Analytische Chemie
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Paper des Monats
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The environmental fate of the frequently used broad-spectrum β-lactam antibiotic amoxicillin (AMX) is of high concern regarding the potential evolution of antimicrobial resistance (AMR). Moreover, it is known that AMX is prone to hydrolysis, yielding a variety of hydrolysis products (HPs) with yet unknown effects. Studies to identify those HPs and investigate their formation mechanisms have been reported but a long-term study on their stability in real water samples was missing. In this regard, we investigated the hydrolysis of AMX at two concentration levels in four distinct water types under three different storage conditions over two months. Concentrations of AMX and four relevant HPs were monitored by an LC-MS/MS method revealing pronounced differences in the hydrolysis rate of AMX in tap water and mineral water on the one hand (fast) and surface water on the other(slow). In this context, the occurrence, relative intensities, and stability of certain HPs are more dependent on the water type than on the storage condition. As clarified by ICP-MS, the main difference between the water types was the content of the metals copper and zinc which are supposed to catalyze AMX hydrolysis demonstrating an effective method to degrade AMX at ambient conditions.
To better understand the dynamics in hazardous environments, gas distribution mapping aims to map the gas concentration levels of a specified area precisely. Sampling is typically carried out in a spatially sparse manner, either with a mobile robot or a sensor network and concentration values between known data points have to be interpolated. In this paper, we investigate sequential deep learning models that are able to map the gas distribution based on a multiple time step input from a sensor network. We propose a novel hybrid convolutional LSTM - transpose convolutional structure that we train with synthetic gas distribution data. Our results show that learning the spatial and temporal correlation of gas plume patterns outperforms a non-sequential neural network model.
Simulating a Gas Source Localization Algorithm with Gas Dispersion Produced by Recorded Outdoor Wind
(2023)
This paper reports the use of the first gas dispersion simulator capable of introducing large wind fluctuations into simulations. The proposed simulator enables testing of a modification made to a gas source localization algorithm in a realistic scenario in order to study how the change affects it. Gas source localization in an outdoor environment is a challenging task mainly due to the complexity of the gas spread caused by the unpredictable nature of constantly changing wind. Therefore, a novel use of outdoor wind in developing a gas source localization system by simulation is presented in this paper. To consider the characteristic of sudden but large and unpredictable changes in wind direction, we propose to use recorded outdoor wind to simulate a realistic outdoor gas dispersion which has been done for the first time to the best of our knowledge. With the use of this simulator, we have tested a modification to a mobile robot-based gas source localization algorithm. Multiple simulations of the modified and the original particle filter-based algorithm have been done to study the effect of the tested modification. The results showed that a small difference in the algorithm can greatly impact the results. From this study, we show that the use of simulation consisting of the necessary traits to evaluate outdoor gas source localization, has the potential to accelerate the development of a reliable localization system.
In this study, we explored the detection of weak bonds (WBs) due to contamination and faulty curing (FC) using linear ultrasound and machine learning. For this purpose, aluminium single-lap adhesive joints containing three variants of bonding quality were investigated: perfect bond, WB due to release agent (RA) contamination, and WB due to FC. The data, according to the deviation of the bonding protocol, were arranged in two groups, creating two datasets: distinct and complete. Each dataset included all bonding conditions (perfect, RA, and FC), although the distinct dataset contained only marginal cases, which were expected to be well separable, whereas the complete dataset included data with minor deviations from the bonding protocol. Pulse-echo C-scan images were acquired for all prepared samples in the immersion tank, and 45 features were initially extracted from the time traces representing each bonding group. The initial data were analysed via a t-test and pairwise correlation analysis to reveal statistically significant features. Then, we performed dimensionality reduction using tree-based, recursive, sequential, and linear discriminant analysis (LDA) feature selectors to explore feature importance and classification accuracy with different feature subsets. Finally, the important features identified with the different feature selectors were fed to support vector machine (SVM) classifiers, and the classification accuracies were compared amongst the different feature subsets. The classification accuracy using a distinct dataset in some cases demonstrated nearly 99% accuracy, indicating that significant bonding protocol deviations could be easily detected. It was demonstrated that classification accuracy increased with the number of features. However, even in the case of the 2D feature space obtained using linear discriminant analysis, the bonding quality classification accuracy remained higher than 84%. The feature subspace reduction with LDA demonstrated sufficient classification accuracy and an improvement of nearly 40% in training time compared with that for the initial feature set. Thus, the classical ultrasonic pulse-echo C-scan with an LDA feature transformation and SVM classifier could be used to identify the deviations in the bonding protocol in aluminium single-lap adhesive joints.
A certain group of mycotoxins, the ergot alkaloids, has caused countless deaths throughout human history. They are found in rye and other cereals and ingesting contaminated foods can cause serious health problems. To identify contaminated food exceeding the legal limits for ergot alkaloids, a portable and cost-effective test system is of great interest to the food industry. Rapid analysis can be achieved by screening for a marker compound, for which we chose ergometrine. We developed a magnetic bead-based immunoassay for ergometrine with amperometric detection in a flow injection system using a handheld potentiostat and a smartphone. With this assay a limit of detection of 3 nM (1 μg/L) was achieved. In spiked rye flour, ergometrine levels from 25 to 250 μg/kg could be quantified. All results could be verified by optical detection. The developed assay offers great promise to meet the demand for on-site ergometrine detection in the food industry.
Reliable and straightforward characterization and analysis of carbon-based nanomaterials on the atomic level is essential to exploring their potential for application. Here we use a combination of highly surface sensitive x-ray photoelectron (XP) spectroscopy and near edge x-ray absorption fine structure spectroscopy (NEXAFS) to study and quantify the covalent functionalization of nanographene and single-walled carbon nanotubes with nitrene [2 + 1]-cycloaddition. With this comprehensive analytical approach, we demonstrate that the π-conjugated system of functionalized carbon-based nanomaterials is preserved according to NEXAFS analysis, which is challenging to prove with XP spectroscopy investigation alone. Using this combination of analytical approaches, we show significant similarities after functionalization for various carbon-based nanomaterials. Both analytical methods are strongly suited to study possible post-modification reactions of functionalized carbon-based nanomaterials.
As virus outbreaks continue to pose a challenge, a nonspecific viral inhibitor can provide significant benefits, especially against respiratory viruses. Polyglycerol sulfates recently emerge as promising agents that mediate interactions between cells and viruses through electrostatics, leading to virus inhibition.
Similarly, hydrophobic C60 fullerene can prevent virus infection via interactions with hydrophobic cavities of surface proteins. Here, two strategies are combined to inhibit infection of SARS-CoV-2 variants in vitro. Effective inhibitory concentrations in the millimolar range highlight the significance of bare fullerene’s hydrophobic moiety and electrostatic interactions of polysulfates with surface proteins of SARS-CoV-2. Furthermore, microscale thermophoresis measurements support that fullerene linear polyglycerol sulfates interact with the SARS-CoV-2 virus via its spike protein, and highlight importance of electrostatic interactions within it. All-atom molecular dynamics simulations reveal that the fullerene binding site is situated close to the receptor binding domain, within 4 nm of polyglycerol sulfate binding sites, feasibly allowing both portions of the material to interact simultaneously.
X-ray scattering and sorption data associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes". X-ray scattering data is provided for COF and POP materials, including data from stability tests, as three-column ascii files with columns q (nm-1), I (m-1) and uncertainty on I, as well as being provided in 2θ.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or poorly accessible areas. A challenging task however, is the generation of three-dimensional distribution maps from these spatially sparse gas measurements. To obtain high-quality reconstructions, the choice of optimal measuring poses is of great importance. Remote gas sensors, that are commonly used in Robot Assisted Gas Tomography (RAGT), require reflecting surfaces within the sensor’s range, limiting the possible sensing geometries, regardless of whether the robots are ground-based or airborne. By combining ground and aerial robots into a heterogeneous swarm whose agents are equipped with reflectors and remote gas sensors, remote inter-robot gas measurements become available, taking RAGT to the next dimension – releasing those constraints. In this paper, we demonstrate the feasibility of drone-to-drone measurements under realistic conditions and highlight the resulting opportunities.
This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (https://doi.org/10.1039/D2CP05053F).
Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.
TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry which is further described in https://doi.org/10.26272/opus4-55751.
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission.