Intrinsic half-metallicity in fractal carbon nitride honeycomb lattices
Literature Information
Aizhu Wang, Mingwen Zhao
Fractals are natural phenomena that exhibit a repeating pattern “exactly the same at every scale or nearly the same at different scales”. Defect-free molecular fractals were assembled successfully in a recent work [Shang et al., Nature Chem., 2015, 7, 389–393]. Here, we adopted the feature of a repeating pattern in searching two-dimensional (2D) materials with intrinsic half-metallicity and high stability that are desirable for spintronics applications. Using first-principles calculations, we demonstrate that the electronic properties of fractal frameworks of carbon nitrides have stable ferromagnetism accompanied by half-metallicity, which are highly dependent on the fractal structure. The ferromagnetism increases gradually with the increase of fractal order. The Curie temperature of these metal-free systems estimated from Monte Carlo simulations is considerably higher than room temperature. The stable ferromagnetism, intrinsic half-metallicity, and fractal characteristics of spin distribution in the carbon nitride frameworks open an avenue for the design of metal-free magnetic materials with exotic properties.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.













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