Exploratory synthesis in new chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is ...challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation-from which we experimentally realized seven new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity and covalency of solid-state bonding from the density functional theory (DFT)-computed electron density, we reveal the complex interplay between chemistry, composition and electronic structure in governing large-scale stability trends in ternary nitride materials.
We describe the design, fabrication, and calibration of a highly compliant artificial skin sensor. The sensor consists of multilayered mircochannels in an elastomer matrix filled with a conductive ...liquid, capable of detecting multiaxis strains and contact pressure. A novel manufacturing method comprised of layered molding and casting processes is demonstrated to fabricate the multilayered soft sensor circuit. Silicone rubber layers with channel patterns, cast with 3-D printed molds, are bonded to create embedded microchannels, and a conductive liquid is injected into the microchannels. The channel dimensions are 200 μm (width) × 300 μm (height). The size of the sensor is 25 mm × 25 mm, and the thickness is approximately 3.5 mm. The prototype is tested with a materials tester and showed linearity in strain sensing and nonlinearity in pressure sensing. The sensor signal is repeatable in both cases. The characteristic modulus of the skin prototype is approximately 63 kPa. The sensor is functional up to strains of approximately 250%.
Hydrothermal synthesis is challenging in metal oxide systems with diverse polymorphism, as reaction products are often sensitive to subtle variations in synthesis parameters. This sensitivity is ...rooted in the non-equilibrium nature of low-temperature crystallization, where competition between different metastable phases can lead to complex multistage crystallization pathways. Here, we propose an ab initio framework to predict how particle size and solution composition influence polymorph stability during nucleation and growth. We validate this framework using in situ X-ray scattering, by monitoring how the hydrothermal synthesis of MnO
proceeds through different crystallization pathways under varying solution potassium ion concentrations (K
= 0, 0.2, and 0.33 M). We find that our computed size-dependent phase diagrams qualitatively capture which metastable polymorphs appear, the order of their appearance, and their relative lifetimes. Our combined computational and experimental approach offers a rational and systematic paradigm for the aqueous synthesis of target metal oxides.
We describe the design and control of a wearable robotic device powered by pneumatic artificial muscle actuators for use in ankle-foot rehabilitation. The design is inspired by the biological ...musculoskeletal system of the human foot and lower leg, mimicking the morphology and the functionality of the biological muscle-tendon-ligament structure. A key feature of the device is its soft structure that provides active assistance without restricting natural degrees of freedom at the ankle joint. Four pneumatic artificial muscles assist dorsiflexion and plantarflexion as well as inversion and eversion. The prototype is also equipped with various embedded sensors for gait pattern analysis. For the subject tested, the prototype is capable of generating an ankle range of motion of 27° (14° dorsiflexion and 13° plantarflexion). The controllability of the system is experimentally demonstrated using a linear time-invariant (LTI) controller. The controller is found using an identified LTI model of the system, resulting from the interaction of the soft orthotic device with a human leg, and model-based classical control design techniques. The suitability of the proposed control strategy is demonstrated with several angle-reference following experiments.
A key challenge for lithium (Li)-ion batteries is the capability to manage battery performance and predict lifetime. Early detection of battery-aging phenomena and the implications for the ...performance are crucial for maintaining warranty and avoiding safety-related liabilities. We established a framework for early detection of loss of Li inventory, which is further separated into Li plating and normal solid electrolyte interphase (SEI) formation. Although SEI formation is inevitable, Li plating causes serious degradation and safety issues. Therefore, Li plating must be identified and avoided. Our framework differentiates Li plating from SEI-formation-dominated cells based on data from the first 25 aging cycles. This classification framework is based on machine learning (ML); multiple coherent and physically meaningful electrochemical signatures along the aging process are used. We also demonstrate that multiple electrochemical signatures must be combined to increase accuracy in the classification.
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A machine learning framework for aging phenomena identification in Li-ion batteriesMultiple electrochemical signatures required for precise identification of Li platingFast, reliable detection of Li plating from conventional electrochemical measurementsPotential to accelerate battery design life cycle and enhance safety
Chen et al. establish a machine learning framework that classifies key cell-aging phenomena, normal SEI growth, and Li plating for fast-charging Li-ion batteries. The framework identifies and combines multiple electrochemical signatures that are physically related to each of aging phenomena and then classifies the signatures using early cycling data.
Extreme fast charging (XFC) has become a focal research point in the lithium-battery community over the last several years. As adoption of electric vehicles increases, fast charging has become a key ...driver in enhancing consumer recharge experience. Recently, the research community has made significant improvements in developing charge protocols to support XFC. New charge protocol designs derived using a combination of advanced, physically derived models, and electrochemical and secondary characterization methods, increase charge acceptance and decrease aging. By coordinating these methods and modifying protocols to account for different material constraints, including lithium plating and cathode particle degradation, novel charge protocols have increased the energy accepted during charging by over 25% in 10 min and increased the charge acceptance prior to a constant-voltage step by approximately 3x. Here, we review several charge-protocol advances, aging factors which are enhanced by XFC and advances which will enable adoption of XFC capable vehicles. These advances include implementing machine learning and other detection algorithms to reduce and classify lithium plating, which is known to significantly degrade cell performance and reduce cell life. The review concludes by discussing full-system fast charge requirements, including electric vehicle service equipment needs for implementing XFC protocols.
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•Overview of critical battery degradation modes occurring during extreme fast charging.•Methods and tools to aid in the development of extreme fast charge protocols.•Use of advanced analysis and machine learning for early failure mode identification.•Combined use of electrochemical models and experiments for protocol development.•Scaled protocols influence electric vehicle infrastructure planning.
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