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A low-carbon micro-alloyed (LCMA) steel with a body-centered cubic (bcc) crystal structure suitable for extremely low temperatures was developed by overcoming the intrinsic ductile-to-brittle transition in bcc alloys at cryogenic temperatures. The excellent cryogenic-to-ambient impact toughness in the LCMA rolled plate results from its heterogeneous microstructure, which gradually changes from bamboo-like ultrafine grains (∼ 1.1 μm) on the surface to relatively equiaxed coarse grains in the core (∼ 3.4 μm), accompanied by a distinct texture gradient variation. The heterostructured LCMA steel displays a cryogenic impact toughness of ∼200 J/cm2 at 77 K, which is 24 times higher than the coarse-grained LCMA steel. Such high impact toughness of heterostructured LCMA arises from the coordinated deformation mechanisms over different length-scales coupled with delamination toughening. At 77 K, the heterostructured steel plate deforms by forming cellular sub-structures at the core to the surface, which refines the microstructure and promotes hetero-deformation induced (HDI) hardening to improve intrinsic toughening. Moreover, the subsequent delamination process induces extrinsic toughening by shielding and blunting the cracks, with the local plane-stress conditions induced by delamination promoting ductile fracture of the coarse grains in the core regions. This low alloy steel with its heterogeneous microstructure exhibits extraordinary impact toughness at cryogenic temperatures highlights the possibility of materials design strategies for sustainable development.

Conductive hydrogels have a remarkable potential for applications in soft electronics and robotics, owing to their noteworthy attributes, including electrical conductivity, stretchability, biocompatibility, etc. However, the limited strength and toughness of these hydrogels have traditionally impeded their practical implementation. Inspired by the hierarchical architecture of high-performance biological composites found in nature, we successfully fabricate a robust and sensitive conductive nanocomposite hydrogel through self-assembly-induced bridge cross-linking of MgB2 nanosheets and polyvinyl alcohol hydrogels. By combining the hierarchical lamellar microstructure with robust molecular B─O─C covalent bonds, the resulting conductive hydrogel exhibits an exceptional strength and toughness. Moreover, the hydrogel demonstrates exceptional sensitivity (response/relaxation time, 20 milliseconds; detection lower limit, ~1 Pascal) under external deformation. Such characteristics enable the conductive hydrogel to exhibit superior performance in soft sensing applications. This study introduces a high-performance conductive hydrogel and opens up exciting possibilities for the development of soft electronics.

Strengthening materials via conventional "top-down" processes generally involves restricting dislocation movement by precipitation or grain refinement, which invariably restricts the movement of dislocations away from, or towards, a crack tip, thereby severely compromising their fracture resistance. In the present study, a high-entropy alloy Al0.5CrCoFeNi is produced by the laser powder-bed fusion process, a "bottom-up" additive manufacturing process similar to how nature builds structures, with the microstructure resembling a nano-bridged honeycomb structure consisting of a face-centered cubic (fcc) matrix and an interwoven hexagonal net of an ordered body-centered cubic B2 phase. While the B2 phase, combined with high-dislocation density and solid-solution strengthening, provides strength to the material, the nano-bridges of dislocations connecting the fcc cells, i.e., the channels between the B2 phase on the cell boundaries, provide highways for dislocation movement away from the crack tip. Consequently, the nature-inspired microstructure imparts the material with an excellent combination of strength and toughness.

Molecular weaving is the interlacing of covalently linked threads to make extended structures. Although weaving based on 3D networks has been reported, the 2D forms remain largely unexplored. Reticular chemistry uses mutually embracing tetrahedral metal complexes as crossing points, which, when linked, typically lead to 3D woven structures. Realizing 2D weaving patterns requires crossing points with an overall planar geometry. We show that polynuclear helicates composed of multiple metal-complex units, and therefore multiple turns, are well suited in this regard. By reticulating helicate units, we successfully obtained 2D weaving structures based on the familiar chicken-wire pattern.

The microstructure of high-entropy alloys with refractory elements and Al as constituents can be considered to be analogous to superalloys. These so-termed refractory high-entropy superalloys (RHSAs) can show remarkable compressive strength up to temperatures exceeding 1200 °C. Here, we examine the microstructure and properties - compressive, tensile, and fracture toughness - of a precipitation-hardened, body-centered cubic, RHSA, Al0.5Nb1.25Ta1.25TiZr, at ambient temperature (RT) to 1200 °C. Two dual-phase microstructures comprising ordered B2 (brittle) and disordered A2 (ductile) phases were produced in this alloy - one with B2 as the matrix, the other with A2 - for evaluation of the mechanical properties. Under compression, both microstructures display RT compressive strengths above 1.5 GPa and considerable ductility exceeding 40% at elevated temperatures; the alloy with the A2 matrix has ∼15% compressive ductility even at RT. However, properties are very different under tensile loading; at all temperatures, both microstructures fail predominately in an intergranular mode in the elastic regime at a fracture stress less than 200 MPa and ductility below 0.15%. The microstructure with the A2 matrix has a KIc fracture toughness of ∼15 MPa√m at RT, although at all temperatures above 800 °C, measured KIc values for both dual-phase microstructures are less than 5 MPa√m. In this study, we investigate the microstructural origin of these mechanical properties, and emphasize the importance of evaluating these alloys in tension.

Local chemical ordering (LCO) in the CrCoNi medium-entropy alloy was investigated by transmission electron microscopy (TEM) after different annealing treatments and their corresponding mechanical properties by bulk tensile tests and nanoindentation. A cold-rolled alloy was annealed at 1000°C for 0.5 h followed by ice water quenching and then aged at a number of different temperatures (600°C, 700°C, 800°C, 900°C, and 1000°C) under vacuum for 240 h to generate different degrees of chemical ordering. A splat-quenched sample rapidly cooled from the liquid phase was also examined. While bulk mechanical properties did not vary among samples with equivalent grain sizes, nanoindentation tests revealed notable differences. As indicated by the load at first pop-in using a Berkovich tip or the indentation yield strength via continuous stiffness measurements using a 10 μm spherical tip, the nanoindentation tests revealed that the stress for onset of plasticity during indentation varied with heat treatment and peaked in the 900°C aged sample. Energy-filtered TEM characterization indicated the presence of ordering in all specimens, with a higher degree of LCO in the aged samples relative to the splat-quenched and 1000°C-quenched samples. The evolution of LCO during aging was determined to occur on the time scale similar to those of bulk diffusion. The difference in nanoindentation strength was attributed to the difference in dislocation nucleation barriers imposed by different degrees of LCO.

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress-strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.