Abstract
The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co-N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic-structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co-N compounds with favorable magnetic properties including hexagonal Co3N nanoparticles with a high saturation magnetic polarization (Js = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy (K1 = 1.01 MJ m-3 or 10.1 Mergs per cm3). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies.
| Original language | English |
|---|---|
| Pages (from-to) | 13011-13021 |
| Number of pages | 11 |
| Journal | Nanoscale |
| Volume | 10 |
| Issue number | 27 |
| DOIs | |
| State | Published - Jul 21 2018 |
Funding
Experimental and theoretical works were supported by the National Science Foundation (NSF), Division of Materials Research (DMR), under the awards DMREF: SusChEM 1436385 and 1436386, respectively. Research at Nebraska was performed in part in the Nebraska Nanoscale Facility, Nebraska Center for Materials and Nanoscience, which is supported by the NSF under Award NNCI: 1542182, and the Nebraska Research Initiative (NRI). The work at ORNL’s HFIR was sponsored by the Scientific User Facilities Division, Office of Science, Basic Energy Sciences, U.S. Department of Energy. The development of adaptive genetic algorithm (AGA) method was supported by the US Department of Energy, Basic Energy Sciences, Division of Materials Science and Engineering, under Contract No. DE-AC02-07CH11358, including a grant of computer time at the National Energy Research Scientific Computing Center (NERSC) in Berkeley, CA. Authors thank Z. Sun and B. Das for technical assistance and helpful discussions.