The application of the data-driven machine learning (DDML) for the disposal of the high-
level nuclear waste (HLW) is of emerging interest in the recent years. This review aims to …

Along with nanoscience advances, nanoarchitectonics have emerged as novel
nanomaterials, with self-assembled arrangement of atoms and interesting properties …

Demand for thermal management materials (TMMs) with efficient in-plane heat dissipation
has grown with the advancement of intelligent wireless communication equipment. Herein …

In recent years,(big) data science has emerged as the “fourth paradigm” in physical science
research. Data-driven techniques, eg machine learning, are advantageous in dealing with …

Thermal conductivity is one of the crucial properties of nano enhanced phase change
materials (NEPCM). Then, in this study three different machine learning methods namely …

Phase change materials (PCMs) have gained popularity in storing thermal energy due to
their high energy storage capacity per volume. However, the low performance of the PCM …

Abstract Machine learning (ML)-assisted modelling of deep geological repositories (DGR) is
of emerging interest and can help to improve the safe and reliable operation of DGRs as …

Electric vehicles (EVs) are increasingly becoming an environmental-friendly option in
current transportation systems, thanks to reduced fossil fuel consumption and carbon …

The utilization of molten salts in heat transfer applications, specifically within shell-and-tube
heat exchangers, has garnered significant attention for its potential in sustainable energy …

An engineered barrier system (EBS) for the deep geological disposal of high-level
radioactive waste (HLW) is composed of a disposal canister, buffer material, gap-filling …