BAM Dissertationen ohne Nummerierung
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The contamination of drinking water with pharmaceuticals represents a severe health risk. In order to monitor the drinking water quality continuously and enable quick countermeasures in case of contamination, novel sensors are required. Here, immunoanalytical methods based on the binding of the analyte to highly selective antibodies can be helpful. In this work, magnetic bead-based immunoassays (MBBAs) have been developed for the detection of two relevant contaminants of drinking water: diclofenac (DCF) and amoxicillin (AMX). In case of the latter, not only the parent drug is of interest in the risk assessment but also its hydrolysis products (HPs). In a comprehensive study, the influence of external factors and intrinsic properties of the water on the rate of hydrolysis was investigated. As the hydrolysis of AMX further impacts the recognition by the antibody, a strategy to analyze samples with unknown hydrolysis degree of AMX was established employing the enzyme β-lactamase in sample preparation. For both analytes, the MBBAs enable the fast quantification with results obtained in less than one hour which represents a major improvement over conventional immunoassays like the enzyme-linked immunosorbent assay (ELISA). Compared to the respective ELISAs with the same antibodies, the MBBAs further exhibit improved analytical parameters such as a broader measurement range and lower limits of detection. Due to the magnetic properties of the beads that serve as a platform for the assays, they are suitable for the mobile and automated detection at the point-of-care. An integrated diagnostic system was designed in which electrochemical detection with chronoamperometry on a microfluidic chip allows for further miniaturization of the system to enable monitoring of the drinking water quality online in water supply pipes at waterworks.
Development of electrochemical antibody-based and enzymatic assays for mycotoxin analysis in food
(2023)
Electrochemical methods are promising to meet the demand for easy-to-use devices monitoring key parameters in the food industry. Many companies run own lab procedures for mycotoxin analysis, but it is a major goal to simplify the analysis. The enzyme-linked immunosorbent assay using horseradish peroxidase as enzymatic label, together with 3,3',5,5' tetramethylbenzidine (TMB)/H2O2 as substrates allows sensitive mycotoxin detection with optical detection methods. For the miniaturization of the detection step, an electrochemical system for mycotoxin analysis was developed. To this end, the electrochemical detection of TMB was studied by cyclic voltammetry on different screen-printed electrodes (carbon and gold) and at different pH values (pH 1 and pH 4). A stable electrode reaction, which is the basis for the further construction of the electrochemical detection system, could be achieved at pH 1 on gold electrodes. An amperometric detection method for oxidized TMB, using a custom-made flow cell for screen-printed electrodes, was established and applied for a competitive magnetic bead-based immunoassay for the mycotoxin ochratoxin A. A limit of detection of 150 pM (60 ng/L) could be obtained and the results were verified with optical detection. The applicability of the magnetic bead-based immunoassay was tested in spiked beer using a handheld potentiostat connected via Bluetooth to a smartphone for amperometric detection allowing to quantify ochratoxin A down to 1.2 nM (0.5 µg/L). Based on the developed electrochemical detection system for TMB, the applicability of the approach was demonstrated with a magnetic bead-based immunoassay for the ergot alkaloid, ergometrine. Under optimized assay conditions a limit of detection of 3 nM (1 µg/L) was achieved and in spiked rye flour samples ergometrine levels in a range from 25 to 250 µg/kg could be quantified. All results were verified with optical detection. The developed electrochemical detection method for TMB gives great promise for the detection of TMB in many other HRP-based assays. A new sensing approach, based on an enzymatic electrochemical detection system for the mycotoxin fumonisin B1 was established using an Aspergillus niger fumonisin amine oxidase (AnFAO). AnFAO was produced recombinantly in E. coli as maltose-binding protein fusion protein and catalyzes the oxidative deamination of fumonisins, producing hydrogen peroxide. It was found that AnFAO has a high storage and temperature stability. The enzyme was coupled covalently to magnetic particles, and the enzymatically produced H2O2 in the reaction with fumonisin B1 was detected amperometrically in a flow injection system using Prussian blue/carbon electrodes and the custom-made wall-jet flow cell. Fumonisin B1 could be quantified down to 1.5 µM (≈ 1 mg/L). The developed system represents a new approach to detect mycotoxins using enzymes and electrochemical methods.
In the event of moisture deterioration, rapid detection and localization is particularly important to prevent further deterioration and costs. For building floors, the layered structure poses a challenging obstacle for most moisture measurement methods. But especially here, layer-specific information on the depth of the water is crucial for efficient and effective repairs. Ground Penetrating Radar (GPR) shows the potential to generate such depth information. Therefore, the present work investigates the suitability of GPR in combination with machine learning methods for the automated classification of the typical deterioration cases (i) dry, (ii) wet insulation, and (iii) wet screed.
First, a literature review was conducted to identify the most common methods for detecting moisture in building materials using GPR. Here, it especially became clear that all publications only investigated individual time-, amplitude- or frequency features separately, without combining them. This was seen as a potential aspect for innovation, as the multivariate application of several signal features can help to overcome individual weaknesses and limitations.
Preliminary investigations carried out on drying screed samples confirmed the profitable use of multivariate evaluations. In addition to the general suitability and dependencies of various features, first limitations due to possible interference between the direct wave and the reflection wave could be identified. This is particularly evident with thin or dry materials, for which the two-way travel times of the reflected radar signals become shorter.
An extensive laboratory experiment was carried out, for which a modular test specimen was designed to enable the variation of the material type and thickness of screed and insulation, as well as the simulation of moisture deteriorations. The data collected revealed clear differences between dry and deteriored structures within measured B-scans. These deviations were to be detected with the newly introduced B-scan features, which evaluate the statistical deviation of A-scan features within a survey line. In this way, deteriorations to unknown floor structures are recognized, regardless of the material parameters present. In a subsequent training and cross-validation process of different classifiers, accuracies of over 88 \% of the 504 recorded measurements (252 different experimental setups) were achieved. For that, the combination of amplitude and frequency features, which covered all relevant reflections of the radar signals, was particularly beneficial. Furthermore, the data set showed only small differences between dry floors and deteriored screeds for the B-scan features, which could be attributed to a homogeneous distribution of the added water in the screeds. The successfully separation of these similar feature distributions raised the suspicion of overfitting, which was examined in more detail by means of a validation with on-site data.
For this purpose, investigations were carried out at five different locations in Germany, using the identical measurement method like in the laboratory. By extracting drilling cores, it was possible to determine the deterioration case for each measurement point and thus generate a corresponding reference. However, numerous data had to be sorted out before classification, since disturbances due to underfloor heating, screed reinforcements, steel beams or missing insulation prevented comparability with the laboratory experiments. Validation of the remaining data (72 B-scans) achieved only low accuracy with 53 \% correctly classified deterioration cases. Here, the previously suspected overfitting of the small decision boundary between dry setups and deteriored screeds within the laboratory proved to be a problem. The generally larger deviations within (also dry) on-site B-scans were thus frequently misclassified as screed deterioration. In addition, there were sometimes strongly varying layer thicknesses or changing cases of deterioration within a survey line, which caused additional errors due to the local limitation of the drilling core reference. Nevertheless, individual on-site examples also showed the promising potential of the applied signal features and the GPR method in general, which partly allowed a profound interpretation of the measurements. However, this interpretation still requires the experience of trained personnel and could not be automated using machine learning with the available database. Nevertheless, such experience and knowledge can be enriched by the findings of this work, which provide the basis for further research.
Future work should aim at building an open GPR data base of on-site moisture measurements on floors to provide a meaningful basis for applying machine learning. Here, referencing is a crucial point, whose limitations with respect to the moisture present and its distribution can easily reduce the potential of such efforts. The combination of several reference methods might help to overcome such limitations. Similarly, a focus on monitoring approaches can also help to reduce numerous unknown variables in moisture measurements and increase confidence in the detection of different deterioration cases.
Laser-based active thermography is a contactless non-destructive testing method to detect material defects by heating the object and measuring its temperature increase with an infrared camera. Systematic deviations from predicted behavior provide insight into the inner structure of the object. However, its resolution in resolving internal structures is limited due to the diffusive nature of heat diffusion. Thermographic super resolution (SR) methods aim to overcome this limitation by combining multiple thermographic measurements and mathematical optimization algorithms to improve the defect reconstruction.
Thermographic SR reconstruction methods involve measuring the temperature change in an object under test (OuT) heated with multiple different spatially structured illuminations. Subsequently, these measurements are inputted into a severely ill-posed and heavily regularized inverse problem, producing a sparse map of the OuT’s internal defect structure. Solving this inverse problem relies on limited priors, such as defect-sparsity, and iterative numerical minimization techniques. Previously mostly experimentally limited to one-dimensional regions of interest (ROIs), this thesis aims to extend the method to the reconstruction of two-dimensionalROIs with arbitrary defect distributions while maintaining reasonable experimental complexity. Ultimately, the goal of this thesis is to make the method suitable for a technology transfer to industrial applications by advancing its technology readiness level (TRL).
In order to achieve the aforementioned goal, this thesis discusses the numerical expansion of a thermographic SR reconstruction method and introduces two novel algorithms to invert the underlying inverse problem. Furthermore, a forward solution to the inverse problem in terms of the applied SR reconstruction model is set up. In conjunction with an additionally proposed algorithm for the automated determination of a set of (optimal) regularization parameters, both create the possibility to conduct analytical simulations to characterize the influence of the experimental parameters on the achievable reconstruction quality. On the experimental side, the method is upgraded to deal with two-dimensional ROIs, and multiple measurement campaigns are performed to validate the proposed inversion algorithms, forward solution and two exemplary analytical studies. For the experimental implementation of the method, the use of a laser-coupled DLP-projector is introduced, which allows projecting binary pixel
patterns that cover the whole ROI, reducing the number of necessary measurements per ROI significantly (up to 20x).
Finally, the achieved reconstruction of the internal defect structure of a purpose-made OuT is qualitatively and qualitatively benchmarked against well-established thermographic testing methods based on homogeneous illumination of the ROI. Here, the background-noise-free two-dimensional photothermal SR reconstruction results show to outclass all defect reconstructions by the considered reference methods.
This thesis investigates ultrasonic guided waves (GW) in multi-layered plates with the focus on higher order modes. The aim is to develop techniques for hybrid structures such as of adhesive bonds and composite pressure vessels (COPV) which are widely used in automotive and aerospace industries and are still challenging to inspect non-destructively. To be able to analyse GW, numerical methods and precise material properties are required. For this purpose, an efficient semi-analytical approach, the Scaled Boundary Finite Element Method, is used. The material properties are inferred by a GW-based optimisation procedure and a sensitivity study is performed to demonstrate the influence of properties on GW. Then, an interesting feature, called mode repulsion, is investigated with respect to weak and strong adhesive bonds. The results show that the coupling between two layers influences the distance between coupled modes in a mode repulsion region, thus allowing for the characterisation of adhesive bonds. At next, wave-damage interaction is studied in the hybrid structure as of the COPV. Results show that the wave energy can be concentrated in a certain layer enabling damage localisation within different layers. Further investigations are carried out on the hybrid plate with an impact-induced damage. Two well-known wavenumber mapping techniques, which allow to quantify the damage in three dimensions, are implemented and their comparison is done for the first time.