ABSTRACT
The thermal battery, a key source for powering defensive power systems, employs Li alloy-based anodes. However, the alloying increases the reduction potential of Li which lowers the overall working voltage and energy output. To overcome these issues, Li alloy must be replaced with pure Li. Utilizing pure Li requires a structure that can hold liquefied Li because the working temperature for the thermal battery exceeds the melting point of Li. The liquefied Li can leak out of the anode, causing short-circuit. A Li-Fe electrode (LiFE) in which Fe powder holds liquefied Li has been developed. In LiFE, higher Li content can lead to higher energy output but increases the risk of Li leakage. Thus, Li content in the LiFE has been limited. Here, we demonstrate a novel core-shell electrode structure to achieve a higher energy output. The proposed core-shell LiFE incorporates a high Li content core and a low Li content shell; high energy comes from the core and the shell prevents the Li from leakage. The fabricated core-shell structured electrode demonstrates the high energy of 9074 W s, an increase by 1.66 times compared to the low Li content LiFE with the conventionally used Li content (5509 W s).
ABSTRACT
Li-excess layered cathode (LLC) materials have a high theoretical specific capacity of 250 mAh g-1 induced by transition metal (cationic) and oxygen (anionic) redox activity. Especially, the oxygen redox reaction related to the activation of the Li2MnO3 domain plays the crucial role of providing a high specific capacity. However, it also induces an irreversible oxygen release and accelerates the layered-to-spinel phase transformation and capacity fading. Here, it is shown that surface doping of vanadium (V5+) cations into LLC material suppresses both the irreversible oxygen release and undesirable phase transformation, resulting in the improvement of capacity retention. The V-doped LLC shows a high discharge capacity of 244.3 ± 0.8 mAh g-1 with 92% retention after 100 cycles, whereas LLC delivers 233.6 ± 1.1 mAh g-1 with 74% retention. Furthermore, the average discharge voltage of V-doped LLC drops by only 0.33 V after 100 cycles, while LLC exhibits 0.43 V of average discharge voltage drop. Experimental and theoretical investigations indicate that doped V-doping increase the transition metal-oxygen (TM-O) covalency and affect the oxidation state of peroxo-like (O2) n - species during the delithiation process. The role of V-doping to make the oxygen redox reversible in LLC materials for high-energy density Li-ion batteries is illustrated here.
ABSTRACT
Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.
