Theoretical insights into surface-phase transition and ion competition during alkali ion intercalation on the Cu₄Se₄ nanosheet

Abstract

Cu4Se4 nanosheet is predicted to be promising electrode materials due to its excellent electrical conductivity, high capacity, fast diffusion and little volume change. They are also applicable to aqueous Na/K batteries with decreased capacities.

A fluorine/oxygen co-doping scheme for biomass carbon provides excellent rapid reaction kinetics for sodium/potassium-ion batteries

“…It is important to note that increasing the interlayer spacing will decrease the diffusion barrier and adsorption energy of Na + /K + in layered materials, thus optimizing the migration performance of Na + /K + within the material. 29 The Raman spectra of all samples display two characteristic peaks centered at 1340 cm −1 (D band) and 1590 cm −1 (G band) (Fig. 2b).…”

Surface-engineered Mo2B: a promising electrode material for constructing Ohmic contacts with blue phosphorene for electronic device applications

“…The work function of the bare Mo 2 B is between those of graphene (4.49 eV) and Cu 4 Se 4 nanosheets (5.361 eV). 47,48 When O is terminated with the surface of Mo 2 B, its work function sharply increases to 7.46 eV, more than the ionization energy (6.26 eV) of BP, and a p-type contact could be obtained. When terminated with OH groups, its work function rapidly decreases to 2.69 eV, less than the electron affinity (4.33 eV) of BP, and it is likely to form an n-type contact.…”

Comprehensive Composite Mould Filling Pattern Dataset for Process Modelling and Prediction

“…However, the high costs associated with experimental investigations promote the development of alternative approaches. In the literature, efforts in process evaluation and modelling predominantly fall into the following three main fields of research: theoretical modelling (e.g., Density Functional Theory [45] and Darcy's Law [46]), physics-based modelling (e.g., Finite Element method [47,48]), and data-driven modelling (e.g., Artificial Neural Network [49,50]). While theoretical and physics-based modelling approaches are renowned for their accuracy, these methods often demand high computational resources to evaluate.…”