Pliable Lithium Superionic Conductor for AllSolid-State Batteries

The key challenges in all-solid-state batteries (ASSBs) are establishing and maintaining perfect physical contact between rigid components for facile interfacial charge transfer, particularly between the solid electrolyte and cathode, during repeated electrochemical cycling. Here, we introduce inorganic-based pliable solid electrolytes that exhibit extraordinary clay-like mechanical properties (storage and loss moduli <1 MPa) at room temperature, high lithium-ion conductivity (3.6 mS cm(-1)), and a glass transition below -50 degrees C. The unique mechanical features enabled the solid electrolyte to penetrate into the high-loading cathode like liquid, thereby providing complete ionic conduction paths for all cathode particles as well as maintaining the pathway even during cell operation. We propose a design principle in which the complex anion formation including Ga, F, and a different halogen can induce the claylike features. Our findings provide new opportunities in the search for solid electrolytes and suggest a new approach for resolving the issues caused by the solid electrolyte-cathode interface in ASSBs

Advances and challenges in multiscale characterizations and analyses for battery materials

Rechargeable ion batteries are efficient energy storage devices widely employed in portable to large-scale applications such as electric vehicles and grids. Electrochemical reactions within batteries are complex phenomena, and they are strongly dependent on the battery materials and systems used. These electrochemical reactions often include detrimental irreversible reactions at various length scales from atomic- to macro-scales, which ultimately determine the overall electrochemical behavior of the system. Understanding such reaction mechanisms is a critical component to improve battery performance. To help this effort, this review article discusses recent advances and remaining challenges in both computational and experimental approaches to better understand dynamic electrochemical reactions in batteries across multiple length scales. Important related findings from this focus issue will also be highlighted. The aim of our focus issue is to contribute to the battery community towards having better understanding of complex reactions occurring in battery devices and of computational and experimental methods to investigate them. Graphical abstract: [Figure not available: see fulltext.

Advances and challenges in multiscale characterizations and analyses for battery materials

Rechargeable ion batteries are efficient energy storage devices widely employed in portable to large-scale applications such as electric vehicles and grids. Electrochemical reactions within batteries are complex phenomena, and they are strongly dependent on the battery materials and systems used. These electrochemical reactions often include detrimental irreversible reactions at various length scales from atomic- to macro-scales, which ultimately determine the overall electrochemical behavior of the system. Understanding such reaction mechanisms is a critical component to improve battery performance. To help this effort, this review article discusses recent advances and remaining challenges in both computational and experimental approaches to better understand dynamic electrochemical reactions in batteries across multiple length scales. Important related findings from this focus issue will also be highlighted. The aim of our focus issue is to contribute to the battery community towards having better understanding of complex reactions occurring in battery devices and of computational and experimental methods to investigate them

Synthetic accessibility and stability rules of NASICONs

In this paper we develop the stability rules for NASICON structured materials, as an example of compounds with complex bond topology and composition. By applying machine learning to the ab-initio computed phase stability of 3881 potential NASICONs we can extract a simple two-dimensional descriptor that is extremely good at separating stable from unstable NASICONS. This machine-learned "tolerance factor" contains information on the Na content, the radii and electronegativities of the elements, and the Madelung energy. We test the predictive capability of this approach by selecting six predicted NASICON compositions. Five out of the six resulted in a phase pure NASICON while the sixth composition led to a NASICON that coexisted with other phases, validating the efficacy of this approach. This work not only provide tools to understand synthetic accessibility of NASICON-type materials, but also demonstrate an efficient paradigm for discovering new materials with complicate composition and atomic structure

Design principles for NASICON super-ionic conductors

Abstract Na Super Ionic Conductor (NASICON) materials are an important class of solid-state electrolytes owing to their high ionic conductivity and superior chemical and electrochemical stability. In this paper, we combine first-principles calculations, experimental synthesis and testing, and natural language-driven text-mined historical data on NASICON ionic conductivity to achieve clear insights into how chemical composition influences the Na-ion conductivity. These insights, together with a high-throughput first-principles analysis of the compositional space over which NASICONs are expected to be stable, lead to the successful synthesis and electrochemical investigation of several new NASICONs solid-state conductors. Among these, a high ionic conductivity of 1.2 mS cm−1 could be achieved at 25 °C. We find that the ionic conductivity increases with average metal size up to a certain value and that the substitution of PO4 polyanions by SiO4 also enhances the ionic conductivity. While optimal ionic conductivity is found near a Na content of 3 per formula unit, the exact optimum depends on other compositional variables. Surprisingly, the Na content enhances the ionic conductivity mostly through its effect on the activation barrier, rather than through the carrier concentration. These deconvoluted design criteria may provide guidelines for the design of optimized NASICON conductors

Design principles for NASICON super-ionic conductors.

Na Super Ionic Conductor (NASICON) materials are an important class of solid-state electrolytes owing to their high ionic conductivity and superior chemical and electrochemical stability. In this paper, we combine first-principles calculations, experimental synthesis and testing, and natural language-driven text-mined historical data on NASICON ionic conductivity to achieve clear insights into how chemical composition influences the Na-ion conductivity. These insights, together with a high-throughput first-principles analysis of the compositional space over which NASICONs are expected to be stable, lead to the successful synthesis and electrochemical investigation of several new NASICONs solid-state conductors. Among these, a high ionic conductivity of 1.2 mS cm-1 could be achieved at 25 °C. We find that the ionic conductivity increases with average metal size up to a certain value and that the substitution of PO4 polyanions by SiO4 also enhances the ionic conductivity. While optimal ionic conductivity is found near a Na content of 3 per formula unit, the exact optimum depends on other compositional variables. Surprisingly, the Na content enhances the ionic conductivity mostly through its effect on the activation barrier, rather than through the carrier concentration. These deconvoluted design criteria may provide guidelines for the design of optimized NASICON conductors

Synthetic accessibility and stability rules of NASICONs.

In this paper we develop the stability rules for NASICON-structured materials, as an example of compounds with complex bond topology and composition. By first-principles high-throughput computation of 3881 potential NASICON phases, we have developed guiding stability rules of NASICON and validated the ab initio predictive capability through the synthesis of six attempted materials, five of which were successful. A simple two-dimensional descriptor for predicting NASICON stability was extracted with sure independence screening and machine learned ranking, which classifies NASICON phases in terms of their synthetic accessibility. This machine-learned tolerance factor is based on the Na content, elemental radii and electronegativities, and the Madelung energy and can offer reasonable accuracy for separating stable and unstable NASICONs. This work will not only provide tools to understand the synthetic accessibility of NASICON-type materials, but also demonstrates an efficient paradigm for discovering new materials with complicated composition and atomic structure