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Per- and polyfluoroalkyl substances (PFAS) have received global public attention because of their wide distribution in aquatic environments and potential adverse effects to humans and wildlife. Due to the lack of analytical standards and the enormous numbers of these compounds, current target-based methods (e.g., LC-MS/MS) are not suitable for the analysis of new/unknown PFAS and transformation products. Therefore, PFAS sum parameter analysis is becoming increasingly important. For PFAS determination of surface waters, solid phase extraction (SPE) is commonly implemented for clean-up and pre-concentration of samples.
Thus, within this work, single-layer SPE methods (based on Strata™-X, Strata™-X-AW, Strata™-NH2 and Oasis-HLB) were investigated for maximum PFAS coverage utilizing extractable organically bound fluorine (EOF) analysis. Thereby, the optimization procedure relied on the analysis of 3 surface water samples in Berlin, Germany, which were affected by the effluent discharge of wastewater treatment plants. To analyse the SPE elution profiles for EOF and inorganic fluorine, high resolution-continuum source-graphite furnace molecular absorption spectroscopy (HR-CS-GFMAS) and ion chromatography (IC) were used, respectively.
Highest EOF concentrations were achieved by using Strata-X/Strata-XAW as SPE sorbents and methanol as eluent. Furthermore, combinations of the most promising SPE sorbents were selected (X/XAW, XAW/X, HLB/X and WAX/GCB) to extract a wider range of PFASs. By comparing multi-layer SPE methods, lower EOF concentrations were obtained for all investigated combination phases compared to the analysed single phases. For the commercially available combination phase Strata-PFAS (WAX/GCB) for all three river water samples, lower EOF concentrations (1.5 times lower) were determined compared to Strata-X.
The results have shown that single-layer SPE systems are currently superior compared to combination phases for PFAS sum parameter analysis. The multi-layer SPE methods need further optimization regarding appropriate sorbent combinations, loading volumes and elution conditions. The developed single-layer SPE methods can help to elucidate pollutions hotspots and discharge routes.
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.
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%.
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.
Communities worldwide face significant threats from Explosive Remnants of War (ERW), which endanger lives and restrict land usage. From forest fires due to ERWs or in ERW-contaminated areas (e.g., in Jüterbog, Germany) to broader global challenges (e.g., the Ukrainian conflict), the need for efficient detection and removal of these remnants, especially for humanitarian demining, is paramount. Traditional methods, like manual demining, have severe limitations in safety and efficiency. Here, we introduce an innovative solution to these challenges: “Chemosensing Smart Dust.” This technology uses chemoselective dyes that change their fluorescence properties when exposed to explosives like 2,4,6-trinitrotoluene (TNT). Fluorescence-based detection offers superior sensitivity, reduced likelihood of false positives, and enhanced accuracy of explosive detection. Drones, equipped with excitation lasers or LEDs, deploy the Chemosensing Smart Dust over areas of interest and actively detect the fluorescence changes using high-resolution cameras, offering a rapid, safe, and adaptable detection method. Beyond demining, this innovative approach has potential applications in monitoring polluted areas, homeland security, and emergency response.
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 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.
Surface functionalized silica nanoparticles (SiO2-NP) gained great interest in the life and material sciences, as they can be used e.g. as drug carriers, fluorescent sensors, and multimodal labels in bioanalytical assays and imaging applications. They are highly stable, are easily produced and modified on a large scale at low cost and can be labeled or stained with a multitude of sensor dyes. These dye modified particle conjugates have several advantages as compared to conventional molecular probes like enhanced brightness, ease of designing ratiometric systems by combining analyte-sensitive and inert reference dyes, and increased photostability. Moreover, stained nanoparticles can enable the use of hydrophobic dyes in aqueous environments.
Here we present our work on multicolored sensors for the measurement of pH, oxygen and saccharides utilizing amorphous SiO2 NPs.