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The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g.
Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content.
The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%.
Recent DNA-based studies have shown that the built environment is surprisingly rich in fungi. These indoor fungi – whether transient visitors or more persistent residents – may hold clues to the rising levels of human allergies and other medical and building-related health problems observed globally. The taxo¬nomic identity of these fungi is crucial in such pursuits. Molecular identification of the built mycobiome is no trivial undertaking, however, given the large number of unidentified, misidentified, and technically compromised fungal sequences in public sequence databases. In addition, the sequence metadata required to make informed taxonomic decisions – such as country and host/substrate of collection – are often lacking even from reference and ex-type sequences. Here we report on a taxonomic annotation workshop (April 10–11, 2017) organized at the James Hutton Institute/University of Aberdeen (UK) to facilitate reproducible studies of the built mycobiome. The 32 participants went through public fungal ITS bar¬code sequences related to the built mycobiome for taxonomic and nomenclatural correctness, technical quality, and metadata availability. A total of 19,508 changes – including 4,783 name changes, 14,121 metadata annotations, and the removal of 99 technically compromised sequences – were implemented in the UNITE database for molecular identification of fungi (https://unite.ut.ee/) and shared with a range of other databases and downstream resources. Among the genera that saw the largest number of changes were Penicillium, Talaromyces, Cladosporium, Acremonium, and Alternaria, all of them of significant importance in both culture-based and culture-independent surveys of the built environment.
After some months of operation, a number of PV modules develop a discolouration defect called 'snail trails', or 'snail traces', which appear as irregular dark stripes across the cells. Whereas these traces were soon identified as discoloured silver contacts along the cell edges or at micro cracks, the chemical and mechanistic reasons for this phenomenon have not yet been resolved in detail. In this work we show that silver nanoparticles accumulating within the encapsulation foil cause the brownish discolouration, and that certain additives of encapsulation and back sheet foils trigger the formation of these nanoparticles.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Hydrated actinide(IV) ions undergo hydrolysis and further polymerization and precipitation with increasing pH. The resulting amorphous and partly crystalline oxydydroxides AnOn(OH)4-2n·xH2O can usually be observed as colloids above the An(IV) solubility limit. The aging process of such colloids results in crystalline AnO2. The presence of carboxylates in the solution prevents the occurrence of such colloids by formation of polynuclear complexes through a competing reaction between hydrolysis and ligation. The majority of recently described carboxylates reveals a hexanuclear core of [An6(µ3-O)4(µ3-OH)4]12+ terminated by 12 carboxylate ligands. We found that the An(IV) carboxylate solution species remain often preserved in crystalline state. The An(IV) carboxylates show An–An distances which are ~ 0.03 Å shorter than the An–An distances in AnO2 like colloids. The difference in the distances could be used to identify such species in solution.
After several months of operation, many photovoltaic (PV) modules develop a discolouration defect called snail trails which appear as irregular dark traces across the cells. These traces are caused by silver nanoparticles accumulating within the encapsulation foil directly above the grid finger. In this work we systematically investigate combinations of encapsulation and back-sheet foils with respect to their susceptibility for snail trails. We can show that certain additive compositions within the encapsulation and back sheet foils are critical for the formation of the discolouring silver nanoparticles. We suggest a reaction model explaining the formation of snail trails from a chemical point of view. This fundamental understanding allows the rapid testing of foils for their snail trail sensitivity as well as the special design of resistant foils.
The elucidation of mechanisms underlying the cellular uptake of nanoparticles (NPs) is an important topic in nanotoxicological research. Most studies dealing with silver NP uptake provide only qualitative data about internalization efficiency and do not consider NP-specific dosimetry. Therefore, we performed a comprehensive comparison of the cellular uptake of differently coated silver NPs of comparable size in different human intestinal Caco-2 cell-derived models to cover also the influence of the intestinal mucus barrier and uptake-specialized M-cells. We used a combination of the Transwell system, transmission electron microscopy, atomic absorption spectroscopy, and ion beam microscopy techniques. The computational in vitro sedimentation, diffusion, and dosimetry (ISDD) model was used to determine the effective dose of the particles in vitro based on their individual physicochemical characteristics. Data indicate that silver NPs with a similar size and shape show coating-dependent differences in their uptake into Caco-2 cells. The internalization of silver NPs was enhanced in uptake-specialized M-cells while the mucus did not provide a substantial barrier for NP internalization. ISDD modeling revealed a fivefold underestimation of dose–response relationships of NPs in in vitro assays. In summary, the present study provides dosimetry-adjusted quantitative data about the influence of NP coating materials in cellular uptake into human intestinal cells. Underestimation of particle effects in vitro might be prevented by using dosimetry models and by considering cell models with greater proximity to the in vivo situation, such as the M-cell model.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.