From Nanoalloy to Nano-Laminated Interfaces for Highly Stable Alkali-Metal Anodes.

Metal anodes are considered the holy grail for next-generation batteries because of their high gravimetric/volumetric specific capacity and low electrochemical potential. However, several unsolved challenges have impeded their practical applications, such as dendrite growth, interfacial side reactions, dead layer formation, and volume change. An electrochemically, chemically, and mechanically stable artificial solid electrolyte interphase is key to addressing the aforementioned issue with metal anodes. This study demonstrates a new concept of organic and inorganic hybrid interfaces for both Li- and Na-metal anodes. Through tailoring the compositions of the hybrid interfaces, a nanoalloy structure to nano-laminated structure is realized. As a result, the nanoalloy interface (1Al2 O3 -1alucone or 2Al2 O3 -2alucone) presents the most stable electrochemical performances for both Li-and Na-metal anodes. The optimized thicknesses required for the nanoalloy interfaces for Li- and Na-metal anodes are different. A cohesive zone model is applied to interpret the underlying mechanism. Furthermore, the influence of the mechanical stabilities of the different interfaces on the electrochemical performances is investigated experimentally and theoretically. This approach provides a fundamental understanding and establishes the bridge between mechanical properties and electrochemical performance for alkali-metal anodes.

A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures.

Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.

Ionic Conductive and Highly-Stable Interface for Alkali Metal Anodes.

Alkali metals are regarded as the most promising candidates for advanced anode for the next-generation batteries due to their high specific capacity, low electrochemical potential, and lightweight. However, critical problems of the alkali metal anodes, especially dendrite formation and interface stabilization, remain challenging to overcome. The solid electrolyte interphase (SEI) is a key factor affecting Li and Na deposition behavior and electrochemical performances. Herein, a facile and universal approach is successfully developed to fabricate ionic conductive interfaces for Li and Na metal anodes by modified atomic layer deposition (ALD). In this process, the Li metal (or Na metal) plays the role of Li (or Na) source without any additional Li (or Na) precursor during ALD. Moreover, the key questions about the influence of ALD deposition temperature on the compositions and structure of the coatings are addressed. The optimized ionic conductive coatings have significantly improved the electrochemical performances. In addition, the electrochemical phase-field model is performed to prove that the ionic conductive coating is very effective in promoting uniform electrodeposition. This approach is universal and can be potentially applied to other different metal anodes. At the same time, it can be extended to other types of coatings or other deposition techniques.

3D Printed High-Performance Lithium Metal Microbatteries Enabled by Nanocellulose.

Batteries constructed via 3D printing techniques have inherent advantages including opportunities for miniaturization, autonomous shaping, and controllable structural prototyping. However, 3D-printed lithium metal batteries (LMBs) have not yet been reported due to the difficulties of printing lithium (Li) metal. Here, for the first time, high-performance LMBs are fabricated through a 3D printing technique using cellulose nanofiber (CNF), which is one of the most earth-abundant biopolymers. The unique shear thinning properties of CNF gel enables the printing of a LiFePO4 electrode and stable scaffold for Li. The printability of the CNF gel is also investigated theoretically. Moreover, the porous structure of the CNF scaffold also helps to improve ion accessibility and decreases the local current density of Li anode. Thus, dendrite formation due to uneven Li plating/stripping is suppressed. A multiscale computational approach integrating first-principle density function theory and a phase-field model is performed and reveals that the porous structures have more uniform Li deposition. Consequently, a full cell built with a 3D-printed Li anode and a LiFePO4 cathode exhibits a high capacity of 80 mA h g-1 at a charge/discharge rate of 10 C with capacity retention of 85% even after 3000 cycles.