Because of its unique mechanical, chemical, and biological properties, 3D-printed polyether ether ketone (PEEK) has great potential as customized bone replacement and other metal alloy implant replacement. PEEK samples were printed using fused deposition modeling (FDM) and evaluated in terms of their dimensional accuracy, crystallinity, and mechanical properties. Crystallinity and mechanical properties increased with elevated chamber temperature and post-printing annealing. Variations of material properties from three printers are evident. Many factors affect the quality of 3D-printed PEEK. Future FDA regulations for 3D-printed products are needed for this highly customizable manufacturing process to ensure safety and effectiveness for biomedical applications.

A rigorous analysis of yield strength of pure iron over a wide grain size scale, using an extensive compilation of experimental data, indicates that the common Hall–Petch relationship is not obeyed with large deviations at the extremes of grain size. The author proposes here a phenomenological exponential function to represent the grain size effect on strength over multiple length scales. It is shown that the exponential function describes the grain size dependence of strength remarkably well, on the basis of a large set of experimental data for pure Fe. A nonlinear regression analysis indicated that the function provided a very high degree of correlation of data. The validity of the function is also supported by its conformation to physical boundary conditions at the extremes of grain size, that is, by asymptotically reaching the limiting stress for dislocation nucleation at infinitesimal grain size, and, the strength of single crystal at infinite grain size. The exponential form is a significant improvement over the Hall–Petch relationship and may be used as a guide to develop a reliable theory of grain size strengthening of iron.

In this work, hierarchical mesoporous Zn–Ni–Co–S–rGO/NF microspheres have been prepared by hydrothermal, sulfurization, and subsequent calcination process. The effect of different sulfurization time on the morphology and capacitance of composites was tested. The high electrochemical performance of (Zn–Ni–Co–S–rGO/NF) composite was obtained when the sulfurization time was 3 h (Zn–Ni–Co–S–rGO/NF-3h), where a specific capacitance of 627.7 F/g at 0.25 A/g and excellent rate capability of about 97.8% capacitance retention at 2 A/g after 4000 cycles were achieved. Moreover, an asymmetric supercapacitor fabricated by (Zn–Ni–Co–S–rGO/NF-3h) composite and activated carbon (AC) as the positive and the negative electrodes, respectively, showed a high energy density of 75.96 W h/kg at a power density of 362.49 W/kg with a remarkable cycle stability performance of 91.2% capacitance retention over 5000 cycles. This incredible electrochemical behavior illustrates that the hierarchical mesoporous Zn–Ni–Co–S–rGO/N-3h microsphere electrodes are promising electrode materials for application in high-performance supercapacitors.

A new constant contact pressure (CCP) indentation creep method is presented, which is based on keeping the mean contact pressure as defined through Sneddon’s hardness constant, until a steady-state strain rate is achieved. This is in contrast to the conventional constant load–hold (CLH) creep experiments, where the load is held constant and relaxation in both hardness and strain rate occurs at the same time. Besides controlling the mean contact pressure, the dynamic stiffness is furthermore used to assess the indentation depth, thereby minimizing thermal drift influence and pile-up or sink-in effects during long-term experiments. The CCP method has been tested on strain rate sensitive ultrafine-grained (UFG) CuZn30 and UFG CuZn5 as well as on fused silica, comparing the results with those of strain rate jump tests as well as the CLH nanoindentation creep tests. With the CCP method, strain rates from 5 × 10−4 s−1 down to 5 × 10−6 s−1 can be achieved, keeping the mean contact pressure constant over a long period of time, in contrast to the CLH method. The CCP technique thus offers the possibility of performing long-term creep experiments while retaining the contact stress underneath the tip constant.

Machine learning (ML) and artificial intelligence (AI) are quickly becoming commonplace in materials research. In addition to the standard workflow of fitting a model to a large set of data in order to make predictions, the materials community is finding novel and meaningful ways to integrate AI within their work. This has led to an acceleration not only of materials design and discovery, but also of other aspects of materials research as well, including faster computational models, the development of autonomous and intelligent “robot researchers,” and the automatic discovery of physical models. In this issue, we highlight a few of these applications and argue that AI/ML is delivering real-world, practical solutions to materials problems. It is also clear that we need AI/ML methods and models, “dialects” that are better adapted to materials research.

At GE Research, we are combining “physics” with artificial intelligence and machine learning to advance manufacturing design, processing, and inspection, turning innovative technologies into real products and solutions across our industrial portfolio. This article provides a snapshot of how this physical plus digital transformation is evolving at GE.

Atomically resolved imaging of materials enabled by the advent of aberration-corrected scanning transmission electron microscopy (STEM) has become a mainstay of modern materials science. However, much of the wealth of quantitative information contained in the fine details of atomic structure or spectra remains largely unexplored. In this article, we discuss new opportunities enabled by physics-informed big data and machine learning technologies to extract physical information from static and dynamic STEM images, ranging from statistical thermodynamics of alloys to kinetics of solid-state reactions at a single defect level. The synergy of deep-learning image analytics and real-time feedback further allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication. Examples of direct atomic motion over mesoscopic distances, engineered doping at selected lattice sites, and assembly of multiatomic structures are reviewed. These advances position the scanning transmission electron microscope to transition from a mere imaging tool toward a complete nanoscale laboratory for exploring electronic, phonon, and quantum phenomena in atomically engineered structures.

A fundamental design rule that nature has developed for biological machines is the intimate correlation between motion and function. One class of biological machines is molecular motors in living cells, which directly convert chemical energy into mechanical work. They coexist in every eukaryotic cell, but differ in their types of motion, the filaments they bind to, the cargos they carry, as well as the work they perform. Such natural structures offer inspiration and blueprints for constructing DNA-assembled artificial systems, which mimic their functionality. In this article, we describe two groups of cytoskeletal motors, linear and rotary motors. We discuss how their artificial analogues can be built using DNA nanotechnology. Finally, we summarize ongoing research directions and conclude that DNA origami has a bright future ahead.