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Scandium nitride (ScN) has recently attracted much attention for its potential applications in thermoelectric energy conversion, as a semiconductor in epitaxial metal/semiconductor superlattices, as a substrate for GaN growth, and alloying it with AlN for 5G technology. This study was undertaken to better understand its stoichiometry and electronic structure. ScN (100) single crystals 2 mm thick were grown on a single crystal tungsten (100) substrate by a physical vapor transport method over a temperature range of 1900–2000 °C and a pressure of 20 Torr. The core level spectra of Sc 2p3/2,1/2 and N 1s were obtained by x-ray photoelectron spectroscopy (XPS). The XPS core levels were shifted by 1.1 eV toward higher values as the [Sc]:[N] ratio varied from 1.4 at 1900 °C to ∼1.0 at 2000 °C due to the higher binding energies in stoichiometric ScN. Angle-resolved photoemission spectroscopy measurements confirmed that ScN has an indirect bandgap of ∼1.2 eV.

 

Biohybrid centimeter-scale robots developed from optoelectronics and optogenetic muscles can be controlled wirelessly. 

Motivated by a possible convergence of terrestrial limbless locomotion strategies ultimately determined by interfacial effects, we show how both 3D gait alterations and locomotory adaptations to heterogeneous terrains can be understood through the lens of local friction modulation. Via an effective-friction modeling approach, compounded by 3D simulations, the emergence and disappearance of a range of locomotory behaviors observed in nature is systematically explained in relation to inhabited environments. Our approach also simplifies the treatment of terrain heterogeneity, whereby even solid obstacles may be seen as high friction regions, which we confirm against experiments of snakes ‘diffracting’ while traversing rows of posts, similar to optical waves. We further this optic analogy by illustrating snake refraction, reflection and lens focusing. We use these insights to engineer surface friction patterns and demonstrate passive snake navigation in complex topographies. Overall, our study outlines a unified view that connects active and passive 3D mechanics with heterogeneous interfacial effects to explain a broad set of biological observations, and potentially inspire engineering design.

 

Atomistic simulation techniques have become an indispensable tool to acquire a fundamental understanding of growth and structural characteristics of two-dimensional (2D) materials of interest, thereby accelerating experimental research in the same field. A new ReaxFF reactive force field presented here is the first comprehensive empirical potential that is explicitly designed to capture the most prominent features of 2D WSe2 solid-phase chemistry, such as defect formation as a function of local geometry and chalcogen chemical potential, vacancy migration and phase transition, thus enabling cost-effective and reliable characterization of 2D WSe2 at large length scales and time scales much longer than what is accessible by first-principles theory. This potential, validated using extensive first-principles energetics data on both periodic and nonperiodic systems and experimental measurements, can accurately describe the mechanochemical coupling between monolayer deformations and vacancy energetics, providing valuable atomistic insights into the morphological evolution of a monolayer in different environments in terms of loading conditions and various concentrations and distributions of defects. Since understanding how growth is affected by the local chemical environment is vital to fabricating efficient and functional atomically thin 2D WSe2, the new ReaxFF description enables investigations of edge-controlled growth of single crystals of 2D WSe2 using reactive environments closely matching experimental conditions at a low computational cost. 

Natural creatures, from fish and cephalopods to snakes and birds, combine neural control, sensory feedback and compliant mechanics to effectively operate across dynamic, uncertain environments. In order to facilitate the understanding of the biophysical mechanisms at play and to streamline their potential use in engineering applications, we present here a versatile numerical approach to the simulation of musculoskeletal architectures. It relies on the assembly of heterogenous, active and passive Cosserat rods into dynamic structures that model bones, tendons, ligaments, fibers and muscle connectivity. We demonstrate its utility in a range of problems involving biological and soft robotic scenarios across scales and environments: from the engineering of millimeter-long bio-hybrid robots to the synthesis and reconstruction of complex musculoskeletal systems. The versatility of this methodology offers a framework to aid forward and inverse bioengineering designs as well as fundamental discovery in the functioning of living organisms.

 

The integration of muscle cells with soft robotics in recent years has led to the development of biohybrid machines capable of untethered locomotion. A major frontier that currently remains unexplored is neuronal actuation and control of such muscle-powered biohybrid machines. As a step toward this goal, we present here a biohybrid swimmer driven by on-board neuromuscular units. The body of the swimmer consists of a free-standing soft scaffold, skeletal muscle tissue, and optogenetic stem cell-derived neural cluster containing motor neurons. Myoblasts embedded in extracellular matrix self-organize into a muscle tissue guided by the geometry of the scaffold, and the resulting muscle tissue is cocultured in situ with a neural cluster. Motor neurons then extend neurites selectively toward the muscle and innervate it, developing functional neuromuscular units. Based on this initial construct, we computationally designed, optimized, and implemented light-sensitive flagellar swimmers actuated by these neuromuscular units. Cyclic muscle contractions, induced by neural stimulation, drive time-irreversible flagellar dynamics, thereby providing thrust for untethered forward locomotion of the swimmer. Overall, this work demonstrates an example of a biohybrid robot implementing neuromuscular actuation and illustrates a path toward the forward design and control of neuron-enabled biohybrid machines. 

Motivated by the unexplored potential of in vitro neural systems for computing and by the corresponding need of versatile, scalable interfaces for multimodal interaction, an accurate, modular, fully customizable, and portable recording/stimulation solution that can be easily fabricated, robustly operated, and broadly disseminated is presented. This approach entails a reconfigurable platform that works across multiple industry standards and that enables a complete signal chain, from neural substrates sampled through micro‐electrode arrays (MEAs) to data acquisition, downstream analysis, and cloud storage. Built‐in modularity supports the seamless integration of electrical/optical stimulation and fluidic interfaces. Custom MEA fabrication leverages maskless photolithography, favoring the rapid prototyping of a variety of configurations, spatial topologies, and constitutive materials. Through a dedicated analysis and management software suite, the utility and robustness of this system are demonstrated across neural cultures and applications, including embryonic stem cell‐derived and primary neurons, organotypic brain slices, 3D engineered tissue mimics, concurrent calcium imaging, and long‐term recording. Overall, this technology, termed “mind in vitro” to underscore the computing inspiration, provides an end‐to‐end solution that can be widely deployed due to its affordable (>10× cost reduction) and open‐source nature, catering to the expanding needs of both conventional and unconventional electrophysiology.