Phase-Field Based Multiscale Modeling of Heterogeneous Solid Electrolytes: Applications to Nanoporous Li3PS4

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design

Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure-property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure-an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

Bicontinuous oxide heteroepitaxy with enhanced photoconductivity

Self-assembled systems have recently attracted extensive attention because they can display a wide range of phase morphologies in nanocomposites, providing a new arena to explore novel phenomena. Among these morphologies, a bicontinuous structure is highly desirable based on its high interface-to-volume ratio and 3D interconnectivity. A bicontinuous nickel oxide (NiO) and tin dioxide (SnO₂) heteroepitaxial nanocomposite is revealed here. By controlling their concentration, we fabricated tuneable self-assembled nanostructures from pillars to bicontinuous structures, as evidenced by TEM-energy-dispersive X-ray spectroscopy with a tortuous compositional distribution. The experimentally observed growth modes are consistent with predictions by first-principles calculations. Phase-field simulations are performed to understand 3D microstructure formation and extract key thermodynamic parameters for predicting microstructure morphologies in SnO₂:NiO nanocomposites of other concentrations. Furthermore, we demonstrate significantly enhanced photovoltaic properties in a bicontinuous SnO₂:NiO nanocomposite macroscopically and microscopically. This research shows a pathway to developing innovative solar cell and photodetector devices based on self-assembled oxides.

Elucidating the Role of Microstructure in Thiophosphate Electrolytes - a Combined Experimental and Theoretical Study of β-Li3 PS4

Solid-state batteries (SSBs) are promising candidates to significantly exceed the energy densities of today's state-of-the-art technology, lithium-ion batteries (LIBs). To enable this advancement, optimizing the solid electrolyte (SE) is the key. β-Li3 PS4 (β-LPS) is the most studied member of the Li2 S-P2 S5 family, offering promising properties for implementation in electric vehicles. In this work, the microstructure of this SE and how it influences the electrochemical performance are systematically investigated. To figure this out, four batches of β-LPS electrolyte with different particle size, shape, and porosity are investigated in detail. It is found that differences in pellet porosities mostly originate from single-particle intrinsic features and less from interparticle voids. Surprisingly, the β-LPS electrolyte pellets with the highest porosity and larger particle size not only show the highest ionic conductivity (up to 0.049 mS cm-1 at RT), but also the most stable cycling performance in symmetrical Li cells. This behavior is traced back to the grain boundary resistance. Larger SE particles seem to be more attractive, as their grain boundary contribution is lower than that of denser pellets prepared using smaller β-LPS particles.

Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics

There has been a surge of interest in applying deep learning (DL) to microstructure generation and materials design. However, existing DL-based methods are generally limited in generating (1) microstructures with high resolution, (2) microstructures with high variability, (3) microstructures with guaranteed periodicity, and (4) highly controllable microstructures. In this study, a DL approach based on a stacked generative adversarial network (StackGAN-v2) is proposed to overcome these shortcomings. The presented modeling approach can reconstruct high-fidelity microstructures of additively manufactured piezoceramics with different resolutions, which are statistically equivalent to original microstructures either experimentally observed or numerically predicted. Advantages of the proposed modeling approach are also illustrated in terms of its capability in controlling the probability density function (PDF) of grain size, grain orientation, and micropore in a large space, which would have significant benefits in exploring the effects of these microstructure features on the piezoelectricity of piezoceramics. In the meantime, periodicity of the microstructures has been successfully introduced in the developed model, which can critically reduce the simulation volume to be considered as a representative volume element (RVE) during computational calculation of piezoelectric properties. Therefore, this DL approach can significantly accelerate the process of designing optimal microstructures when integrating with computational methods (e.g., fast Fourier spectral iterative perturbation (FSIPM)) to achieve desired piezoelectric properties. The proposed DL-based method is generally applicable to optimal design of a variety of periodic microstructures, allowing for maximum explorations of design spaces and fine manipulations of microstructural features.

3D Fiber-Network-Reinforced Bicontinuous Composite Solid Electrolyte for Dendrite-free Lithium Metal Batteries

Replacement of flammable organic liquid electrolytes with solid Li⁺ conductors is a promising approach to realize excellent performance of Li metal batteries. However, ceramic electrolytes are either easily reduced by Li metal or penetrated by Li dendrites through their grain boundaries, and polymer electrolytes are also faced with instability on the electrode/electrolyte interface and weak mechanical property. Here, we report a three-dimensional fiber-network-reinforced bicontinuous solid composite electrolyte with flexible Li⁺-conductive network (lithium aluminum titanium phosphate (LATP)/polyacrylonitrile), which helps to enhance electrochemical stability on the electrode/electrolyte interface by isolating Li and LATP and suppress Li dendrites growth by mechanical reinforcement of fiber network for the composite solid electrolyte. The composite electrolyte shows an excellent electrochemical stability after 15 days of contact with Li metal and has an enlarged tensile strength (10.72 MPa) compared to the pure poly(ethylene oxide)-bistrifluoromethanesulfonimide lithium salt electrolyte, leading to a long-term stability and safety of the Li symmetric battery with a current density of 0.3 mA cm^-2 for 400 h. In addition, the composite electrolyte also shows good electrochemical and thermal stability. These results provide such fiber-reinforced membranes that present stable electrode/electrolyte interface and suppress lithium dendrite growth for high-safety all-solid-state Li metal batteries.

Electrically reversible cracks in an intermetallic film controlled by an electric field

Cracks in solid-state materials are typically irreversible. Here we report electrically reversible opening and closing of nanoscale cracks in an intermetallic thin film grown on a ferroelectric substrate driven by a small electric field (~0.83 kV/cm). Accordingly, a nonvolatile colossal electroresistance on–off ratio of more than 10⁸ is measured across the cracks in the intermetallic film at room temperature. Cracks are easily formed with low-frequency voltage cycling and remain stable when the device is operated at high frequency, which offers intriguing potential for next-generation high-frequency memory applications. Moreover, endurance testing demonstrates that the opening and closing of such cracks can reach over 10⁷ cycles under 10-μs pulses, without catastrophic failure of the film.

Electric-field-induced cracks are generally detrimental to functionality of ferroelectric ceramics. Liu et al. use an intermetallic alloy and ferroelectric oxide junction to mediate the reversible formation of cracks at nanoscales, resulting in colossal electroresistance modulation for memory applications.