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Paper des Monats
- ja (71)
To complement the information provided by deterministic seismic imaging at length scales above a certain resolution limit we present the first application of adjoint envelope tomography (AET) to experimental data. AET uses the full envelopes of seismic records including scattered coda waves to obtain information about the distribution of absorption and small-scale heterogeneity which provide complementary information about the investigated medium. Being below the resolution limit this small-scale structure cannot be resolved by conventional tomography but still affects wave propagation by attenuating ballistic waves and generating scattered waves. Using ultrasound data from embedded sensors in a meter-sized concrete specimen we image the distribution of absorption and heterogeneity expressed by the intrinsic quality factor Q−1 and the fluctuation strength ɛ that characterizes the strength of the heterogeneity. The forward problem is solved by modeling the 2-D multiple nonisotropic scattering in an acoustic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. Gradients for the model updates are obtained by convolution with the back-propagated envelope misfit using the adjoint formalism in analogy to full waveform inversion.
We use a late coda time window to invert for absorption and an earlier time window to infer the distribution of heterogeneity. The results successfully locate an area of salt oncrete with increased scattering and concentric anomalies of intrinsic attenuation. The resolution test shows that the recovered anomalies constitute reasonable representations of internal structure of the specimen.
Copper sulfide shows some unique physico-chemical properties that make it appealing as a cathode active material (CAM) for solid-state batteries (SSBs). The most peculiar feature of the electrode reaction is the reversible formation of μm-sized Cu crystals during cycling, despite its large theoretical volume change (75%). Here, the dynamic microstructural evolution of CuS cathodes in SSBs is studied using in situ synchrotron X-ray tomography. The formation of μm-sized Cu within the CAM particles can be clearly followed. This process is accompanied by crack formation that can be prevented by increasing the stack pressure from 26 to 40 MPa. Both the Cu inclusions and cracks show a preferential orientation perpendicular to the cell stack pressure, which can be a result of a z-oriented expansion of the CAM particles during lithiation. In addition, cycling leads to a z-oriented reversible displacement of the cathode
pellet, which is linked to the plating/stripping of the Li counter electrode. The pronounced structural changes cause pressure changes of up to 6 MPa within the cell, as determined by operando stack pressure measurements. Reasons for the reversibility of the electrode reaction are discussed and are attributed to the favorable combination of soft materials.
We investigate the pore space of rock samples with respect to different petrophysical parameters using various methods, which provide data on pore size distributions, including micro computed tomography (µ-CT), mercury Intrusion porosimetry (MIP), nuclear magnetic resonance (NMR), and spectral-induced polarization (SIP). The resulting cumulative distributions of pore volume as a function of pore size are compared. Considering that the methods differ with
regard to their limits of resolution, a multiple-length-scale characterization of the pore space is proposed, that is based on a combination of the results from all of these methods.
The approach is demonstrated using samples of Bentheimer and Röttbacher sandstone. Additionally, we compare the potential of SIP to provide a pore size distribution with other commonly used methods (MIP, NMR). The limits of Resolution of SIP depend on the usable frequency range (between 0.002 and 100 Hz). The methods with similar Resolution show a similar behavior of the cumulative pore volume distribution in the verlapping pore size range. We assume that µ-CT and NMR provide the pore body size while MIP and SIP characterize the pore throat size. Our study Shows that a good agreement between the pore radius distributions can only be achieved if the curves are adjusted considering the resolution and pore volume in the relevant range of pore radii. The MIP curve with the widest range in Resolution should be used as reference.
Ever more stringent regulations on greenhouse gas emissions from transportation motivate efforts to revisit materials used for vehicles. High-strength aluminium alloys often used in aircrafts could help reduce the weight of automobiles, but are susceptible to environmental degradation. Hydrogen ‘embrittlement’ is often indicated as the main culprit; however, the exact mechanisms underpinning failure are not precisely known: atomic-scale analysis of H inside an alloy remains a challenge, and this prevents deploying alloy design strategies to enhance the durability of the materials. Here we performed near-atomic-scale analysis of H trapped in second-phase particles and at grain boundaries in a high-strength 7xxx Al alloy. We used these observations to guide atomistic ab initio calculations, which show that the co-segregation of alloying elements and H favours grain boundary decohesion, and the strong partitioning of H into the second-phase particles removes solute H from the matrix, hence preventing H embrittlement. Our insights further advance the mechanistic understanding of H-assisted embrittlement in Al alloys, emphasizing the role of H traps in minimizing cracking and guiding new alloy design.
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.
AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.
Environmental pollutants like fine particulate matter can cause adverse health effects through oxidative stress and inflammation. Reactive oxygen and nitrogen species (ROS/RNS) such as peroxynitrite can chemically modify proteins, but the effects of such modifications on the immune system and human health are not well understood. In the course of inflammatory processes, the Toll-like receptor 4 (TLR4) can sense damage-associated molecular patterns (DAMPs). Here, we investigate how the TLR4 response and pro-inflammatory potential of the proteinous DAMPs α-Synuclein (α-Syn), heat shock protein 60 (HSP60), and high-mobility-group box 1 protein (HMGB1), which are relevant in neurodegenerative and cardiovascular diseases, changes upon chemical modification with peroxynitrite.
For the peroxynitrite-modified proteins, we found a strongly enhanced activation of TLR4 and the pro-inflammatory transcription factor NF-κB in stable reporter cell lines as well as increased mRNA expression and secretion of the pro-inflammatory cytokines TNF-α, IL-1β, and IL-8 in human monocytes (THP-1). This enhanced activation of innate immunity via TLR4 is mediated by covalent chemical modifications of the studied DAMPs.
Our results show that proteinous DAMPs modified by peroxynitrite more potently amplify inflammation via TLR4 activation than the native DAMPs, and provide first evidence that such modifications can directly enhance innate immune responses via a defined receptor. These findings suggest that environmental pollutants and related ROS/RNS may play a role in promoting acute and chronic inflammatory disorders by structurally modifying the body's own DAMPs. This may have important consequences for chronic neurodegenerative, cardiovascular or gastrointestinal diseases that are prevalent in modern societies, and calls for action, to improve air quality and climate in the Anthropocene.
The ability to track electrode degradation, both spatially and temporally, is fundamental to understand performance loss during operation of lithium batteries. X-ray computed tomography can be used to follow structural and morphological changes in electrodes; however, the direct detection of electrochemical processes related to metallic lithium is difficult due to the low sensitivity to the element. In this work, 4-dimensional neutron computed tomography, which shows high contrast for lithium, is used to directly quantify the lithium diffusion process in spirally wound Li/SOCl2 primary cells. The neutron dataset enables the quantification of the lithium transport from the anode and the accumulation inside the SOCl2 cathode to be locally resolved. Complementarity between the collected neutron and X-ray computed tomographies is shown and by applying both methods in concert we have observed lithium diffusion blocking by the LiCl protection layer and identified all cell components which are difficult to distinguish using one of the methods alone.
The understanding of dynamic processes in Li-metal batteries is an important consideration to enable the full capacity of cells to be utilised. These processes, however, are generally not directly observable using X-ray techniques due to the low attenuation of Li; and are challenging to visualise using neutron imaging due to the low temporal resolution of the technique. In this work, complementary X-ray and neutron imaging are combined to track the dynamics of Li within a primary Li/SOCl2 cell. The temporal challenges posed by neutron imaging are overcome using the golden ratio imaging method which enables the identification of Li diffusion in operando. This combination of techniques has enabled an improved understanding of the processes which limit rate performance in Li/SOCl2 cells and may be applied beyond this chemistry to other Li-metal cells.
In recent years, low-temperature polymer electrolyte fuel cells have become an increasingly important pillar in a zero-carbon strategy for curbing climate change, with their potential to power multiscale stationary and mobile applications. The performance improvement is a particular focus of research and engineering roadmaps, with water management being one of the major areas of interest for development. Appropriate characterisation tools for mapping the evolution, motion and removal of water are of high importance to tackle shortcomings.
This article demonstrates the development of a 4D high-speed neutron imaging technique, which enables a quantitative analysis of the local water evolution. 4D visualisation allows the time-resolved studies of droplet formation in the flow fields and water quantification in various cell parts. Performance parameters for water management are identified that offer a method of cell classification, which will, in turn, support computer modelling and the engineering of next-generation flow field designs.