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Time courses of HBCD levels and enantiomeric signatures in herring gull eggs from the German coast
(2010)
Thermo-Desorption Gas Chromatography-Mass Spectrometry (TD-GC-MS) was used to investigate the thermal degradation of two different polyurethanes (PU). PU samples were heated at different heating rates and the desorbed products were collected in a cold injection system and thereafter submitted to GC-MS. Prospects and limitations of the detection and quantification of semi-volatile degradation products were investigated. A temperature dependent PU depolymerization was
found at temperatures above 200 °C proved by an increasing release of 1,4-butanediol and methylene diphenyl diisocyanate (MDI) representing the main building blocks of both polymers. Their release was monitored quantitatively based on external calibration with authentic compounds. Size Exclusion Chromatography (SEC) of the residues obtained after thermodesorption confirmed the initial competitive degradation mechanism indicating an equilibrium of crosslinking and depolymerization as previously suggested. Matrix-Assisted Laser Desorption Ionization (MALDI) mass spectrometry of SEC fractions of thermally degraded PUs provided additional hints on degradation mechanism.
The validation of a robust quantification procedure for EtG in hair using GC–NCI–MS is presented. Aqueous extraction is followed by complete lyophylization of the extract and derivatization with pentafluoropropionic anhydride (PFPA) under controlled temperature and duration. Clean-up of extracts was dispensable and standard single quadrupole MS displayed sufficient selectivity and sensitivity. The method displayed a wide linearity range and enabled LOD of 0.68 pg/mg, LOQ of 2.4 pg/mg, and precision below 8.12%. Since EtG was seen to display prolonged stability in the aqueous extracts and after derivatization with PFPA this straightforward procedure allows a routine throughput of large quantities of samples with little proneness to procedural scatter of results. The method was applied to demonstrate the homogeneity of two hair reference materials with mean EtG contents of 8.48 pg/mg and 22.0 pg/mg. Aside from the application in homogeneity studies of hair reference materials predominantly in the concentration range of 10–50 pg/mg the method was also designed for daily routine quantification of real-world sample with regard to drinking behavior assessment.
Levels of α-, β-, and γ-hexabromocyclododecane (HBCD) were determined in pooled eggs from herring gulls (Larus argentatus) sampled on three bird sanctuaries near the German North Sea coast between 1988 and 2008 (Mellum and Trischen) and the German Baltic Sea coast between 1998 and 2008 (Heuwiese) and archived by the German Environmental Specimen Bank. Pressurized fluid extraction, gel permeation chromatography, and LCMS/MS using 13C12-labelled isotope standards and a chiral column were applied. α-HBCD was the dominating diastereomer and ranged between 3.7 and 107 ng g-1 lw while β- and γ-HBCD were throughout close to LOQ. The highest α-HBCD concentration was found in eggs from Mellum sampled in the year 2000. Interestingly, HBCD in eggs from the three islands displayed similar time courses with levels increasing to a peak contamination around 2000 and decreasing levels ever since. Chiral signatures of α-HBCD in eggs differed among the islands but indicated a preferential enrichment of the first eluting enantiomer (-)-α-HBCD.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated.
Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
The interconversion of the stereoisomers contained in technical 1,2,5,6,9,10-hexabromocyclododecane, a major brominated flame retardant increasingly found in the environment and in biota, was investigated at elevated temperatures. The application of pure enantiomers of the three constituents α-, β-, and γ-HBCD enabled the unambiguous elucidation of the individual isomerisation reactions as well as the quantification of all respective rate constants. At 160 °C the rate constants range over two orders of magnitude from 1.50 × 10-3 to 1.88 × 10-5 mol(%) s-1. A preliminary mechanistic explanation for the differences of the rate constants which govern the composition of HBCD diastereomers at equilibrium is given.
In this work we focus on our most recent studies to super resolution (SR) laser thermography. The goal of SR nondestructive testing methods is to facilitate the separation of closely spaced defects. We explain how to combine laser scanning with SR techniques. It can be shown that stepwise as well as continuous scanning techniques are applicable. Finally, we discuss the effect of experimental parameters and im-age processing techniques to find the optimal SR technique which leads to the highest reconstruction quality within laser thermography.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2020)
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. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data 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 than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
A method for the determination of the novel brominated flame retardant tetrabromobisphenol A bis(2,3-dibromopropylether), 1,1'-(isopropylidene)bis[3,5-dibromo-4-(2,3-dibromo-propoxy)-benzene] (TBBPA-dbpe), was developed. Technical TBBPA-dbpe was purified and the results of a thorough physical characterisation are reported. The application of APCI-MS is discussed and the fragmentation patterns are described. Quantification of TBBPA-dbpe was done by HPLC-DAD using external calibration. The validation of the method was accomplished using sediment and sewage sludge samples spiked with defined amounts of authentic TBBPA-dbpe. The average recovery rates of TBBPA-dbpe from spiked samples ranged from 35 to 91% (sediment) and from 57 to 98% (sewage sludge) depending on the respective extraction method. Pressurised fluid extraction (PFE) and fluidised bed extraction were superior to classical Soxhlet and sonication procedures and yielded recovery rates between 90 and 98% with relative standard deviations of 2%. The limits of detection (DTC), identification (ID) and determination (DTM) using HPLC-DAD were 10, 21 and 30 ng g-1 in sediment and 22, 44 and 72 ng g-1 in sewage sludge, respectively.