Articles

  • 5 days ago | azom.com | Lexie Corner |Muhammad Osama

    Reviewed by Lexie CornerA recent study published in Advanced Materials Interfaces examined how surface roughness impacts the performance of superconducting resonators—key components in quantum computing systems. The research focused on niobium (Nb) thin-film resonators on silicon substrates and analyzed how microscopic surface features influence microwave losses and the internal quality factor (Qi).

  • 1 week ago | azom.com | Lexie Corner |Muhammad Osama

    Reviewed by Lexie CornerA recent study published in npj Materials Degradation introduces a two-stage machine learning (ML) framework that predicts the degradation of protective coatings under various environmental conditions. By incorporating data on environmental stressors, changes in material properties, and corrosion indicators, the model offers a more accurate way to forecast coating failure.

  • 1 week ago | azom.com | Lexie Corner |Ibtisam Abbasi

    Reviewed by Lexie CornerIndustry and government efforts to transition away from fossil fuels are driving a sharp increase in demand for electric vehicle (EV) batteries. However, several challenges remain. These include concerns about battery reliability, supply chain limitations, environmental risks tied to raw materials, and high production costs. This article outlines key issues and recent developments in the EV battery sector.

  • 1 week ago | azonano.com | Lexie Corner |Noopur Jain

    Reviewed by Lexie CornerA recent article in Advanced Science reported a new method for incorporating lithium ions into CsPbBr₃ nanocrystals. This approach aims to improve their electronic properties for use in applications such as white light-emitting diodes (WLEDs). Image Credit: Gorodenkoff/Shutterstock.comEnhancing CsPbBr3 nanocrystalsWithin the field of optoelectronics, there is a significant focus on enhancing the electronic properties of (CsPbBr3) nanocrystals (NCs).

  • 1 week ago | azom.com | Lexie Corner |Muhammad Osama

    Reviewed by Lexie CornerA recent study published in Advanced Sustainable Systems introduced a data-driven method for designing better electrolytes for lithium metal batteries (LMBs). The framework combines computer simulations and machine learning (ML) to improve the stability and cycle life of lithium metal anodes. By predicting the performance of various lithium salts, the approach aims to address key challenges in energy storage and speed up the discovery of effective electrolyte materials.

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