Tailoring Heterostructure Growth on Liquid Metal Nanodroplets through Interface Engineering.

Liquid metal (LM) nanodroplets possess intriguing surface properties, thus offering promising potential in chemical synthesis, catalysis, and biomedicine. However, the reaction kinetics and product growth at the surface of LM nanodroplets are significantly influenced by the interface involved, which has not been thoroughly explored and understood. Here, we propose an interface engineering strategy, taking a spontaneous galvanic reaction between Ga and AuCl ions as a representative example, to successfully modulate the growth of heterostructures on the surface of Ga-based LM nanodroplets by establishing a dielectric interface with a controllable thickness between LM and reactive surroundings.

Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage.

Dielectric capacitors offer great potential for advanced electronics due to their high power densities, but their energy density still needs to be further improved. High-entropy strategy has emerged as an effective method for improving energy storage performance, however, discovering new high-entropy systems within a high-dimensional composition space is a daunting challenge for traditional trial-and-error experiments. Here, based on phase-field simulations and limited experimental data, we propose a generative learning approach to accelerate the discovery of high-entropy dielectrics in a practically infinite exploration space of over 10 combinations.

Programming Polarity Heterogeneity of Energy Storage Dielectrics by Bidirectional Intelligent Design.

Dielectric capacitors, characterized by ultra-high power densities, are considered as fundamental energy storage components in electronic and electrical systems. However, synergistically improving energy densities and efficiencies remains a daunting challenge. Understanding the role of polarity heterogeneity at the nanoscale in determining polarization response is crucial to the domain engineering of high-performance dielectrics.

Texture Engineering Modulating Electromechanical Breakdown in Multilayer Ceramic Capacitors.

Understanding the electromechanical breakdown mechanisms of polycrystalline ceramics is critical to texture engineering for high-energy-density dielectric ceramics. Here, an electromechanical breakdown model is developed to fundamentally understand the electrostrictive effect on the breakdown behavior of textured ceramics. Taking the Na Bi TiO -Sr Bi TiO ceramic as an example, it is found that the breakdown process significantly depends on the local electric/strain energy distributions in polycrystalline ceramics, and reasonable texture design could greatly alleviate electromechanical breakdown.

Studies toward synthesis of the core skeleton of spiroaspertrione A.

Bioassay-guided isolation of spiroaspertrione A from cultures of . TJ23 in 2017 demonstrated potent resensitization of oxacillin against methicillin-resistant by lowering the oxacillin minimal inhibitory concentration up to 32-fold. To construct this unique spiro[bicyclo[3.

Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics.

Understanding the breakdown mechanisms of polymer-based dielectrics is critical to achieving high-density energy storage. Here a comprehensive phase-field model is developed to investigate the electric, thermal, and mechanical effects in the breakdown process of polymer-based dielectrics. High-throughput simulations are performed for the P(VDF-HFP)-based nanocomposites filled with nanoparticles of different properties.

High-Throughput Phase-Field Design of High-Energy-Density Polymer Nanocomposites.

Understanding the dielectric breakdown behavior of polymer nanocomposites is crucial to the design of high-energy-density dielectric materials with reliable performances. It is however challenging to predict the breakdown behavior due to the complicated factors involved in this highly nonequilibrium process. In this work, a comprehensive phase-field model is developed to investigate the breakdown behavior of polymer nanocomposites under electrostatic stimuli.