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By means of synchrotron X-ray computed tomography, model composites consisting of silica and styrenebutadiene rubber were studied with a very high spatial resolution. By our analysis technique we were able to determine the nano- and micro dispersion from the experimental results by our numerical analysis. These results are discussed with respect to crucial advantages of the generically 3-dimensional tomography technique as compared to 2-dimensional methods. Our quantitative analysis of the 3d images demonstrates that in the case of our model composites the filler dispersion is good, independent on the filler concentration. Our new 3-dimensional results evidence that considering only 2-d pictures may lead to ambiguities that can lead to wrong conclusions.
A surface-labeled lyophilized lymphocyte (sLL) preparation has been developed using human peripheral blood mononuclear cells prelabeled with a fluorescein isothiocyanate conjugated anti-CD4 monoclonal antibody. The sLL preparation is intended to be used as a reference material for CD4+ cell counting including the development of higher order reference measurement procedures and has been evaluated in the pilot study CCQM-P102. This study was conducted across 16 laboratories from eight countries to assess the ability of participants to quantify the CD4+ cell count of this reference material and to document cross-laboratory variability plus associated measurement uncertainties. Twelve different flow cytometer platforms were evaluated using a standard protocol that included calibration beads used to obtain quantitative measurements of CD4+ T cell counts. There was good overall cross-platform and counting method agreement with a grand mean of the laboratory calculated means of (301.7 ± 4.9) µL-1 CD4+ cells. Excluding outliers, greater than 90% of participant data agreed within ±15%. A major contribution to variation of sLL CD4+ cell counts was tube to tube variation of the calibration beads, amounting to an uncertainty of 3.6%. Variation due to preparative steps equated to an uncertainty of 2.6%. There was no reduction in variability when data files were centrally reanalyzed. Remaining variation was attributed to instrument specific differences. CD4+ cell counts obtained in CCQM-P102 are in excellent agreement and show the robustness of both the measurements and the data analysis and hence the suitability of sLL as a reference material for interlaboratory comparisons and external quality assessment.
Adhesion measurements are presented which were carried out with an atomic force microscope between polymer balls attached to a cantilever and a silicon wafer under ultra high vacuum conditions. In using a silicon surface with a defined structure a correlation between adhesion force and contact area was found. This correlation could partly be explained by the Johnson-Kendall-Roberts model, if a change of the surface energy is assumed as a result of the structuring. For a constant geometric contact area an additional structuring leads to a decrease of the adhesion force.
Under the auspices of the Protein Analysis Working Group (PAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) a pilot study, CCQM-P216, was coordinated by the Chinese National Institute of Metrology (NIM), National Research Council of Canada (NRC) and the Bureau International des Poids et Mesures (BIPM). Eleven Metrology Institutes or Designated Institutes and the BIPM participated in the first phase of the pilot study (Part 1). The purpose of this pilot study was to develop measurement capabilities for larger proteins using a recombinant humanized IgG monoclonal antibody against Spike glycoprotein of SARS-CoV-2 (Anti-S IgG mAb) in solution. The first phase of the study was designed to employ established methods that had been previously studies by the CCQM Protein Analysis Working Group, involving the digestion of protein down to the peptide or amino acid level. The global coronavirus pandemic has also led to increased focus on antibody quantitation methods. IgG are among the immunoglobulins produced by the immune system to provide protection against SARS-CoV-2. Anti-SARS-CoV-2 IgG can therefore be detected in samples from affected patients. Antibody tests can show whether a person has been exposed to the SARS-CoV-2, and whether or not they potentially show lasting immunity to the disease. With the constant spread of the virus and the high pressure of re-opening economies, antibody testing plays a critical role in the fight against COVID-19 by helping healthcare professionals to identify individuals who have developed an immune response, either via vaccination or exposure to the virus. Many countries have launched large-scale antibody testing for COVID-19. The development of measurement standards for the antibody detection of SARS-CoV-2 is critically important to deal with the challenges of the COVID-19 pandemic. In this study, the SARS-CoV-2 monoclonal antibody is being used as a model system to build capacity in methods that can be used in antibody quantification. Amino acid reference values with corresponding expanded uncertainty of 36.10 ± 1.55 mg/kg, 38.75 ± 1.45 mg/kg, 18.46 ± 0.78 mg/kg, 16.20 ± 0.67 mg/kg and 30.61 ± 1.30 mg/kg have been established for leucine, valine, phenylalanine, isoleucine and proline, respectively. Agreement between nearly all laboratories was achieved for the amino acid analysis within 2 to 2.5 %, with one participant achieving markedly higher results due to a technical issue found in their procedure; this result was thus excluded from the reference value calculations. The relatively good agreement within a laboratory between different amino acids was not dissimilar to previous results for peptides or small proteins, indicating that factors such as hydrolysis conditions and calibration procedures could be the largest sources of variability. Peptide reference values with corresponding expanded uncertainty of 4.99 ± 0.28 mg/kg and 6.83 ± 0.65 mg/kg have been established for ALPAPIEK and GPSVFPLAPSSK, respectively. Not surprisingly due to prior knowledge from previous studies on peptide quantitation, agreement between laboratories for the peptide-based analysis was slightly poorer at 3 to 5 %, with one laboratory's result excluded for the peptide GPSVFPLAPSSK. Again, this level of agreement was not significantly poorer than that achieved in previous studies with smaller or less complex proteins. To reach the main text of this paper, click on Final Report.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated.
The existence of microbial activity hotspots in temperate regions of Earth is driven by soil heterogeneities, especially the temporal and spatial availability of nutrients. Here we investigate whether microbial activity hotspots also exist in lithic microhabitats in one of the most arid regions of the world, the Atacama Desert in Chile. While previous studies evaluated the total DNA fraction to elucidate the microbial communities, we here for the first time use a DNA separation approach on lithic microhabitats, together with metagenomics and other analysis methods (i.e., ATP, PLFA, and metabolite analysis) to specifically gain insights on the living and potentially active microbial community. Our results show that hypolith colonized rocks are microbial hotspots in the desert environment. In contrast, our data do not support such a conclusion for gypsum crust and salt rock environments, because only limited microbial activity could be observed. The hypolith community is dominated by phototrophs, mostly Cyanobacteria and Chloroflexi, at both study sites. The gypsum crusts are dominated by methylotrophs and heterotrophic phototrophs, mostly Chloroflexi, and the salt rocks (halite nodules) by phototrophic and halotolerant endoliths, mostly Cyanobacteria and Archaea. The major environmental constraints in the organic-poor arid and hyperarid Atacama Desert are water availability and UV irradiation, allowing phototrophs and other extremophiles to play a key role in desert ecology.
The superior properties of high-entropy multi-functional materials are strongly connected with their atomic heterogeneity through many different local atomic interactions. The detailed element-specific studies on a local scale can provide insight into the primary arrangements of atoms in multicomponent systems and benefit to unravel the role of individual components in certain macroscopic properties of complex compounds. Herein, multi-edge X-ray absorption spectroscopy combined with reverse Monte Carlo simulations was used to explore a homogeneity of the local crystallographic ordering and specific structure relaxations of each constituent in the equiatomic single-phase facecentered cubic CrMnFeCoNi high-entropy alloy at room temperature. Within the considered fitting approach, all five elements of the alloy were found to be distributed at the nodes of the fcc lattice without any signatures of the additional phases at the atomic scale and exhibit very close statistically averaged interatomic distances (2.54 – 2.55 Å) with their nearest-neighbors. Enlarged structural displacements were found solely for Cr atoms. The macroscopic magnetic properties probed by conventional magnetometry demonstrate no opening of the hysteresis loops at 5 K and illustrate a complex character of the long-range magnetic order after field-assisted cooling in ± 5 T. The observed magnetic behavior is assigned to effects related to structural relaxations of Cr. Besides, the advantages and limitations of the reverse Monte Carlo approach to studies of multicomponent systems like high-entropy alloys are highlighted.
The maximum magnetisation (saturation magnetisation) obtainable for iron oxide nanoparticles can be increased by doping the nanocrystals with non-magnetic elements such as zinc. Herein, we closely study how only slightly different synthesis approaches towards such doped nanoparticles strongly influence the resulting sub-nano/atomic structure. We compare two co-precipitation approaches, where we only vary the base (NaOH versus NH3), and a thermal decomposition route. These methods are the most commonly applied ones for synthesising doped iron oxide nanoparticles. The measurable magnetisation change upon zinc doping is about the same for all systems. However, the sub-nano structure, which we studied with Mössbauer and X-ray absorption near edge spectroscopy, differs tremendously. We found evidence that a much more complex picture has to be drawn regarding what happens upon Zn doping compared to what textbooks tell us about the mechanism. Our work demonstrates that it is crucial to study the obtained structures very precisely when “playing” with the atomic order in iron oxide nanocrystals.