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Formation Mechanism of a Nano-Ring of Bismuth Cations and Mono-Lacunary Keggin-Type Phosphomolybdate
(2022)
A new hetero-bimetallic polyoxometalate (POM) nano-ring was synthesized in a one-pot procedure. The structure consists of tetrameric units containing four bismuth-substituted monolacunary Keggin anions including distorted [BiO8] cubes. The nano-ring is formed via self-assembly from metal precursors in aqueous acidic medium. The compound (NH4)16[(BiPMo11O39)4] ⋅ 22 H2O; (P4Bi4Mo44) was characterized by single-crystal X-ray diffraction, extended X-ray absorption fine structure spectroscopy (EXAFS), Raman spectroscopy, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry (MALDI-TOF), and thermogravimetry/differential scanning calorimetry mass spectrometry (TG-DSC-MS). The formation of the nano-ring in solution was studied by time-resolved in situ small- and wide-angle X-ray scattering (SAXS/WAXS) and in situ EXAFS measurements at the Mo−K and the Bi−L3 edge indicating a two-step process consisting of condensation of Mo-anions and formation of Bi−Mo-units followed by a rapid self-assembly to yield the final tetrameric ring structure.
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.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
Lake Magadi, East African Rift Valley, is a hyperalkaline and saline soda lake highly enriched in Na+, K+, CO32–, Cl–, HCO3–, and SiO2 and depleted in Ca2+ and Mg2+, where thick evaporite deposits and siliceous sediments have been forming for 100 000 years. The hydrogeochemistry and the evaporite deposits of soda lakes are subjects of growing interest in paleoclimatology, astrobiology, and planetary sciences. In Lake Magadi, different hydrates of sodium carbonate/bicarbonate and other saline minerals precipitate. The precipitation sequence of these minerals is a key for understanding the hydrochemical evolution, the paleoenvironmental conditions of ancient evaporite deposits, and industrial crystallization. However, accurate determination of the precipitation sequence of these minerals was challenging due to the dependency of the different hydrates on temperature, water activity, pH and pCO2, which could induce phase transformation and secondary mineral precipitation during sample handling. Here, we report a comprehensive methodology applied for monitoring the evaporitic mineral precipitation and hydrochemical evolution of Lake Magadi. Evaporation and mineral precipitations were monitored by using in situ video microscopy and synchrotron X-ray diffraction of acoustically levitated droplets. The mineral patterns were characterized by ex situ Raman spectroscopy, X-ray diffraction, and scanning electron microscopy. Experiments were coupled with thermodynamic models to understand the evaporation and precipitation-driven hydrochemical evolution of brines. Our results closely reproduced the mineral assemblages, patterns, and textural relations observed in the natural setting. Alkaline earth carbonates and fluorite were predicted to precipitate first followed by siliceous sediments. Among the salts, dendritic and acicular trona precipitate first via fractional crystallization─reminiscent of grasslike trona layers of Lake Magadi. Halite/villiaumite, thermonatrite, and sylvite precipitate sequentially after trona from residual brines depleted in HCO3–. The precipitation of these minerals between trona crystals resembles the precipitation process observed in the interstitial brines of the trona layers. Thermonatrite precipitation began after trona equilibrated with the residual brines due to the absence of excess CO2 input. We have shown that evaporation and mineral precipitation are the major drivers for the formation of hyperalkaline, saline, and SiO2-rich brines. The discrepancy between predicted and actual sulfate and phosphate ion concentrations implies the biological cycling of these ions. The combination of different in situ and ex situ methods and modeling is key to understanding the mineral phases, precipitation sequences, and textural relations of modern and ancient evaporite deposits. The synergy of these methods could be applicable in industrial crystallization and natural brines to reconstruct the hydrogeochemical and hydroclimatic conditions of soda lakes, evaporite settings, and potentially soda oceans of early Earth and extraterrestrial planets.
Due to the diffusive nature of heat propagation in solids, the detection and resolution of internal defects with active thermography based non-destructive testing is commonly limited to a defect-depth-to-defect-size ratio greater than or equal to one. In the more recent past, we have already demonstrated that this limitation can be overcome by using a spatially modulated illumination source and photothermal super resolution-based reconstruction. Furthermore, by relying on compressed sensing and computational imaging methods we were able to significantly reduce the experimental complexity to make the method viable for investigating larger regions of interest. In this work we share our progress on improving the defect/inhomogeneity characterization using fully 2D spatially structured illumination patterns instead of scanning with a single laser spot. The experimental approach is based on the repeated blind pseudo-random illumination using modern projector technology and a high-power laser. In the subsequent post-processing, several measurements are then combined by taking advantage of the joint sparsity of the defects within the sample applying 2D-photothermal super resolution reconstruction. Here, enhanced nonlinear convex optimization techniques are utilized for solving the underlying ill-determined inverse problem for typical simple defect geometries. As a result, a higher resolution defect/inhomogeneity map can be obtained at a fraction of the measurement time previously needed.
Additively manufactured (and in particular laser powder bed fused) materials represent a manyfold challenge for the materials scientist and engineer because of their distinctive microstructure. If laser powder bed fusion is used to produce components, the complexity level increases because meso-structures (e.g., overhanging features, surface and internal defects) gain importance. Furthermore, if the main advantage of additive manufacturing, i.e., the freedom of design, is to be fully exploited, and geometrically complex structures, such as lattices, are manufactured, then such structures become meta-materials. This means that the geometry and the materials properties become equally important.
This matryoshka-like (more literary than the dry “multi-scale”) complexity makes the characterization of the residual stress fields by means of diffraction methods so difficult with the current means, that new paradigms are necessary to tackle the challenge.
Indeed, classic open problems acquire an extra layer of difficulty, such that new solutions need to be found and the sometimes-dormant debate needs to be re-opened. Examples include the determination of: a- the unstrained reference: this reference can become location-dependent and needs to be carefully determined; b- the so-called diffraction elastic constants, which becomes immensely challenging since even the single-crystal elastic constants are not known for additively manufactured materials.
On top of this, other problems arise. The determination of the principal axes of stress becomes non-trivial because the hatching strategy sometimes dominates over the sample geometry. Even further, in complex structures, such as lattices, the textbook statement that the strain measurement in six independent directions uniquely identifies the strain tensor becomes simply invalid. The peculiar surface features of additively manufactured materials transform trivial tasks into formidable challenges: the precise alignment of a specimen in a beam or the determination of surface stresses with laboratory X-rays need to be thoroughly re-discussed and lay far from being routine tasks.
In this paper, we will show a few examples of the cases mentioned above. We will demonstrate that sometimes the classic approach works very well, but other times surprising conclusions can be drawn from in-depth studies of the residual stress in additively manufactured materials. In short, we predicate that classic methods cannot be used on additively manufactured materials and structures without a critical evaluation of their validity and application range.
Quality assessment of components produced by metal based additive manufacturing (AM) technologies such as laser powder bed fusion is rising in importance due to the increased use of AM in industrial production. Here, the presence of internal porosity was identified as a limiting factor for the final component quality. The utilization of thermography as an in-situ monitoring technique allows the determination of the part’s thermal history which was found to be connected to the porosity formation [1]. Combining the local thermal information derived from thermography with the porosity information obtained by x-ray micro computed tomography, machine learning algorithms can be utilized to predict the porosity distribution in the part. In this study, a first approach for the prediction of keyhole porosity in a cylindric specimen from AISI 316L stainless steel is presented. It is based on data augmentation using the “SmoteR” algorithm [2] to cure the dataset imbalance and a 1-dimensional convolutional neural network.
[1] C.S. Lough et al., Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared thermal feature porosity probability maps. Journal of Materials Processing Technology, 302, p. 117473 (2022)
https://dx.doi.org/10.1016/j.imatprotec.2021.117473
[2] L. Torgo et al., SMOTE for Regression. Progress in Artificial Intelligence, Chapter 33, p. 378-289 (2013)
https://dx.doi.org/10.1007/978-3-642-40669-0_33
Atmospheric pressure plasmas interact in various physical ways with their surroundings. They release heat and generate charge carriers, which leads to two effects. The first effect is the generation of acoustic waves due to heat release, known as the thermoacoustic effect, and the second effect is the perturbation of the resting fluid provoked by the release of charge carriers, called “ionic wind”. The direct connection between the charge carrier production of the discharge arrangement and the surroundings also allows the detection of acoustic waves by tracking the electrical current of the arrangement.
This contribution introduces a multi-fluid model approach to describe the acoustic interaction of atmospheric plasmas. In addition, we present experimental results on commercially available and in-house fabricated discharge arrangements for either emitting or receiving acoustic waves.
The molecular ruby analogue [Cr(ddpd)2]3+ (ddpd=N,N’-dimethyl-N,N’-dipyridine-2-ylpyridine-2,6-diamine) exhibits near infrared (NIR) emission with a high photoluminescence (PL) quantum yield ΦPL of 11 % and a lifetime of 898 μs in deaerated water at room temperature. While ligand-based control of the photophysical properties has received much attention, influences of the counter anions and microenvironment are still underexplored. In this study, the luminescence properties of the molecular ruby were systematically examined for the counter anions Cl−, Br−, [BF4]−, [PF6]−, [BPh4]−, and [BArF24]− in acetonitrile (MeCN) solution, in crystals, and embedded into polystyrene nanoparticles (PSNP). Stern-Volmer analyses of the oxygen quenching studies in the intensity and lifetime domain showed the highest oxygen sensitivity of the complexes with the counter anions of [BF4]− and [BArF24]−, which also revealed the longest luminescence lifetimes. Embedding [Cr(ddpd)2][PF6]3 in PSNPs and shielding with poly(vinyl alcohol) yields a strongly NIR-emissive oxygen-insensitive material with a record ΦPL of 15.2 % under ambient conditions.
Sensitized triplet-triplet annihilation upconversion (sTTA-UC) mainly relies on precious metal complexes thanks to their high intersystem crossing (ISC) efficiencies, excited state energies, and lifetimes, while complexes of abundant first-row transition metals were only rarely utilized and with often moderate UC quantum yields. [Cr(bpmp)2]3+ (bpmp = 2,6-bis(2-pyridylmethyl)pyridine) containing earth-abundant chromium possesses an absorption band suitable for green light excitation, a doublet excited state energy matching the triplet energy of 9,10-diphenyl anthracene (DPA), a close to millisecond excited state lifetime, and high photostability. Combined ISC and doublet-triplet energy transfer from excited [Cr(bpmp)2]3+ to DPA gives 3DPA with close-to-unity quantum yield. TTA of 3DPA furnishes greento-blue UC with a quantum yield of 12.0 % (close to the theoretical maximum). Sterically less-hindered anthracenes undergo a [4+4] cycloaddition with [Cr(bpmp)2]3+ and green light.