Engineering of Aromatic Naphthalene and Solvent Molecules to Optimize Chemical Prelithiation for Lithium-Ion Batteries.
Jagabandhu PatraShi-Xian LuJui-Cheng KaoBing-Ruei YuYu-Ting ChenYu-Sheng SuTzi-Yi WuDominic BresserChien-Te HsiehYu-Chieh LoJeng-Kuei ChangPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2024)
A cost-effective chemical prelithiation solution, which consists of Li + , polyaromatic hydrocarbon (PAH), and solvent, is developed for a model hard carbon (HC) electrode. Naphthalene and methyl-substituted naphthalene PAHs, namely 2-methylnaphthalene and 1-methylnaphthalene, are first compared. Grafting an electron-donating methyl group onto the benzene ring can decrease electron affinity and thus reduce the redox potential, which is validated by density functional theory calculations. Ethylene glycol dimethyl ether (G1), diethylene glycol dimethyl ether, and triethylene glycol dimethyl ether solvents are then compared. The G1 solution has the highest conductivity and least steric hindrance, and thus the 1-methylnaphthalene/G1 solution shows superior prelithiation capability. In addition, the effects of the interaction time between Li + and 1-methylnaphthalene in G1 solvent on the electrochemical properties of a prelithiated HC electrode are investigated. Nuclear magnetic resonance data confirm that 10-h aging is needed to achieve a stable solution coordination state and thus optimal prelithiation efficacy. It is also found that appropriate prelithiation creates a more Li + -conducing and robust solid-electrolyte interphase, improving the rate capability and cycling stability of the HC electrode.
Keyphrases
- solid state
- ionic liquid
- density functional theory
- magnetic resonance
- molecular dynamics
- ion batteries
- solar cells
- polycyclic aromatic hydrocarbons
- gold nanoparticles
- carbon nanotubes
- magnetic resonance imaging
- machine learning
- computed tomography
- amino acid
- risk assessment
- mass spectrometry
- contrast enhanced
- atomic force microscopy
- molecular dynamics simulations
- single molecule