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Organisationseinheit der BAM
Rechargeable sodium–oxygen batteries (NaOBs) are receiving extensive research interests because of their advantages such as ultrahigh energy density and cost efficiency. However, the severe failure of Na metal anodes has impeded the commercial development of NaOBs. Herein, combining in situ synchrotron X-ray computed tomography (SXCT) and other complementary characterizations, a novel electro-chemo-mechanical failure mechanism of sodium metal anode in NaOBs is elucidated. It is visually showcased that the Na metal anodes involve a three-stage decay evolution of a porous Na reactive interphase layer (NRIL): from the initially dot-shaped voids evolved into the spindle-shaped voids and the eventually-developed ruptured cracks. The initiation of this three-stage evolution begins with chemical-resting and is exacerbated by further electrochemical cycling. From corrosion science and fracture mechanics, theoretical simulations suggest that the evolution of porous NRIL is driven by the concentrated stress at crack tips. The findings illustrate the importance of preventing electro-chemo-mechanical degradation of Na anodes in practically
rechargeable NaOBs.
Due to the extremely high specific surface area of nanoparticles and corresponding potential for adsorption, the results of surface analysis can be highly dependent on the history of the particles, particularly regarding sample preparation and storage. The sample preparation method has, therefore, the potential to have a significant influence on the results. This report describes an interlaboratory comparison (ILC) with the aim of assessing which sample preparation methods for ToF-SIMS analysis of nanoparticles provided the most intra- and interlaboratory consistency and the least amount of sample contamination. The BAM reference material BAM-P110 (TiO2 nanoparticles with a mean Feret diameter of 19 nm) was used as a sample representing typical nanoparticles. A total of 11 participants returned ToF-SIMS data,in positive and (optionally) negative polarity, using sample preparation methods of “stick-and-go” as well as optionally “drop-dry” and “spin-coat.” The results showed that the largest sources of variation within the entire data set were caused by adventitious hydrocarbon contamination or insufficient sample coverage, with the spin-coating protocol applied in this ILC showing a tendency toward insufficient sample coverage; the sample preparation method or the participant had a lesser influence on results.
Selective Electron Beam Melting (SEBM) is a promising powder bed Additive Manufacturing technique for near-net-shape manufacture of high-value titanium components. However without post-manufacture HIPing the fatigue life of SEBM parts is currently dominated by the presence of porosity. In this study, the size, volume fraction, and spatial distribution of the pores in model samples have been characterised in 3D, using X-ray Computed Tomography, and correlated to the process variables. The average volume fraction of the pores (b0.2%) was measured to be lower than that usually observed in competing processes, such as selective laser melting, but a strong relationship was found with the differentbeamstrategies used to contour ,and infill by hatching, a part section. The majority of pores were found to be small spherical gas pores, concentrated in the infill hatched region; this was attributed to the lower energy density and less focused beam used in the infill strategy allowing less opportunity for gas bubbles to escape the melt pool. Overall, increasing the energy density or focus of the beam was found to correlate strongly to a reduction in the level of gas porosity. Rarer irregular shaped pores were mostly located in the contour region and have been attributed to a lack of fusion between powder particles.
The tribological performance of short glass fibers (SGF),solid lubricants and silica nanoparticles filled epoxy (EP) composites was investigated under oil lubrication conditions. It is demonstrated that the addition of SGF greatly reduces the friction and wear of EP. However, further addition of solid lubricants and silica nanoparticles does not change obviously the friction and wear. It is identified that the high tribological performance of SGF reinforced EP is related to the high load carrying capacity and abrasion resistance of SGF. The nanostructure of the tribofilm was comprehensively characterized. It is deemed that the tribofilm plays an important role in the tribological performance by avoiding the direct rubbing of the sliding pairs exposed to boundary and mixed lubrication conditions.
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.
Constant interactions between tumor cells and the extracellular matrix (ECM) influence the progression of prostate cancer (PCa). One of the key components of the ECM are collagen fibers, since they are responsible for the tissue stiffness, growth, adhesion, proliferation, migration, invasion/metastasis, cell signaling, and immune recruitment of tumor cells. To explore this molecular marker in the content of PCa, we investigated two different tumor volumes (500 mm3 and 1000 mm3) of a xenograft mouse model of PCa with molecular magnetic resonance imaging (MRI) using a collagen-specific probe. For in vivo MRI evaluation, T1-weighted sequences before and after probe administration were analyzed. No significant signal difference between the two tumor volumes could be found. However, we detected a significant difference between the signal intensity of the peripheral tumor area and the central area of the tumor, at both 500 mm3 (p < 0.01, n = 16) and at 1000 mm3 (p < 0.01, n = 16). The results of our histologic analyses confirmed the in vivo studies: There was no significant difference in the amount of collagen between the two tumor volumes (p > 0.05), but within the tumor, higher collagen expression was observed in the peripheral area compared with the central area of the tumor. Laser ablation with inductively coupled plasma mass spectrometry further confirmed these results. The 1000 mm3 tumors contained 2.8 +- 1.0% collagen and the 500 mm3 tumors contained 3.2 +- 1.2% (n = 16). There was a strong correlation between the in vivo MRI data and the ex vivo histological data (y = 0.068x + 1.1; R2 = 0.74) (n = 16). The results of elemental analysis by inductively coupled plasma mass spectrometry supported the MRI data (y = 3.82x + 0.56; R2 = 0.79; n = 7). MRI with the collagen-specific probe in PCa enables differentiation between different tumor areas. This may help to differentiate tumor from healthy tissue, potentially identifying tumor areas with a specific tumor biology.