Zobrazeno 1 - 10
of 2 023
pro vyhledávání: '"Murray, Christopher P."'
Autor:
Murray, Christopher P., Mamyraimov, Daniyar, Ali, Mugahid, Downing, Clive, Povey, Ian M., McCloskey, David, O'Regan, David D., Donegan, John F.
Publikováno v:
ACS Appl. Electron. Mater. 2023, 5, 4080-4093
Next-generation heat-assisted magnetic recording (HAMR) relies on fast, localized heating of the magnetic medium during the write process. Au plasmonic near-field transducers are an attractive solution to this challenge, but increased thermal stabili
Externí odkaz:
http://arxiv.org/abs/2412.14101
Autor:
Marino, Emanuele, LaCour, R. Allen, Moore, Timothy C., van Dongen, Sjoerd W., Keller, Austin W., An, Di, Yang, Shengsong, Rosen, Daniel J., Gouget, Guillaume, Tsai, Esther H. R., Kagan, Cherie R., Kodger, Thomas E., Glotzer, Sharon C., Murray, Christopher B.
Publikováno v:
Nature Synthesis 3, 111-122, 2024
The synthesis of binary nanocrystal superlattices (BNSLs) enables the targeted integration of orthogonal physical properties, like photoluminescence and magnetism, into a single superstructure, unlocking a vast design space for multifunctional materi
Externí odkaz:
http://arxiv.org/abs/2410.17016
Autor:
Ducellier, Ariane, Hsu, Alexander, Kendrick, Parkes, Gustafson, Bill, Dwyer-Lindgren, Laura, Murray, Christopher, Zheng, Peng, Aravkin, Aleksandr
We consider statistical inference problems under uncertain equality constraints, and provide asymptotically valid uncertainty estimates for inferred parameters. The proposed approach leverages the implicit function theorem and primal-dual optimality
Externí odkaz:
http://arxiv.org/abs/2407.20520
Autor:
Zheng, Peng, Worku, Nahom, Bannick, Marlena, Dielemann, Joseph, Weaver, Marcia, Murray, Christopher, Aravkin, Aleksandr
Benchmarking tools, including stochastic frontier analysis (SFA), data envelopment analysis (DEA), and its stochastic extension (StoNED) are core tools in economics used to estimate an efficiency envelope and production inefficiencies from data. The
Externí odkaz:
http://arxiv.org/abs/2404.04301
Autor:
Ye, Xingchen, Chen, Jun, Irrgang, M. Eric, Engel, Michael, Dong, Angang, Glotzer, Sharon C., Murray, Christopher B.
Publikováno v:
Nature Materials 16, 214-219 (2017)
Expanding the library of self-assembled superstructures provides insight into the behavior of atomic crystals and supports the development of materials with mesoscale order. Here we build upon recent findings of soft matter quasicrystals and report a
Externí odkaz:
http://arxiv.org/abs/2308.03551
Autor:
Thompson, Sarah M., Şahin, Cüneyt, Yang, Shengsong, Flatté, Michael E., Murray, Christopher B., Bassett, Lee C., Kagan, Cherie R.
Copper-doped zinc sulfide (ZnS:Cu) exhibits down-conversion luminescence in the UV, visible, and IR regions of the electromagnetic spectrum; the visible red, green, and blue emission is referred to as R-Cu, G-Cu, and B-Cu, respectively. The sub-bandg
Externí odkaz:
http://arxiv.org/abs/2301.04223
Autor:
Rahaman, Mahfujur, Marino, Emanuele, Joly, Alan G., Song, Seunguk, Jiang, Zhiqiao, OCallahan, Brian T., Rosen, Daniel J., Jo, Kiyoung, Kim, Gwangwoo, El-Khoury, Patrick Z., Murray, Christopher B., Jariwala, Deep
Observation of interlayer, charge-transfer (CT) excitons in van der Waals heterostructures (vdWHs) based on 2D-2D systems has been well investigated. While conceptually interesting, these charge transfer excitons are highly delocalized and spatially
Externí odkaz:
http://arxiv.org/abs/2210.12608
Autor:
Jo, Kiyoung, Marino, Emanuele, Lynch, Jason, Jiang, Zhiqiao, Gogotsi, Natalie, Darlington, Thomas P., Soroush, Mohammad, Schuck, P. James, Borys, Nicholas J., Murray, Christopher, Jariwala, Deep
Strong light-matter interactions in localized nano-emitters when placed near metallic mirrors have been widely reported via spectroscopic studies in the optical far-field. Here, we report a near-field nano-spectroscopic study of the localized nanosca
Externí odkaz:
http://arxiv.org/abs/2208.09766
Autor:
Poelking, Carl, Chessari, Gianni, Murray, Christopher W., Hall, Richard J., Colwell, Lucy, Verdonk, Marcel
Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if they provide case-specific insight into the physical interactions that
Externí odkaz:
http://arxiv.org/abs/2204.06348
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