All Outputs (3)

Identifying carbon as the source of visible single-photon emission from hexagonal boron nitride (2020)
Journal Article
Mendelson, N., Chugh, D., Reimers, J. R., Cheng, T. S., Gottscholl, A., Long, H., …Aharonovich, I. (2021). Identifying carbon as the source of visible single-photon emission from hexagonal boron nitride. Nature Materials, 20(3), 321-328. https://doi.org/10.1038/s41563-020-00850-y

Single-photon emitters (SPEs) in hexagonal boron nitride (hBN) have garnered increasing attention over the last few years due to their superior optical properties. However, despite the vast range of experimental results and theoretical calculations,... Read More about Identifying carbon as the source of visible single-photon emission from hexagonal boron nitride.

Step-Flow Growth of Graphene-Boron Nitride Lateral Heterostructures by Molecular Beam Epitaxy (2020)
Journal Article
Thomas, J., Bradford, J., Cheng, T. S., Summerfield, A., Wrigley, J., Mellor, C. J., …Beton, P. H. (2020). Step-Flow Growth of Graphene-Boron Nitride Lateral Heterostructures by Molecular Beam Epitaxy. 2D Materials, 7(3), Article 035014. https://doi.org/10.1088/2053-1583/ab89e7

Integration of graphene and hexagonal boron nitride (hBN) into lateral heterostructures has drawn focus due to the ability to broadly engineer the material properties. Hybrid monolayers with tuneable bandgaps have been reported, while the interface i... Read More about Step-Flow Growth of Graphene-Boron Nitride Lateral Heterostructures by Molecular Beam Epitaxy.

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging (2020)
Journal Article
Cheng, T., Conselice, C. J., Aragón-Salamanca, A., Li, N., Bluck, A. F., Hartley, W. G., …Tarle, G. (2020). Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. Monthly Notices of the Royal Astronomical Society, 493(3), 4209-4228. https://doi.org/10.1093/mnras/staa501

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation... Read More about Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging.