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This key comparison aims to assess the capabilities of the participants to determine the amount-of-substance fraction oxygen in nitrogen. The GAWG has classified this as a track B comparison, due to the unexpected 50 μmol/mol argon mole fraction content of the transfer standards, which effects the achievable performance of some measurement techniques such a GC-TCD. The separation of oxygen and argon is challenging, and not all systems in use are equally well designed for it. As this analytical challenge due to a substantial fraction of argon in the transfer standards became a reality, the Gas Analysis Working Group (GAWG) decided to qualify this key comparison as a regular key comparison and not as a core comparison, which may be used to support calibration and measurement capabilities (CMCs) for oxygen in nitrogen, or for oxygen in nitrogen mixtures containing argon only (see also the section on support to CMCs).
We report polyethylene glycol (PEG)-grafting antifouling surfaces using a plasma copolymerized (PcP) technique to monitor protease activity in complex media. By varying the mixing ratio of the PEG and ethylenediamine (EDA) precursors, the PcP-PEG-EDA (PcP-PE) film was able to easily control surface amine density with good preservation of the internal PEG structure. We found that nonspecific protein adsorption was dramatically reduced in serum-containing media on the PcP-PE films, as opposed to that on plasma polymerized-EDA (PP-E) films without PEG. When SPR sensor chips coated with PcP-PE film were employed to detect protease activity, biotinylated luciferase probes (luciferase-peptide-biotin) on streptavidin-conjugated SPR chips enabled real-time and label-free measurement of matrix metalloproteinase activity in cell culture media. Owing to its excellent antifouling ability, this newly developed method boasts minimal nonspecific binding and can serve as a biochip platform to promote a wide range of applications in the biological field.
Classification of Spot-Welded Joints in Laser Thermography Data Using Convolutional Neural Networks
(2021)
Spot welding is a crucial process step in various industries. However, classification of spot welding quality is still a tedious process due to the complexity and sensitivity of the test material, which drain conventional approaches to its limits. In this article, we propose an approach for quality inspection of spot weldings using images from laser thermography data. We propose data preparation approaches based on the underlying physics of spot-welded joints, heated with pulsed laser thermography by analyzing the intensity over time and derive dedicated data filters to generate training datasets. Subsequently, we utilize convolutional neural networks to classify weld quality and compare the performance of different models against each other. We achieve competitive results in terms of classifying the different welding quality classes compared to traditional approaches, reaching an accuracy of more than 95 percent. Finally, we explore the effect of different augmentation methods.
Laser tissue soldering (LTS) based on indocyanine green (ICG)-mediated heat- denaturation of proteins might be a promising alternative technique for micro-suturing, but up to now the Problem of too weak shear strength of the solder welds in comparison to sutures is not solved. Earlier reports gave promising results showing that solder supported by carrier materials can enhance the cohesive strength of the liquid solder. In these studies, the solder was applied to the carriers by dip coating. Higher reliability of the connection between the solder and the carrier material is expected when the solder is bound covalently to the carrier material. In the present study a poly(ether imide) (PEI) membrane served as carrier material and ICG- supplemented albumin as solder substrate. The latter was covalently coupled to the carrier membrane under physiological conditions to prevent structural protein changes. As laser source a diode continuous-wave laser emitting at 808 nm with intensities between 250mW and 1500mW was utilized. The Albumin functionalized carrier membrane was placed onto the tunica media of explanted pig thoracic aortae forming an overlapping area of approximately 0.5×0.5 cm2. All tests were performed in a dry state to prevent laser light absorption by water. Infrared spectroscopy, spectro-photometrical determination of the secondary and Primary amine groups after acid orange II staining, contact angle measurements, and atomic force microscopy proved the successful functionalization of the PEI membrane with albumin. A laser power of 450mW LTS could generate a membrane-blood vessel connection which was characterized by a shear strength of 0.08±0.002MPa, corresponding to 15% of the tensile strength of the native blood vessel. Theoretically, an overlapping zone of 4.1mmaround the entire circumference of the blood vessel could have provided shear strength of the PEI membrane-blood vessel compound identical to the tensile strength of the native blood vessel. These in-vitro results confirmed the beneficial effects of solder reinforcement by carrier membranes, and suggest LTS with covalently bound solders on PEI substrates for further studies in animal models.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
We investigate a single-pixel camera (SPC) that relies on non-mechanical scanning with a terahertz (THz) spatial light modulator (SLM) and Compressed Sensing (CS) for image generation. The camera is based on a 350 GHz multiplier source and a Golay cell detector. The SLM consists of a Germanium disc, which is illuminated by a halogen lamp. The light of the lamp is transmitted through a thin-film transistor (TFT) liquid crystal display (LCD). This enables the generation of light patterns on the Germanium disc, which in turn produce reflecting patterns for THz radiation. Using up to 1000 different patterns the pseudo-inverse reconstruction algorithm and the CS algorithm CoSaMP are evaluated with respect to image quality. It is shown that CS allows a reduction of the necessary measurements by a factor of three without compromising the image quality.
We combine three different approaches to greatly enhance the defect reconstruction ability of active thermographic testing. As experimental approach, laser-based structured illumination is performed in a step-wise manner. As an intermediate signal processing step, the virtual wave concept is used in order to effectively convert the notoriously difficult to solve diffusion-based inverse problem into a somewhat milder wavebased inverse problem. As a final step, a compressed-sensing based optimization procedure is applied which efficiently solves the inverse problem by making advantage of the joint sparsity of multiple blind measurements. To evaluate our proposed processing technique, we investigate an additively manufactured stainless steel sample with eight internal defects. The concerted super resolution approach is compared to conventional thermographic reconstruction techniques and shows an at least four times better spatial resolution.
The separation of two closely spaced defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown.
The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects.
Laser excited super resolution thermal imaging for nondestructive inspection of internal defects
(2020)
A photothermal super resolution technique is proposed for an improved inspection of internal defects. To evaluate the potential of the laser-based thermographic technique, an additively manufactured stainless steel specimen with closely spaced internal cavities is used. Four different experimental configurations in transmission, reflection, stepwise and continuous scanning are investigated. The applied image post-processing method is based on compressed sensing and makes use of the block sparsity from multiple measurement events. This concerted approach of experimental measurement strategy and numerical optimization enables the resolution of internal defects and outperforms conventional thermographic inspection techniques.