Open Access

This talk is a summary of initiatives, BAM did in the last years to harmonize the analysis of microplastic. It starts with the explanation of needs: Standards, reference materials and accurate method analysis. It presents measurements with the TED-GC/MS as screening method to determine the microplastic mass and gives different papers as guidance, how to handle various matrices such as bottles, surface or wastewater as well as sediment or soil. The talk also presents the reference materials BAM developed and gives an outlook for existing ISO standards.

Accurate determination of particle number concentration is essential in the industry as well the healthcare sector. Widely applied methods for the measurement of particle number concentration, such as MADLS, SAXS, spICP-MS, rely on material specific input parameters, modelling assumptions, or calibration strategies, which contribute to measurement uncertainty, making absolute quantification challenging and limiting direct metrological traceability. In the framework of the European project ConcenSus (https://concensusproject.org/home), a dedicated approach is being studied to determine (nano)particle number concentration using imaging methods. SEM, TEM and other imaging methods are well established for characterising the sizes and shapes of advanced materials with ISO standards in place (ISO 19749:2021, ISO 21363:2020). However, no corresponding standard operation procedures exist for (nano)particle number concentration determination using imaging methods. This work presents a proof-of-principle study for determining the particle number concentration using SEM by employing a dedicated sample preparation method. First, relatively simpler materials such as Ag and Pd nanoparticles have been tested, with future project work increasing particle complexity to include other material types, for example, liposomes, QDs, mesoporous SiO2, etc. The performance of our approach has been assessed by comparison with concentrations measured with MADLS. The results obtained with both methods are in the same order of magnitude (10E11 particles/mL), which demonstrate the strong potential of SEM combined with dedicated sample preparation as a powerful method for the number metrology of (nano)particles independent of material-type and theoretical assumptions, focusing mainly on direct particle counting. Further optimisation of the sample preparation will enable an automated workflow for the measurement of particle number concentration by imaging.

With recent advances in generative machine learning, different models have been adapted to predict novel materials, and new architectures are emerging frequently. While several metrics allow for the rating of individual characteristics (e.g., quality or novelty) of generated crystal structures on an instance level, approaches that evaluate the general performance of generative models for materials prediction are missing. To close this gap, we developed the Transport Novelty Distance (TNovD). This metric evaluates generative models by jointly judging the novelty and quality of all newly generated crystal structures. Thereto, the Wasserstein distance is calculated on an abstract feature space distribution derived from the chemical and physical characteristics of the materials. These features are created by embedding the crystals description with an invariant Graph Neural Network (GNN) that was trained with the InfoNCE loss on the identical set of materials as the generative model. Using contrastive learning allows to not only account for materials themselves, but also for their augmented counterparts and differently sized supercells. Based on the resulting feature space, couplings between generated and train set are calculated and split into a quality and a memorization regime by a threshold. This allows to evaluate quality and novelty simultaneously. The TNovD was tested on various toy experiments for memorization and different cases of crystal structure degeneration. Additionally, we validated it on the MP20 validation set and the WBM substitution dataset. The experiments results demonstrate the TNovD capabilities of detecting both memorization and low-quality materials. Afterwards, we benchmark the performance of several popular material generative models with the MP20 validation data. While introduced for materials, our TNovD framework is domain-agnostic and can be adapted for other areas in the space of chemical compounds, such as images and molecules.

Generative machine learning is increasingly used for inorganic crystal structure generation. Most models and the corresponding evaluation approaches rely on simple forms of crystal structure representation. In this paper, we showcase the power of atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs), such as MACE, for such tasks. We first introduce a distance measure that assesses the output of material generative models by capturing both quality and novelty in a single distribution-based evaluation framework. In particular, we introduce the Coarse-Fine Transport Distance (CFTD) using two different featurizers, where the quality component is based on coarse MACE features. We showcase CFTD’s versatility in capturing crystal-structure quality while also detecting memorization, and compare it with the recently introduced continuous SUN metrics. We further show that coarse MACE features can be used as guidance for a material generative model.

The removal of per‐ and polyfluoroalkyl substances (PFAS) from water is of utmost importance. Quaternized polyethyleneimine (qPEI) was shown to be an efficient PFAS adsorbent material. Here, synthesized qPEI is immobilized on a quartz crystal microbalance (QCM) substrate and the adsorption of different PFAS on the qPEI‐coated surface is explored in detail. qPEI adsorbers prepared as films are used to explore the gravimetric uptake of selected perfluoroalkyl carboxylic acids (PFCAs), and perfluorobutanesulfonic acid (PFBS). The advantage of this QCM method is the in situ monitoring of the adsorbed quantity. The results reveal a clear dependence of the uptake amount on the fluorinated carbon chain length, following the trend PFNA PFOA PFHpA PFHxA PFBS, indicating stronger interaction and higher adsorption capacities for longer‐chain PFAS. Vibrational spectroscopy provides more insights and verifies that, in addition to the ionic interaction, the PFAS‐CF groups participate in the fluorospecific interactions at the adsorption. Overall, this study demonstrates the suitability of qPEI‐based films for PFAS uptake investigations and highlights the importance of the molecular structure on the adsorption efficiency. Although the PFAS concentrations explored are significantly larger than the relevant environmental concentrations, this study supports the development of efficient, low‐cost PFAS adsorbent materials.