Related papers: Machine Learning Relationships between Nanoporous …
We present a computational microscopy analysis (targeted molecular dynamics simulations) of the structure and performance of conductive metal organic framework (MOF) electrodes in supercapacitors with room temperature ionic liquids. The…
Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most…
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase boundaries and inter-particle connections strongly influence system performance. Advances in X-ray…
The amorphous structure of nanoporous carbon electrodes in supercapacitors complicates the establishment of clear links between electrode geometry and capacitance. In this work, we examine how specific structural features govern charge…
Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accuracy close to quantum calculations. However, their…
Confinement strongly influences electrochemical systems, where structural control has enabled advances in nanofluidics, sensing, and energy storage. In electric double-layer capacitors (EDLCs), or supercapacitors, energy density is governed…
Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in…
Investigating the correlations between dynamic and static storage properties of nanoporous electrodes is beneficial for further progress of supercapacitors-based technologies. While the dependence of the capacitance on the pores' sizes is…
Asymmetric behaviors of capacitance and charging dynamics in the cathode and anode are general for nanoporous supercapacitors. Understanding this behavior is essential for the optimal design of supercapacitors. Herein, we perform…
Nanoporous supercapacitors are an important player in the field of energy storage that fill the gap between dielectric capacitors and batteries. The key challenge in the development of supercapacitors is the perceived trade-off between…
Nanoporous supercapacitors play an important role in modern energy storage systems, and their modeling is essential to predict and optimize the charging behaviour. Two classes of models have been developed that consist of finite and…
Metal-organic frameworks (MOFs) are a specific class of hybrid, crystalline, nano-porous materials made of metal-ion-based nodes and organic linkers. Most of the studies on MOFs largely focused on porosity, chemical and structural…
Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally…
Ultrathin nanopore membranes based on 2D materials have demonstrated ultimate resolution toward DNA sequencing. Among them, molybdenum disulphide (MoS2) shows long-term stability as well as superior sensitivity enabling high throughput…
Electrical conductivity is an inherent property of a hydrophobic porous media (HPM) and has critical applications. This research aims to provide a solution for predicting the electrical conductivity of nanoscale HPM with heterogeneous pore…
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation. However, the chemical space of MOFs is close to an…
Machine learning has emerged as a potent computational tool for expediting research and development in solid oxide fuel cell electrodes. The effective application of machine learning for performance prediction requires transforming…
Mechanical properties of porous SOFC electrodes are largely determined by their microstructures. Measurements of the elastic properties and microstructural parameters can be achieved by modelling of the digitally reconstructed 3D volumes…
A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a…