Analytische Chemie
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Faecal contaminants in water are considered serious threats for human health, due to the presence of viruses, bacteria and other harmful microorganisms.1 Urobilin (UB) is a well-known faecal pigment and can be used as a marker for faecal matter in water.2 UB is commonly present in the urine of all mammals as the catabolic end product of bilirubin degradation.2 As the only simple chemical approach to its detection, Schlesinger’s test is usually used to enhance the weak fluorescence of UB in alcoholic media by complexation with Zinc.2, 3 The major limitation of this method is the only weak enhancement of the intrinsically weak UB fluorescence in aqueous media.3 This work presents an approach to introduce different Zn salts for improved fluorescence response, where we found a clear dependence of the fluorescence yield of UB-Zn(II) complexes on the counterion of the salt in water. By employing a combination of fluorescence parameters like transition energy, fluorescence intensity, and fluorescence lifetime, a photophysical understanding of the structure and conformation of the UB-Zn(II) complexes responsible for the fluorescence enhancement in water could be gained. The possibilities of developing a sensitive analytical method based on the acquired understanding are also discussed.
SWIR luminescent nanomaterials – key chemical parameters for bright probes for in vivo bioimaging
(2024)
A current challenge for studying physio-pathological phenomena and diseaserelated processes in living organisms with non-invasive optical bioimaging is the development of bright optical reporters that enable deep tissue penetration, a high detection sensitivity, and a high spatial and temporal resolution. The focus of this project are nanomaterials, which absorb and emit in the shortwave infrared (SWIR) between ~900–2500 nm where scattering, absorption, and autofluorescence of the tissue are strongly reduced compared to the visible and NIR.
Oxidative degradation processes of tetrabromobisphenol A (TBBPA), a brominated flame retardant (BFR) in wood, plastics and electronics, were investigated by electrochemistry (EC) coupled online to electrospray ionization mass spectrometry (ESI/MS). Oxidative phase I and II metabolites production was achieved using an electrochemical flow-through cell equipped with a boron doped diamond electrode. Structural elucidation and prediction of oxidative metabolism pathways of TBBPA according to type II ipso-substitution were based on retention time, m/z ratio in negative mode and fragmentation pattern. Using the data obtained through high resolution MS analysis and the identification of single electron transfer (SET) as the initial step of a two-electron oxidation provided the necessary information to propose a mechanism for the electrochemical oxidation of TBBPA. Oxidation reactions involving aromatic hydroxylation and β-scission were the main degradation observed when studying the electrochemical behavior of TBBPA. Computational chemistry experiments using density functional theory (DFT) allowed to identify mono-hydroxylated reaction intermediate and dismissed the mechanism involving two concurrent hydroxylation. TBBPA oxidation products were compared to known metabolites of its biological and environmental degradation confirming the ability of electrochemistry to simulate β-scission reactions.
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.
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.
The poster describes how molecular biology, especially recombinant expression of proteins, in this case, an enzyme, can underpin developments of biosensors. The fumonisin oxidase produced by the fungus Aspergillus niger (AnFAO) is highly selective for the toxic mycotoxin fumonisin. Its structure and sequence has been published before. We took this information and expressed the enzyme in E. coli. The enzyme proved active and could be employed in an amperometric biosensor for the detection of the mycotoxin.
Immunoassays, based on analyte recognition and capture by highly selective antibodies with high affinity, are intensively used in all fields of laboratory diagnostics and in screen-ings of food and environmental samples. Yet, for many purposes, online sensors are desir-able, and, in principle, all immunoassay tech-niques can be integrated into lab-on-chip set-ups that can work as continuous monitoring devices. Yet, the challenge remains to devel-op platforms and elements that are fit for a quick transition of laboratory microplate as-says to immunosensors.
Ergot alkaloids form a toxicologically relevant group of mould toxins (mycotoxins) that are among the most common contaminants of food 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 adapt the previously determined reaction conditions to enable the synthesis of norergotamine on a mg scale.
The European Commission has identified Advanced Manufacturing and Advanced Materials as two of six Key Enabling Technologies (KETs). By fully utilizing these KETs, advanced and sustainable economies will be created. It is considered that Metrology is a key enabler for the advancement of these KETs. EURAMET, the association of metrology institutes in Europe, has strengthened the role of Metrology for these KETs by enabling the creation of a European Metrology Network for Advanced Manufacturing. The EMN is made up of National Metrology Institutes (NMIs) and Designated Institutes (DIs) from across Europe and was formally established in October 2021. The EMN aims to provide a high-level coordination of European metrology activities for the Advanced Materials and Advanced Manufacturing community.
The EMN itself is organized in three sections representing the major stages of the manufacturing chain: 1) Advanced Materials, 2) Smart Manufacturing Systems, and 3) Manufactured Components & Products. The EMN for Advanced Manufacturing is engaging with stakeholders in the field of Advanced Manufacturing and Advanced Materials (Large companies & SMEs, industry organisations, existing networks, and academia), as well as the wider metrology community (including TCs) to provide input for the preparation of a Strategic Research Agenda (SRA) for Metrology for Advanced Manufacturing.
This presentation will describe the progress in the development of the SRA by the EMN for Advanced Manufacturing. The metrology challenges identified across the various key industrial sectors, which utilise Advanced Materials and Advanced Manufacturing will be presented.
The EMN for Advanced Manufacturing is supported by the project JNP 19NET01 AdvManuNet.