Results 131 to 140 of about 486,367 (311)

Single‐Step Conversion of Metal Impurities in CNTs to Electroactive Metallic Nitride Nanoclusters for Electrochemical CO2 Reduction

open access: yesAdvanced Functional Materials, Volume 36, Issue 1, 2 January 2026.
A single‐step, low‐temperature co‐pyrolysis process removes encapsulated seed metal NPs (10–50 nm) from CNTs, redistributing them as surface‐anchored metal and metal–nitride NCs (1–1.5 nm). Herein, Ni3N NCs achieve an ultra‐low onset overpotential for CO2 reduction to CO with >98% Faradaic efficiency across 100–700 mA cm−2.
Ahmed Badreldin   +15 more
wiley   +1 more source

Advanced Surface Engineering and Passivation Strategies of Quantum Dots for Breaking Efficiency Barrier of Clean Energy Technologies: A Comprehensive Review

open access: yesAdvanced Functional Materials, Volume 36, Issue 4, 12 January 2026.
This review describes the different surface engineering strategies for quantum dots that addresses the challenges associated with surface defects, highlighting their role in enhancing the performance of solar energy conversion technologies. Abstract Colloidal quantum dots (QDs) have garnered significant attention for their unique potential in clean ...
Kokilavani S., Gurpreet Singh Selopal
wiley   +1 more source

Reassessing Electrolyte Design for Non‐Aqueous Magnesium Batteries: Atomistic Structures and Performance Optimization

open access: yesAdvanced Materials, Volume 38, Issue 5, 22 January 2026.
This review systematically examines recent advances in non‐aqueous electrolytes for rechargeable magnesium batteries, focusing on chlorine‐containing and chlorine‐free systems. It highlights the interdependent relationship between electrolyte compositions, atomistic structures, and electrochemical performance.
Hao Xu   +10 more
wiley   +1 more source

Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

open access: yesAdvanced Energy Materials, Volume 16, Issue 2, 14 January 2026.
This perspective highlights how machine learning accelerates sustainable energy materials discovery by integrating quantum‐accurate interatomic potentials with property prediction frameworks. The evolution from statistical methods to physics‐informed neural networks is examined, showcasing applications across batteries, catalysts, and photovoltaics ...
Kwang S. Kim
wiley   +1 more source

Childhood cancer risk in offspring of mothers occupationally exposed to hydrocarbon solvents. [PDF]

open access: yesEur J Cancer Prev
Chen Y   +5 more
europepmc   +1 more source

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