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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.
Polymeric core-shell particles were synthesized in a semi-batch emulsion polymerization process. The shell of the particles consist of PVDF with a high amount of beta-phase. Small-angle X-ray scattering (SAXS) was used to quantify the size of the cores of the particles and the thickness of the shell.
Small-angle scattering (SAS) offers a reliable route to characterize the nanostructure of large amounts of material with a minimum of tedium, for example, easily extracting size distributions and volume fractions. There are a variety of analysis programs available while the evaluation of SAS measurements has been dominated by the classical curve fitting approach. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for SAS data analysis and has been available and used for many years. The latest developments will be presented and a scattering function of a mass fractal model of branched polymers in solution is provided as an example for implementing a plug-in.
Alternatively to classical curve fitting, part two presents the latest developments of the user-friendly open-source Monte Carlo regression package McSAS. The form-free Monte Carlo nature of McSAS means, it is not necessary to provide further restrictions on the mathematical form of the parameter distribution: without prior knowledge, McSAS is able to extract complex multimodal or odd- shaped parameter distributions from SAS data. The headless mode is presented by an example of operation within interactive programming environments such as a Jupyter notebook.
Small-angle X-ray scattering (SAXS) can be used for structural de- termination of biological macromolecules and polymers in their na- tive states. To improve the reliability of such experiments, the re- duction of radiation damage occurring from exposure to X-rays is needed.One method, is the use of scavenger molecules that protect macromolecules against radicals produced by radiation exposure.In this study we investigate the feasibility to apply the compatible solute, osmolyte and radiation protector Ectoine (THP(B)) as a scavenger throughout SAXS measurements of single-stranded DNA-binding protein Gene-V Protein (G5P/GVP). Therefore we monitor the radiation induced changes of G5P during bio-SAXS. The resulting microscopic energy-damage relation was determined by particle scattering simu- lations with TOPAS/Geant4. The results are interpreted in terms of radical scavenging as well as post-irradiation effects, related to preferential-exclusion from the protein surface. Thus, Ectoine provides an non-disturbing way to improve structure-determination of proteins via bio-SAXS in future studies.
Showcasing research from the Federal Institute for Material Research and Testing Berlin and Fraunhofer Institute for Celltherapy and Immunology Branch Bioanalytics and Bioprocesses Potsdam.
Bio-SAXS of single-stranded DNA-binding proteins: Radiation protection by the compatible solute ectoine.
We aimed to increase the possible undisturbed exposure time during bio-SAXS measurements of single-stranded DNA-binding proteins. Therefore small angle X-ray scattering was performed on Gene-V Protein (G5P/GVP), which is involved in DNA repair processes. To achieve this, irradiations were performed in presence and absence of the hydroxyl-radical scavenger and osmolyte Ectoine, which showed efficient radiation protection and prevented protein aggregation, thus allows for a non-disturbing way to improve structure-determination of biomolecules.
The progress in the VAMAS Project #15" Measurement of particle size and shape distribution of bipyramidal titania including deposition from liquid suspension" within TWA 34 Nanoparticle Populations is presented with highlight of the following points:
- Determine and compare particle size and shape distribution by means of:
• electron microscopy (SEM, TEM, STEM-in-SEM)
• atomic force microscopy (AFM)
• small angle X-ray scattering (SAXS)
- Determine uncertainty induced by deposition protocol from liquid suspension with comparison to known values from a prior ILC with already deposited nanoparticles on TEM grids.
- Provide comparative validation of protocols for the techniques other than TEM.
Nachruf auf Wilhelm Ruland
(2021)
Mit Prof. Dr. Wilhelm Ruland starb am 3. Februar 2021 einer der letzten großen Wissenschaftler, welche die Streutheorie nach dem Zweiten Weltkrieg vorangetrieben haben. Sein zentrales Thema war die Streuung an weicher Materie. Hier lieferte er bis ins hohe Alter grundlegende Beiträge, die den Stellenwert seines Leitspruchs demonstrieren: Nichts ist praktischer als eine gute Theorie.
The main outcomes of the EMPIR project nPSize are presented and the suitability of the new capabilities, e.g. reference materials, sample preparation protocols, measurement procedures, and data analysis, to be standardized and implemented in accredited analytical laboratories is discussed. Complementation and/or filling gaps of published and ongoing standardisation projects on size, shape and number concentration measurements under ISO/TC 229/JWG 2 are offered.
The two VAMAS inter-laboratory comparisons resulted from the nSPize project and just started under TWA 34 Nanoparticle Populations (Projects #15 and #16) of bipyramidal TiO2 anatase and bimodal SiO2 nanoparticles are presented in detail.
In order to assess new nanomaterials and nanoparticles for potential risks to health and the environment, they need to be well-characterised. The measurement of constituent nanoparticle size, shape, and size distribution are important factors for the risk evaluation process.
EMPIR project Improved traceability chain of nanoparticle size measurements (17NRM04, nPSize) is working to assess a range of traceable nanoparticle measurement approaches, including Electron Microscopy (SEM, TEM, STEM-in-SEM), Atomic Force Microscopy and Small Angle X-ray Scattering, and deliver improved calibration methods to users. For the techniques under investigation, physical models of their response to a range of nanoparticle types are developed. Validated reference materials are also used for inter-comparisons of measurement systems, with an evaluation of the associated measurement uncertainty. With project contributions to standards development work, manufacturers will be better placed to assess the human and environmental risks posed by nanomaterials across a whole range of products.
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.