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 Ga0 and AuCl4- 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. Combining high-resolution electron energy-loss spectroscopy (EELS) analysis and theoretical simulation, it was found that the induced charge distribution at the interface dominates the spatiotemporal distribution of the reaction sites. Employing tungsten oxide (WOx) with varying thicknesses as the demonstrated dielectric interface of LM, Ga@WOx@Au with distinct core-shell-satellite or dimer-like heterostructures has been achieved and exhibited different photoresponsive capabilities for photodetection. Understanding the kinetics of product growth and the regulatory strategy of the dielectric interface provides an experimental approach to controlling the structure and properties of products in LM nanodroplet-involved chemical processes.

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 1011 combinations. By encoding-decoding latent space regularities to facilitate data sampling and forward inference, we employ inverse design to screen out the most promising combinations via a ranking strategy. Through only 5 sets of targeted experiments, we successfully obtain a Bi(Mg0.5Ti0.5)O3-based high-entropy dielectric film with a significantly improved energy density of 156 J cm-3 at an electric field of 5104 kV cm-1, surpassing the pristine film by more than eight-fold. This work introduces an effective and innovative avenue for designing high-entropy dielectrics with drastically reduced experimental cycles, which could be also extended to expedite the design of other multicomponent material systems with desired properties.

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. Here, a bidirectional design with phase-field simulation and machine learning is performed to forward reveal the structure-property relationship and reversely optimize polarity heterogeneity to improve energy storage performance. Taking BiFeO3-based dielectrics as typical systems, this work establishes the mapping diagrams of energy density and efficiency dependence on the volume fraction, size and configuration of polar regions. Assisted by CatBoost and Wolf Pack algorithms, this work analyzes the contributions of geometric factors and intrinsic features and find that nanopillar-like polar regions show great potential in achieving both high polarization intensity and fast dipole switching. Finally, a maximal energy density of 188 J cm-3 with efficiency above 95% at 8 MV cm-1 is obtained in BiFeO3-Al2O3 systems. This work provides a general method to study the influence of local polar heterogeneity on polarization behaviors and proposes effective strategies to enhance energy storage performance by tuning polarity heterogeneity.

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 Na0.5 Bi0.5 TiO3 -Sr0.7 Bi0.2 TiO3 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. Then, high-throughput simulations are performed to establish the mapping relationship between the breakdown strength and different intrinsic/extrinsic variables. Finally, machine learning is conducted on the database from the high-throughput simulations to obtain the mathematical expression for semi-quantitatively predicting the breakdown strength, based on which some basic principles of texture design are proposed. The present work provides a computational understanding of the electromechanical breakdown behavior in textured ceramics and is expected to stimulate more theoretical and experimental efforts in designing textured ceramics with reliable electromechanical performances.

Studies toward synthesis of the core skeleton of spiroaspertrione A.

Bioassay-guided isolation of spiroaspertrione A from cultures of Aspergillus sp. TJ23 in 2017 demonstrated potent resensitization of oxacillin against methicillin-resistant Staphylococcus aureus by lowering the oxacillin minimal inhibitory concentration up to 32-fold. To construct this unique spiro[bicyclo[3.2.2]nonane-2,1'-cyclohexane] system, a protocol for ceric ammonium nitrate-induced intramolecular cross-coupling of silyl enolate is disclosed.

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. Machine learning is conducted on the database from the high-throughput simulations to produce an analytical expression for the breakdown strength, which is verified by targeted experimental measurements and can be used to semiquantitatively predict the breakdown strength of the P(VDF-HFP)-based nanocomposites. The present work provides fundamental insights to the breakdown mechanisms of polymer nanocomposite dielectrics and establishes a powerful theoretical framework of materials design for optimizing their breakdown strength and thus maximizing their energy storage by screening suitable nanofillers. It can potentially be extended to optimize the performances of other types of materials such as thermoelectrics and solid electrolytes.

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. It is found that the breakdown strength and path significantly depend on the microstructure of the nanocomposite. The predicted breakdown strengths for polymer nanocomposites with specific microstructures agree with existing experimental measurements. Using this phase-field model, a high throughput calculation is performed to seek the optimal microstructure. Based on the high-throughput calculation, a sandwich microstructure for PVDF-BaTiO3 nanocomposite is designed, where the upper and lower layers are filled with parallel nanosheets and the middle layer is filled with vertical nanofibers. It has an enhanced energy density of 2.44 times that of the pure PVDF polymer. The present work provides a computational approach for understanding the electrostatic breakdown, and it is expected to stimulate future experimental efforts on synthesizing polymer nanocomposites with novel microstructures to achieve high performances.