Work Package 3

Characterisation and validation of the particle material parameters density and refractive index

The aim of this WP is to develop traceable measurement methods for characterisation of the particle material parameters, particle density and refractive index (RI), to validate the modelling of such material parameters in the measurement process and to improve the mathematical models so that they are applicable to industry‑relevant particles and particle‑based pollutants (Obj. 3 and 4). Therefore, measurement procedures based on a combination of complementary methods, such as CLS, centrifugal FFF (CF3), contrast‑variation SAXS, TGA and XRD will be employed to measure the particle density. Likewise, particle‑by‑particle methods such as FCM and spLS, ensemble methods, such as backscattering interferometry, and in‑between methods such as FFF‑RI‑MALS will be used to measure the particle RI. Current measurement capabilities available at the participating laboratories will be further developed to establish measurement traceability by creating uncertainty budgets. The density and RI of the industry‑relevant RTMs developed in WP1 will be measured.

WP3 will provide input to WP1, WP2 and WP4. WP1 will use the outcome of WP3 to assign reference values of the density and RI to the RTMs and WP2 will use these values as input for the protocol for the VAMAS ILC. WP4 will use the information on the procedures for density and RI characterisation to generate a roadmap for generating traceability and standardisation of particle RI and density measurements.

Hand interacting with a digital tablet displaying security and payment icons.

Tasks under WP3

Task 3.1

In this task, measurement methods for the particle density will be developed beyond the current state‑of‑the‑art. These methods are based on i) CLS, ii) CF3, iii) the synergistic use of SAXS, CLS and TGA, iv) the synergistic use of SAXS and FCCS, and v) correlating XRD, XRF and NEXAFS. These methods will be used to measure the density of the RTMs. Therefore, an internal ILC will be organised to measure the RTMs developed in WP 1. For this internal ILC, stakeholders will also receive an invitation to participate. The participants will determine the measurement uncertainty of their techniques. The results will be used to draft guidelines on particle density measurements of industry relevant micro‑ and nanoparticles.

Task 3.2

In this task, novel measurement methods for the RI will be developed. These methods are based on i) FCM and spLS for single particle RI determination, ii) backscattering interferometry and RI matching for ensemble RI determination, and iii) FFF‑RI‑MALS for particle size‑resolved RI determination. To determine the RI values of the RTMs, an internal ILC will be organised. For this internal ILC, stakeholders will also receive an invitation to participate. The measurement uncertainty of the various techniques will be determined. These results will serve as input for a report comparing particle‑by‑particle, size‑resolved and ensemble RI measurements.

Task 3.3

Finally, in Task 3.3, the mathematical models and algorithms for selected material parameters in the measurement process will be validated. First, all methods measuring the particle RI require information on the RI of the dispersant media of the RTMs. Therefore, the RI of the dispersant media is measured using a traceable goniometer and distributed to the project participants. Second, there is no reference particle yet for RI measurements. To validate the machine learning algorithm of the metrological FCM, a reference particle will be matched to a liquid which will be measured using a traceable goniometer. Third, backscattering interferometry requires information on the density and thus will use the values measured in Task 3.1 to validate RI measurements based on density data. Fourth, the mathematical models and algorithms used in RI modelling will be expanded to include more types of complex RTMs. Fifth, simulated density values will be compared to the measured values to validate the modelling of X‑ray attenuation of the contrast agents for the contrast variation SAXS and combined SAXS/TGA/centrifugation methods. Lastly, training data and generated raw data will be combined in a dataset, and an article will be drafted on the machine learning algorithm trained with the training data.