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By means of density functional theory (DFT) computations, we explored the potential of carbon- and nitrogen-doped Mo 2 P (CMP and NMP) layered materials as the representative of transition metal phosphides (TMPs) for the development of lithium-ion battery (LIB) anode materials, paying special attention to the synergistic effects of the dopants. Both CMP and NMP have exceptional stabilities and excellent electronic conductivity, and a high theoretical maximum storage capacity of ∼ 486 mA h g −1 . Li-ion diffusion barriers on the two-dimensional (2D) CMP and NMP surfaces are extremely low (∼0.036 eV), and it is expected that on these 2D layers Li can diffuse 10 4 times faster than that on MoS 2 and graphene at room temperature, and both monolayers have relatively low average open-circuit voltage (0.38 and 0.4 eV). All these exceptional properties make CMP and NMP monolayers as promising candidates for high-performance LIB anode materials, which also demonstrates that simple doping is an effective strategy to enhance the performance of anode materials in rechargeable batteries. 

Lithium-ion batteries (LIBs) are widely used energy storage devices, and sodium-ion batteries (SIBs) are promising alternatives to LIBs because sodium is of high abundance and low toxicity. However, a dominant obstacle for the advancement of LIBs and SIBs is the lack of high capacity anode materials, especially for SIBs. Here, we propose that three characteristics, namely appropriate pore size, suitable pore distribution, and an entirely planar topology, can help achieve ultrahigh capacity 2D anode materials. Under such guidelines, we constructed a B 7 P 2 monolayer, and investigated its potential as a LIB/SIB anode material by means of density functional theory (DFT) computations. Encouragingly, the B 7 P 2 monolayer possesses all the essential properties of a high-capacity LIB/SIB anode: its high stability ensures the experimental feasibility of synthesis, its metallicity does not change upon Li/Na adsorption and desorption, the Li/Na can well diffuse on the surface, and the open-circuit voltage is in a good range. Most importantly, the B 7 P 2 monolayer has a high storage capacity of 3117 mA h g −1 for both LIBs and SIBs, and this capacity value ranks among the highest for 2D SIB anode materials. This study offers us some good clues to design/discover other anode materials with ultrahigh capacities, and serves us another vivid example that (implicit and hidden) trends/rules in the literature can guide us in the design of functional materials more efficiently. 

By means of density functional theory computations, we explored the electrochemical performance of an FeSe monolayer as an anode material for lithium and non-lithium ion batteries (LIBs and NLIBs). The electronic structure, adsorption, diffusion, and storage behavior of different metal atoms (M) in FeSe were systematically investigated. Our computations revealed that M adsorbed FeSe (M = Li, Na and K) systems show metallic characteristics that give rise to good electrical conductivity and mobility with low activation energies for diffusion (0.16, 0.13 and 0.11 eV for Li, Na, and K, respectively) of electrons and metal atoms in the materials, indicative of a fast charge/discharge rate. In addition, the theoretical capacities of the FeSe monolayer for Li, Na and K can reach up to 658, 473, and 315 mA h g −1 , respectively, higher than that of commercial graphite (372 mA h g −1 for Li, 284 mA h g −1 for Na, and 273 mA h g −1 for K), and the average open-circuit voltage is moderate (0.38–0.88 V for Li, Na and K). All these characteristics suggest that the FeSe monolayer is a potential anode material for alkali-metal rechargeable batteries. 

Oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal-air batteries. Graphene-supported single-atom catalysts (SACs) have been widely explored; however, either experiments or density functional theory (DFT) computations cannot screen catalysts at high speed. Herein, based on DFT computations of 104 graphene-supported SACs (M@C3, M@C4, M@pyridine-N4, and M@pyrrole-N4), we built up machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (errors = 0.013/0.005/0.020 V for ORR/OER/HER, respectively), and employed these models to predict the catalysis performance of 260 other graphene-supported SACs containing metal-NxCy active sites (M@NxCy). We recomputed the top catalysts recommended by ML towards ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3C1 and Ir@pyridine-N2C2) outperforming noble metal oxides, RuO2 and IrO2. The ML models quantitatively unveiled the significance of various descriptors and fast narrowed down the potential list of graphene-supported single-atom catalysts. This approach can be easily used to screen and design other SACs, and significantly accelerate the catalyst design for many other important reactions.