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Deeply Cyclable and Ultrahigh-Rate Lithium Metal Anodes Enabled by Coaxial Nanochamber Heterojunction on Carbon Nanofibers.

ABSTRACT: Lithium metal anodes (LMAs) are the most promising candidates for high-energy-density batteries due to the high theoretical specific capacity and lowest potential. However, the practical application of LMAs is hampered by the short lifespan and unsatisfactory lithium utilization (<50%). An oxide-oxide heterojunction enhanced with nanochamber structure design is proposed to improve lithium utilization and cycling performance of LMA under ultrahigh rates. Typically, a MnO₂ -ZnO heterojunction provides high binding energy for strong absorption of Li-ions and intimately bonded interfaces for fast transfer of electrons. Under the guidance of the smooth Li-ion migration and rapid electron flow, the Li metal can be restricted as thin layers within submicro scale in nanochambers with constrain boundary and stress dissipation, inhibiting the local agglomeration and blocking. Thus, the lithiophilic active sites can be effectively exposed to the Li-ions within submicro scale, improving the reversible conversion for high lithium utilization during long-term cycling. As such, the Li@MnZnO/CNF electrode achieves a high lithium utilization of 70% at a record-high current density of 50 mA cm^-2 with areal capacity of 10 mAh cm^-2 . This work offers an avenue to improve lithium utilization for long-lifespan LMAs working under high current densities and capacities.

SUBMITTER: Le T

PROVIDER: S-EPMC8655213 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

Dataset Information

Deeply Cyclable and Ultrahigh-Rate Lithium Metal Anodes Enabled by Coaxial Nanochamber Heterojunction on Carbon Nanofibers.

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