2D verbeekite, the monoclinic phase of PdSe2, was directly synthesized under the Se deficient condition using an ambient-pressure chemical vapor deposition method.
Filter Research Highlights
Area of Research
- Biological Systems (1)
- Chemistry and Physics at Interfaces (8)
- Clean Energy (1)
- Computer Science (2)
- Functional Materials for Energy (11)
- Materials (130)
- Materials Characterization (7)
- Materials for Computing (22)
- Materials Synthesis from Atoms to Systems (3)
- Materials Theory and Simulation (3)
- Materials Under Extremes (2)
- Neutron Science (2)
- Nuclear Science and Technology (2)
- Nuclear Security Science and Technology (1)
- Quantum information Science (1)
- Supercomputing (1)
Magnons in a honeycomb-lattice ferromagnet have an analogous description to the single-orbital tight-binding model for electrons in graphene.
Molten salts attract resurgent attention because of their unique physiochemical properties, making them promising media for next generation concentrating solar power systems and molten salt reactors, but many fundamental questions remain unanswered.
New computational architectures based on topological materials have been proposed that could be faster with simultaneously lower energy consumption.
The concept of “frustration” in spin systems is widely used to stabilize new states in thin films or crystals. Frustration indicates that spins have conflicting tendencies and a compromise spin state emerges from this competition.
Kagome lattice (the name of kagome came from Japanese woven baskets) consists of interconnected triangles.
Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts.
Additively manufactured (AM) metal alloys by laser powder bed fusion (L-PBF) involve large temperature gradients and rapid cooling that enable microstructural refinement to the nanoscale for achieving high strength.
Researchers associated with the ExaAM project, a part of the Exascale Computing Project, developed ExaCA, a cellular automata (CA)-based model for grain-scale alloy solidification capable of simulation on both CPU and GPU architectures.
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy.