster convergence compared with a true-random distribution of the scatterers, especially at higher momentum transfer. The usage of the software is demonstrated for the evaluation of scattering data of Au nanocubes with rounded edges, which were measured at the four-crystal monochromator beamline of PTB at the synchrotron radiation facility BESSY II in Berlin. The implementation is fast enough to run on a single desktop computer and perform model fits within minutes. The accuracy of the method was analyzed by comparison with analytically known form factors and verified with another implementation, the SPONGE, based on a similar principle with fewer approximations. Additionally, the SPONGE coupled to McSAS3 allows one to retrieve information on the uncertainty of the size distribution using a Monte Carlo uncertainty estimation algorithm. KW - X-ray scattering KW - SAXS KW - Non-spherical nanoparticles KW - Nanoparticles KW - Nanomaterials KW - Debye scattering equation KW - Simulation KW - Data fitting PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-557652 DO - https://doi.org/10.1107/S160057672200499X VL - 55 IS - Pt 4 SP - 993 EP - 1001 PB - International Union of Crystallography CY - Chester, England AN - OPUS4-55765 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -