Electrochemical Properties of Boron‐Doped Fullerene Derivatives for Lithium‐Ion Battery Applications

Abstract

The high electron affinity of fullerene C coupled with the rich chemistry of carbon makes it a promising material for cathode applications in lithium-ion batteries. Since boron has one electron less than carbon, the presence of boron on C cages is expected to generate electron deficiency in C , and thereby to enhance its electron affinity. By using density functional theory (DFT), we studied the redox potentials and electronic properties of C and C B. We have found that doping C with one boron atom results in a substantial increase in redox potential from 2.462 V to 3.709 V, which was attributed to the formation of an open shell system. We also investigated the redox and electronic properties of C B functionalized with various redox-active oxygen containing functional groups (OCFGs). For the combination of functionalization with OCFGs and boron doping, it is found that the enhancement of redox potential is reduced, which is mainly attributed to the open shell structure being changed to a closed-shell one. Nevertheless, the redox potentials are still higher than that of pristine C . From the observation that the lowest unoccupied molecular orbital of closed-shell OCFG- functionalized C B is correlated well with the redox potential, it was confirmed that the spin state is crucial to be considered to understand the relationship between electronic structure and redox properties.

Mechanochemical Synthesis of γ‐Graphyne with Enhanced Lithium Storage Performance

“…Among different kinds of electrode materials, nanostructured carbon materials have represented the most promising and challenging materials since their extensive applications in the realm of electrochemistry . Several of carbon materials like carbon nanotubes (CNTs), fullerene, and graphene have been applied as the anodes in LIBs due to their superior electronic conductivity, structural stability, light weight, and flexibility compared with other metal and oxide anodes . However, the carbon atom arrangements in these nanosize carbon materials are the same as the commercial graphite anode, which is composed of all sp 2 ‐hybridized carbon atoms, indicating the similarly limited Li + storage mechanism by situating Li + between the carbon sheets …”

Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries

“…† Computational details on the DFT modelling approach employed to predict the redox potential are addressed in our previous studies. [8][9][10][11][12][13]16,17 Thus, we briey describe the computational methods. All the calculations were performed using Jaguar soware 30 with PBE0 to calculate the exchange correlation interactions 31 and 6-31+G(d,p) basis set.…”

“…[1][2][3][4][5][6][7] Furthermore, increasing demands on high performance electrochemical energy storage technologies have attracted lots of attention, especially focusing on high energy, charge, and power capacities. [1][2][3][4][5][6][7][8][9][10][11][12][13][14] Among a number of available candidates, lithium-ion batteries have been recognized as one of the most promising secondary batteries due to their high energy and charge capacities in addition to their cyclic stability. [2][3][4][5][6][7][8][9] However, it should be noted that the current lithium ion batteries suffer from several issues such as difficulty in the diffusion of lithium ions through the bulk phase of conventional transition metal oxides of positive electrodes; which results in poor power capacities.…”

“…It has two components: one is quantum mechanical density functional theory (DFT) modeling and another is the machine learning method. We have used DFT modeling method for carbon-based molecular electrode materials for battery [8][9][10][11][12][13] and conrmed its accurate predictability for redox potentials with small uncertainty (<0.3 V). Specically, in this contribution we aim to train a learning model for various carbon based electrode materials.…”

“…Specically, in this contribution we aim to train a learning model for various carbon based electrode materials. [8][9][10][11][12][13] Although DFT can produce high-delity predictions of redox potential, it is impractical to establish a predictive highthroughput screening method only based on DFT modeling. The main reasons are that (1) the effects of various molecular architectures are not linearly proportional to the target properties and (2) multistep quantum mechanical computations for predicting accurate redox potentials are computationally very expensive.…”