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Abstract Exploring advanced cathode materials that have high electrochemical activity and superior thermal compatibility with other components is of great importance for the application of intermediate-temperature solid oxide fuel cells. In the present study, influences of Ca doping on electrochemical and physicochemical properties of NdBa 1-x Ca x CoCuO 5+δ were studied systematically. A relatively low thermal expansion coefficient (16.8 × 10−6 °C−1) was obtained for the Ca-doped sample over the temperature range of 25–800 °C. Moreover, Ca doping significantly enhanced the electrochemical performance of NdBa 1-x Ca x CoCuO 5+δ. The highest densities of power for NdBa 1-x Ca x CoCuO 5+δ -based single cells at the temperatures of 800 °C increased from 1.42 W cm−2 for x = 0.0 to 1.84 W cm−2 for x = 0.3. This increase in electrochemical performance of NdBa 1-x Ca x CoCuO 5+δ cathode with Ca doping can be ascribed to enhanced concentrations of oxygen vacancies and higher electrical conductivity. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely used in various emerging fields due to their large specific surface area, high porosity and tunable pore size. Its excellent chemical tunability provides a wide material space, in which tens of thousands of MOFs have been synthesized. However, it is impossible to explore such a vast chemical space through trial-and-error methods, making it difficult to achieve custom design of high-performance MOFs for specific applications. Machine learning (ML) is a powerful tool for guiding materials design and preparation by mining the hidden knowledge in data, and can even make prediction of material properties in seconds. This review aims to provide readers with a new perspective on how ML has been changing the research and development paradigm of MOFs. The four main data sources for MOFs and how to select the suitable features (descriptors) are firstly presented to enable the reader to quickly acquire data and carry out machine learning. Moreover, the application of ML in the development of MOFs is highlighted from the perspectives of performance prediction, rational design and intelligent synthesis. Finally, the future challenges and opportunities of combining ML with MOFs from the points of view of data and algorithms are proposed. This review will provide instructive guidance for ML-assisted MOFs research. [Display omitted] • A comprehensive literature review on a new paradigm of MOFs research is reported. • The current main data sources and descriptors of MOFs are classified and discussed. • Further challenges are proposed to provide direction for the development of machine learning in MOFs. [ABSTRACT FROM AUTHOR]

CoCrMo alloys have been widely used in metal-on-metal (MoM) hip replacements due to their superior wear and corrosion resistance properties. However, metal ions like Co 2+ and Cr 3+ , or even Cr 6+ released from CoCrMo hip prostheses can induce macrophage apoptotic vs. necrotic mortality and damage the surrounding tissues. Simultaneously, osteolysis induced by the wear debris can be a cause of failure. Nano wear debris is more active than the bulk material, due to its small size. In this study, to accurately analyse the fresh wear debris retrieved from the hip simulator and the interaction between the particles and tribocorrosion of CoCrMo, wear debris was observed without protein digest, using a combined experimental approach involving the employment of TEM and ICP-MS. The results suggest that nanoscale wear debris generated from a hip simulator in bovine serum albumin (BSA) lubrication was Cr-rich, containing crystalline and amorphous structures; meanwhile, without any proteins, the wear particles mostly had an hcp-Co crystalline structure. [ABSTRACT FROM AUTHOR]

The study of piezocatalysis has become an important topic in piezoelectric research, especially for addressing environmental issues. However, the reliance on nanofabrication seriously hinders investigating materials with complex compositional design for higher performance and large-scale application. In this work, we use a machine learning strategy to efficiently sample the vast compositional space for ceramic powders with excellent piezoelectric response that we expect to impact piezocatalytic performance. The ceramics, synthesized by solid state reaction methods, belong to the multi-component system (Ba 1−x−y Ca x Sr y)(Ti 1−u−v−w Zr u Sn v Hf w)O 3 with target property d 33, the piezoelectric coefficient. The highest d 33 tends to occur within the phase boundary region with coexisting rhombohedral, orthorhombic and tetragonal phases, especially on the rhombohedral phase side. We select (Ba 0.95 Ca 0.05)(Ti 0.9 Sn 0.1)O 3 as it combines a relatively large d 33 with the fewest number of elements. Its sintered ceramic exhibits a high d 33 of 605 ± 14 pC/N, consistent with the machine learning prediction 633 ± 70 pC/N. The mechanically-ground ceramic powders have an excellent piezocatalytic activity with a degradation rate of (2.16 ± 0.28) × 10−2 min−1 for RhB dye solution, comparable to the performance of previously reported nanoparticles. Our work provides further insight into the nature of piezoelectricity in BaTiO 3 based ceramics, and affords an effective strategy for searching for superior piezocatalysts suitable for large-scale applications. We use machine learning to accelerate the discovery of ceramic particles prepared by conventional solid-state reaction method with high piezocatalytic performance in the multi-component ferroelectric system (Ba 1−x−y Ca x Sr y)(Ti 1−u−v−w Zr u Sn v Hf w)O 3. After establishing a regression model as a surrogate model, we combine the predicted values and the associated uncertainty of the model to guide the search for compositions with high d 33 in the vast search space. When the d 33 predictions are mapped in the phase diagram, the meaning of the composition-property relationship becomes very clear: a relatively large d 33 within the MPB region with the rhombohedral phase to one side. Then we select the system (Ba 0.95 Ca 0.05)(Ti 0.9 Sn 0.1)O 3 as it combines a large d 33 with the fewest number of elements. Its sintered bulk ceramic exhibits a relatively high d 33 of 620 pC/N and a piezocatalytic activity with a degradation rate of 24.5 × 10−3 min−1 for the RhB dye solution. [Display omitted] • Machine learning accelerated composition design towards superior piezocatalytic performance in BaTiO 3 -based ceramic powders. • Data mining reveals the highest d 33 occurs within the phase boundary region, especially on the rhombohedral phase side. • (Ba 0.95 Ca 0.05)(Ti 0.9 Sn 0.1)O 3 ceramic exhibits a highest degradation rate of 24.5 × 10−3 min−1 for RhB dye. [ABSTRACT FROM AUTHOR]

We have studied in detail the growth of Bi-2212 ribbon-like thin film by melting Bi-2212 powders dispersed on flat, sputter-deposited Ag(0 0 1) films with order of tens of nm root-mean-square surface roughness. The formation processes of Bi-2212 ribbon-like thin films were studied by in situ high-temperature microscope observations. Because the powders melt incongruently, the liquid phase disperses with residual solid phases on the substrate. The residual solid phases act as the barrier for the melting phase to diffuse. Nearly monophasic Bi-2212 with excellent c-axis orientation in these thin films, proved by X-ray diffraction results, is the result of shortened diffusion length of the liquid phase. These techniques can be used especially to synthesize high quality Bi-based superconducting thin films for intrinsic Josephson junction devices. [Copyright &y& Elsevier]

This work analyzed the role of unique cell structures in hydrogen embrittlement of additively manufactured maraging steel. For comparison, some as built samples were solution treated to eliminate cell structures, producing the typical martensite where dislocations randomly distributed. By contrast, the cell structures constituted by dislocation tangles in as built samples introduced more hydrogen traps and delayed hydrogen diffusion, but caused much higher HE susceptibility, because the hydrogen accumulation around cell walls created locally hydrogen-enriched environment for the grain boundaries closely adjacent to cell walls, increasing the risk of intergranular cracking. Hydrogen susceptibility was markedly reduced after eliminating cell structures. • Cell walls deteriorated the hydrogen environment of grain boundaries closely nearby. • Eliminating cell walls by solution treatment markedly improved hydrogen resistance. • Cell structures improved strength and uniform deformation ability in as built state. • Hydrogen diffusion processes were numerically simulated for deep cognizance. [ABSTRACT FROM AUTHOR]