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Since nowadays people spend most of their time indoors, a healthy environment is essential. Volatile organic compounds (VOCs) emitted from furniture and building materials are reported to cause health complaints. Therefore, the usage of low emitting materials will improve the indoor air quality. Quantitative VOC emission testing is usually conducted in emission test chambers under specified controlled conditions as described in DIN 16000-9 and DIN EN 16516.
For reasons of quality control/quality assurance (QC/QA) and for a better comparability of test results from different laboratories, suitable emission reference materials (ERM) are needed. Here, it is important to have a homogenous material with known emission rates over a specific time. Different approaches can be found in literature, inter alia polymer films loaded with the target compound to be released again, or a lacquer material to which a VOC mixture is added. After curing of the lacquer, the material can be loaded into a test chamber. Drawback of those approaches are their relatively fast decreasing emission profiles. For QC/QA purposes according to the test standards, VOC sources with constant emission profiles are desirable.
The EU-funded research project MetrIAQ “Metrology for the determination of emissions of dangerous substances from building materials into indoor air” is working on a multi-component ERM with an envisaged instability of ≤ 10 % in the emission rate over at least 14 days.
Within a doctoral thesis porous materials are impregnated with VOCs. Supercritical CO2 is used as solvent. Thus, the impregnated material does not contain any solvent that may show a measurable amount of emission in the emission test chamber. Furthermore, CO2 has the benefits to have a good availability and low costs. For the selection of porous materials several properties like the pore size, the surface, and the interaction with the components in the atmosphere need to be considered. The impregnation method is optimised while the different porous materials are tested. For the selection of porous materials the pores need to be large enough for the VOC molecules, further influence of the pore size is tested.
Emission testing of volatile organic compounds (VOC) from materials and products is commonly based on emission test chamber measurements. To ensure the comparability of results from different testing laboratories their measurement performance must be verified. For this purpose, Bundesanstalt für Materialforschung und -prüfung (BAM) organizes an international proficiency test every two years using well-characterised test materials (one sealant, one furniture board and four times a lacquer) with defined VOC emissions. The materials fulfilled the requirements of homogeneity, reproducibility, and stability. Altogether, 41 VOCs were included of which 37 gave test chamber air concentrations between 10 and 98 µg/m³. This is the typical concentration range to be expected and to be quantified when performing chamber tests. Four compounds had higher concentrations between 250 and 1105 µg/m³. The relative standard deviations (RSD) of BAM proficiency tests since 2008 are compared and the improvement of the comparability of the emission chamber testing is shown by the decrease of the mean RSD down to 23% in 2021. In contrast, the first large European interlaboratory comparison in 1999 showed a mean RSD of 51%.
Amorphous materials play an important role in C-F bond activation but face the difficulty of limited available structural information by methods such as powder XRD and solid-state MAS NMR spectroscopy especially if the nucleus is not abundant enough. Here, we present heterogeneous catalysts, active in C-F bond activation, where EXAFS allowed specifically elucidating the local structure, which would have not been possible elsewise.
Bacterial adhesion on surfaces of medical, water and food applications may lead to infections, water or food spoilage and human illness. In comparison to traditional static and macro flow chamber assays for biofilm formation studies, microfluidic chips allow in situ monitoring of biofilm formation under various flow regimes, have better environment control and smaller sample requirements.
In this work, a novel microfluidic platform is developed to investigate biofilm adhesion under precisely controlled bacteria concentration, temperature, and flow conditions. This platform central unit is a single-inlet microfluidic flow cell with a 5 mm wide chamber designed and tested to achieve ultra-homogenous flow in the central area of chamber. Within this area, defined microstructures are integrated that will disturb the homogeneity of the flow, thus changing bacterial adhesion pattern.
Here we present the monitoring of bacterial biofilm formation in a microfluidic chip equipped with a microstructure known as micro-trap. This feature is based on a 3D bacteria trap designed by Di Giacomo et al. and successfully used to sequester motile bacteria.
At first, fluorescent particles similar in size to Escherichia coli (E. coli) are used to simulate bacteria flow inside the flow cell and at the micro-trap. The turbulences induced by the trap are analyzed by imaging and particle tracking velocimetry (PTV). Secondly, the model strain E. coli TG1, ideal and well described for biofilm studies, is used to analyze biofilm formation in the micro-trap. Therefore, a stable fluorescent strain E. coli TG1-MRE-Tn7-141 is constructed by using Tn7 transposon mutagenesis according to the method described by Schlechter et al. Sequestering of E. coli cells within the micro-trap was followed using epifluorescence microscopy.
The novel microfluidic platform shows great potential for assessment of bacterial adhesion under various flow regimes. The performance of structural feature with respect to the generation of turbulences that promote or reduce bacterial adhesion can be systematically examined.
The combination of flow analysis and fluorescent strain injection into the microfluidic chip shows that the micro-trap is useful for capturing bacteria at defined positions and to study how flow conditions, especially micro-turbulences, can affect biofilm formation. It represents a powerful and versatile tool for studying the relation between topography and bacteria adhesion.
The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.
The motivation and overall objective of the project are to develop electrocatalysts that are free of noble metals (e.g., Pt or Au) and are instead based on medium and high entropy alloys (MEA\HEA) of transition metals for the electrochemical reduction of CO2.
The MEA\HEA are multi principal element alloys (MPEAs) consisting of more than three elements with almost equal alloying proportions, forming solid solutions without intermetallic phases. In such a crystal structure, the individual elements are well mixed, and each atom has different nearest neighbours. In catalysis, especially in selectivity, it is precisely this atomic chaos that matters. Due to the large number of possible combinations of elements, these materials offer excellent conditions to tune their functional properties for specific applications.
Especially, catalyst systems in which Cu is combined with another metallic component show a significant increase in catalytic efficiency compared to monometallic systems. Since the catalytic activity, selectivity, and stability of electrocatalysts strongly depend on the size and surface, systematic studies on the influence of the organic stabilizers on heterogeneous catalysis are also of interest.
The focus of this project is to design Cu-based MEA\HEA electrocatalysts on the atomic level. For this purpose, base alloys will be prepared, processed into high-purity nanoparticles by pulsed laser ablation, and tested as electrocatalysts.
Multi-principal element alloy (MPEA) nanoparticle electrocatalysts have the potential to provide a cost-effective and efficient alternative to noble metal electrocatalysts. The chemically complex nature and the high configurational entropy of MPEAs offer advantages in tailoring catalytic activity, product selectivity, and improved stability under harsh reaction conditions. Cu-containing bimetallic catalyst systems have already been demonstrated to lead to a significant increase in catalytic efficiency compared to monometallic systems. Thus, this project aims at the design of Cu-containing MPEAs and nanoparticle electrocatalysts for carbon dioxide reduction reaction.
In this project, base alloys were prepared by means of arc melting with subsequent homogenization treatments and processed by pulsed laser ablation in water and organic solvents into high-purity nanoparticles. The nanoparticles were characterized by means of transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX). Electrochemical testing was performed both on bulk alloy samples and nanoparticle film coated glassy carbon electrodes by means of cyclic voltammetry (CV), linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). Nanoparticle coated electrodes have been investigated by means of atomic force microscopy (AFM) and scanning kelvin probe force microscopy (SKPFM) to assure a homogeneous distribution on the electrode surface. The presentation will summarize our initial results on the electrocatalytic activity of Cu-MPEA system for carbon dioxide reduction.
Ergot alkaloids form a toxicologically relevant group of mould toxins (mycotoxins) that are among the most common contaminants of foodstuff and animal feed worldwide. Reliable controls are essential to minimise health risks and economic damage. Due to their toxicological relevance, EU limit values for 12 priority ergot alkaloids have been introduced for the first time in 2022 and range from 500 μg/kg in rye milling products down to 20 ug/kg Processed cereal-based foods for infants and young children[1]. High-performance liquid chromatography - mass spectrometry is used to quantify low concentrations of ergots in food, however the European standard analytical procedure cannot be applied due to the lack of isotopically labelled reference standards.
The complex structure of the ergot alkaloids makes a total synthesis extremely challenging, expensive and time-consuming. Therefore, we are focusing on different semi-preparative methods (electrochemistry, organic synthesis, heterogeneous catalysis) to specifically N-demethylate the C8 carbon atom of the lysergic acid moiety. The norergot alkaloid formed is then isotopically labelled using an electrophilic methyl source, i.e. iodomethane or dimethyl sulphate to obtain the specific isotopic labelled ergot alkaloid. Initial experiments have shown that N-demethylation of the ergot alkaloid ergotamine is possible by both electrochemical and wet-chemical organic synthesis. The next step is to improve the previously determined reaction conditions to enable the synthesis of norergotamine on a mg scale for further reactions.