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- Active thermal imaging (2)
- Fatty acids (2)
- Laser thermography (2)
- Rhenium (2)
- Volatilome (2)
- 3D Scanning (1)
- A549 (1)
- ATR-FTIR spectroscopy (1)
- Alcohol marker (1)
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Organisationseinheit der BAM
An international intercomparison involving eight national metrology institutes (NMIs) was conducted to establish their current measurement capabilities for determining five selected congeners from the brominated flame retardant classes polybrominated diphenyl ethers and polybrominated biphenyls. A candidate reference material consisting of polypropylene fortified with technical mixtures of penta-, octa- and decabromo diphenyl ether and decabromo biphenyl, which was thoroughly assessed for material homogeneity and stability, was used as study material. The analytical procedures applied by the participants differed with regard to sample pre-treatment, extraction, clean-up, employed calibrants and type of calibration procedure as well as regarding analytical methods used for separation, identification and quantification of the flame retardant congeners (gas chromatography coupled to an electron capture detector (GC-ECD), gas chromatography-mass spectrometry in the electron ionisation mode (GC-EI-MS), gas chromatography-mass spectrometry in the electron capture negative ionisation mode (GC-ECNI-MS), and liquid chromatography-inductive coupled plasma-mass spectrometry (LC-ICP-MS)). The laboratory means agreed well with relative standard deviations of the mean of means of 1.9%, 4.8%, 5.5% and 5.4% for brominated diphenyl ether (BDE) 47, 183 and 209 and for the brominated biphenyl (BB) congener 209, respectively. For BDE 206, a relative standard deviation of 28.5% was obtained. For all five congeners, within-laboratory relative standard deviations of six measurements obtained under intermediate precision conditions were between 1% and 10%, and reported expanded measurements uncertainties typically ranged from 4% to 10% (8% to 14% for BDE 206). Furthermore, the results are in good agreement with those obtained in the characterization exercise for determining certified values for the flame retardant congeners in the same material. The results demonstrate the state-of-the-art measurement capabilities of NMIs for quantifying representative BDE congeners and BB 209 in a polymer. The outcome of this intercomparison (pilot study) in conjunction with possible improvements for employing exclusively calibrants with thoroughly assessed purity suggests that a key comparison aiming at underpinning calibration and measurement capability (CMC) claims of NMIs can be conducted.
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
In an attempt to develop new technetium-based radiopharmaceuticals for the noninvasive diagnosis of myocardial metabolism, we have synthesized three examples of novel metal-containing fatty acid derivatives according to the 3+1 mixed-ligand and the Schiff base/tricarbonyl design. The chelates contain the metal core in the oxidation states +5 and +1, respectively, and are attached to the end-position of a fatty acid chain. The complex formation was accomplished by ligand-exchange reactions with three different rhenium precursors, whereas the inactive rhenium metal was utilized as a surrogate of the technetium radionuclide. The molecular structures of the fatty acid complexes 7, 10 and 14 were determined by single-crystal X-ray diffraction analyses and impressively show a general problem in technetium tracer research, namely the significant structural alterations of bioactive molecules by coordination even to small metal chelates.
Technetium- and Rhenium-Labelled Fatty Acids as Model Compounds for Myocardial Metabolism Imaging
(2002)
In an attempt to develop new technetium-based radiopharmaceuticals for the non-invasive diagnosis of oxidative myocardial metabolism, rhenium model compounds according to the 3+1' mixed ligand approach as well as the organometallic tricarbonyl-design were synthesized. The geometrical impact of different chelates on the integrity of the fatty acid head structure was determined by single crystal X-ray analyses. To evaluate the diagnostic potential of the analogous Technetium-99m compounds, fatty acid complexes of the 3+1' mixed ligand type were prepared on n.c.a.-level and studied in the isolated constant-flow-perfused guinea pig heart model; compared to established [123I]Iodine-labelled fatty acid radiotracers, the tested Technetium-99m derivatives showed a specific, however significantly lower myocardial extraction rate.
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).
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.
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.
The design and operation of an observational study on the profiles of volatile organic compounds (VOC) in the breath of 37 lung cancer patients and 23 healthy controls of similar age is outlined. The necessity to quantify each VOC considered as potential disease marker on basis of individual calibration is elaborated and the quality control measures required to maintain reproducibility in breath sampling and subsequent instrumental trace VOC analysis using SPME-GC-MS over a study period of 14 months are described. 24 VOC were quantified on basis of their previously suggested potential as cancer markers. The breath concentration levels of aromatic compounds was expectedly increased in smokers while lung cancer patients displayed significantly increased levels of oxygenated VOC such as aldehydes, 2-butanone and 1-butanol. Though sets of selected oxygenated VOC displayed sensitivities and specificities between 80 and 90% using linear discriminant analysis with leave-one-out cross validation the effective selectivity of the breath VOC approach with regard to cancer detection is clearly limited. Results were discussed against the background of the literature on volatile cancer marker investigations and the prospects to link increased VOC levels in patients’ breath with approaches to employ sniffer dogs. Experiences from this study and the literature suggest that the current state of breath VOC based discrimination between cancer patients and healthy controls is hardly improvable. Observational studies tend to observe significant differences mostly in levels of certain oxygenated VOC but without resolution required for practical application. Any step towards practicable exploitation of VOC profile differences for illness detection would have to solve current restrictions set by the low and variable VOC concentrations. Further challenges are the technical complexity of studies involving breath sampling and possibly the limited capability of current analytical procedures to detect instable marker candidates.
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.
