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Paper des Monats
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An interlaboratory comparison (ILC)was organised to characterise 87Sr/86Sr isotope ratios in geological and industrial reference materials by applying the so-called conventional method for determining 87Sr/86Sr isotope ratios. Four cements (VDZ 100a,VDZ 200a, VDZ 300a, IAG OPC-1), one limestone (IAG CGL ML-3) and one slate (IAG OU-6) reference materials were selected, covering a wide range of naturally occurring Sr isotopic signatures. Thirteen laboratories received aliquots of these six reference materials together with a detailed technical protocol. The consensus values for the six reference materials and their associated measurement uncertainties were obtained by applying a Gaussian, linear mixed effects model fitted to all the measurement results. By combining the consensus values and their uncertainties with an uncertainty contribution for potential heterogeneity, reference values ranging from 0.708134 mol mol-1 to 0.729778 mol mol-1 were obtained with relative expanded uncertainties of ≤ 0.007 %. This study represents an ILC on conventional 87Sr/86Sr isotope ratios, within which metrological principles were considered and the compatibility of measurement results obtained by MC-ICP-MS and by MC-TIMS is demonstrated. The materials characterised in this study can be used as reference materials for validation and quality control purposes and to estimate measurement uncertainties in conventional 87Sr/86Sr isotope ratio measurement.
This study uses conventional 87Sr/86Sr and 143Nd/144Nd isotope and interelement ratios of Ca, Sr, K, Mn, Mg and Ti as fingerprints for provenancing ordinary Portland cements (OPC). Herein, the first database of Sr and Nd isotope ratios investigated in OPCs, stemming from 29 cement plants located worldwide, was created. The results show that the Sr isotope ratios of OPCs are higher than those of seawater from the observed geological period. The spread of 143Nd/144Nd in OPCs is not as large as the spread for 87Sr/86Sr isotope ratios. However, the combination of both Sr and Nd isotope ratios provides the potential for distinguishing between cements of different production sites. Most of the OPCs investigated have measurable differences in their 87Sr/86Sr and 143Nd/144Nd isotope ratios, which can be employed as a valuable analytical fingerprinting tool. In the case of equivocal results, divisive hierarchical clustering was employed to help overcome this issue. The construction of geochemical profiles allowed the computing of suitably defined distances between cements and clustering them according to their chemical similarity. By applying this methodology, successful fingerprinting was achieved in 27 out of the 29 ordinary Portland cements that were analysed.
Per- and polyfluoroalkyl substances (PFAS) are widely used for durable water-repellent finishing of different fabrics and textiles like outdoor clothing, carpets, medical textiles and more. Existing PFAS extraction techniques followed by target analysis are often insufficient in detecting widely used side-chain fluorinated polymers (SFPs) that are barely or non-extractable. SFPs are typically copolymers consisting of a non-fluorinated backbone with perfluoroalkyl side-chains to obtain desired properties. We compared the accessible analytical information and performance of complementary techniques based on oxidation (dTOP assay, PhotoTOP), hydrolysis (THP assay), standard extraction, extractable organic fluorine (EOF), and total fluorine (TF) with five functional textiles and characterized 7 further textiles only by PhotoTOP oxidation. The results show that when applied directly to textile samples, oxidation by dTOP and PhotoTOP and also hydrolysis by the THP are able to capture large fractions of the TF in form of perfluoroalkyl side-chains present in the textiles while methods relying on extracts (EOF, target and non-target analysis) were much lower (e.g., factor ~25-50 lower). The conversion of large fractions of the measured TF into PFCAs or FTOHs from fluorinated side chains is in contrast to previous studies. Concentrations ranged from <LOQ to over ~1000 mg F/kg after oxidation/hydrolysis and <LOQ to over 2000 mg F/kg for TF, while EOF and target PFAS in extracts were detected at much lower concentrations (up to ~ 60 mg F/kg) (amount of fluorine in the order: extraction << EOF << oxidation/hydrolysis ≤ TF). Perfluoroalkyl carboxylic acids (PFCAs) and fluorotelomer alcohols (FTOHs) from THP and PhotoTOP both represented the chain-length distribution in the textiles showing that long-chain SFPs are still used in current textiles. Further advantages and disadvantages of the applied methods are discussed.
Mistle: bringing spectral library predictions to metaproteomics with an efficient search index
(2023)
Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.
Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step.
We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.
Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.
Human factors (HF) are little understood, and particularly in non-destructive testing (NDT) experimental data is rare, samples are often small, and statistical methods are rarely used to evaluate results. HF have been widely implicated in major occurrences of technical failure, for example at North Anna Power Plant and on United Airlines Flight 232. Understanding HF is vital for reliable detection and prevention of failures. Reliability assessments, though known to be affected by intrinsic capability, application factors, and HF, have thus far only concentrated to a sufficient degree on intrinsic capability. The addition of HF to that assessment has proven difficult due to the lack of a method of quantifying HF. This paper presents the first attempt from a psychological perspective to quantify HF from qualitative data.
HF data was derived from qualitative human-oriented Failure Modes and Effects Analysis (Human-FMEA) workshops for visual inspection of tunnels by laser scanning and for ultrasonic testing of welds. Data was collected on human failure modes, causes, consequences and preventive measures, as well as eliciting a risk priority number (RPN). Using this data, a system of quantitative weightings was created to allocate errors to inductively derived HF categories for further allocation to existing HF categorisation models.
This weighting model proved useful for creating quantitative summaries of HF, informing and validating qualitative FMEA results, and comparing existing HF categorisation models. Further potential lies within a planned interface to quantitative reliability assessment methods such as POD (Probability of Detection). While providing quantifications, the method retains a qualitative and holistic nature, can, thus, bridge the gap between psychological and engineering concerns of HF and reliability, contributing to future interdisciplinary work.
Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straight forward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc.
Since its isolation, graphene has received growing attention from academia and industry due to its unique properties. However, the “what is my material” barrier hinders further commercialization. X-ray photoelectron spectroscopy (XPS) is considered as a method of choice for the determination of the elemental and chemical composition. In this work the influence of the morphology of graphene particles on the XPS results is studied and investigated as a function of X-ray energy, using conventional XPS with Al K𝜶 radiation and hard X-ray photoemission spectroscopy (HAXPES) using Cr K𝜶 radiation. Thereby, the information depth is varied between 10 and 30 nm. For this purpose, two commercial powders containing graphene nanoplatelets with lateral dimensions of either ≈100 nm or in the micrometer range are compared. These larger ones exist as stack of graphene layers which is inspected with scanning electron microscopy. Both kinds of particles are then functionalized with either oxygen or fluorine. The size of the graphene particles is found to influence the degree of functionalization. Only the combination of XPS and HAXPES allows to detect the functionalization at the outermost surface of the particles or even of the stacks and to provide new insights into the functionalization process.
Although layer-based additive manufacturing methods such as laser powder bed fusion (PBF-LB) offer an immense geometrical freedom in design, they are typically subject to a build-up of internal stress (i.e. thermal stress) during manufacturing. As a consequence, significant residual stress (RS) is retained in the final part as a footprint of these internal stresses. Furthermore, localized melting and solidification inherently induce columnar-type grain growth accompanied by crystallographic texture. Although diffraction-based methods are commonly used to determine the RS distribution in PBF-LB parts, such features pose metrological challenges in their application. In theory, preferred grain orientation invalidates the hypothesis of isotropic material behavior underlying the common methods to determine RS. In this work, more refined methods are employed to determine RS in PBF-LB/M/IN718 prisms, based on crystallographic texture data. In fact, the employment of direction-dependent elastic constants (i.e. stress factors) for the calculation of RS results in insignificant differences from conventional approaches based on the hypothesis of isotropic mechanical properties. It can be concluded that this result is directly linked to the fact that the {311} lattice planes typically used for RS analysis in nickel-based alloys have high multiplicity and less strong texture intensities compared with other lattice planes. It is also found that the length of the laser scan vectors determines the surface RS distribution in prisms prior to their removal from the baseplate. On removal from the baseplate the surface RS considerably relaxes and/or redistributes; a combination of the geometry and the scanning strategy dictates the sub-surface RS distribution.
Quantitative Analysis and 2D/3D Elemental Imaging of Cocoa Beans Using X‑ray Fluorescence Techniques
(2023)
As an important raw material for the confectionery industry, the cocoa bean (Theobroma cacao L.) has to meet certain legal requirements in terms of food safety and maximum contaminant levels in order to enter the cocoa market. Understanding the enrichment and distribution of essential minerals but also toxic metals is of utmost importance for improving the nutritional quality of this economically important raw food material. We present three X-ray fluorescence (XRF) techniques for elemental bio-imaging of intact cocoa beans and one additional XRF technique for quantitative analysis of cocoa pellets. The interrelation of all the methods presented gives a detailed picture of the content and 3D-resolved distribution of elements in complete cocoa beans for the first time.
The huge atomic heterogeneity of high-entropy materials along with a possibility to unravel the behavior of individual components at the atomic scale suggests a great promise in designing new compositionally complex systems with the desired multi-functionality. Herein, we apply multi-edge X-ray absorption spectroscopy (extended X-ray absorption fine structure (EXAFS), X-ray absorption near edge structure (XANES), and X-ray magnetic circular dichroism (XMCD)) to probe the structural, electronic, and magnetic properties of all individual constituents in the single-phase face-centered cubic (fcc)-structured nanocrystalline thin film of Cr20Mn26Fe18Co19Ni17 (at.%) high-entropy alloy on the local scale. The local crystallographic ordering and component-dependent lattice displacements were explored within the reverse Monte Carlo approach applied to EXAFS spectra collected at the K absorption edges of several constituents at room temperature. A homogeneous short-range fcc atomic environment around the absorbers of each type with very similar statistically averaged interatomic distances (2.54–2.55 Å) to their nearest-neighbors and enlarged structural relaxations of Cr atoms were revealed. XANES and XMCD spectra collected at the L2,3 absorption edges of all principal components at low temperature from the oxidized and in situ cleaned surfaces were used to probe the oxidation states, the changes in the electronic structure, and magnetic behavior of all constituents at the surface and in the sub-surface volume of the film. The spin and orbital magnetic moments of Fe, Co, and Ni components were quantitatively evaluated. The presence of magnetic phase transitions and the co-existence of different magnetic phases were uncovered by conventional magnetometry in a broad temperature range.