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Search Publications by: Winnie Wong-Ng (Assoc)

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Displaying 26 - 50 of 549

Nano/Bioscience and Technology Education for the 21st Century

October 12, 2021
Author(s)
J Jacobs, Winnie Wong-Ng, Gale A. Holmes, Richard F. Kayser
NEW:Update 2005, the 20th annual workshops for science and technology educators with the theme, NANO/BIOSCIENCE AND TECHNOLOGY EDUCATION FOR THE 21ST CENTURY, was hosted by the National Institute of Standards and Technology/Materials Science and

Processing of High Temperature Ceramic Superconductors

October 12, 2021
Author(s)
P Goyal, Winnie Wong-Ng, Y Murakami, D Driscoll
The contents of this transaction volume comprise the proceedings of the Electronics Division Focused Session HighTemperature Superconductor Processing during the American Ceramic Society annual meeting at St. Louis, MO, from April29 to May 1, 2002.. A

Structure and Crystal Chemistry of M 0.5 Ta 0.5 B (M = Ti, Zr, and Hf)

October 12, 2021
Author(s)
I Talmy, J Zaykoski, J A. Kudak, Winnie Wong-Ng
During the development of refractory composites (cermets) combining MB2 (M=Ti, Zr, or Hf) ceramics and Ta metal, three new phases of compositions M0.5Ta0.5B were discovered. Preliminary determination of lattice parameters using x-ray powder diffraction

Metal-Insulator transition in Doped Barium Plumbate

September 16, 2021
Author(s)
Andreza M. Eufrasio, Ian L. Pegg, Nikolaus Deems, Biprodas Dutta, Winnie Wong-Ng, Qingzhen Huang
Solid solutions in the Ba(Pb1−xSrx)O3−z system were prepared by aliovalent substitution of Pb4+ by Sr2+ ions to investigate the effect of cation stoichiometry on thermal and electrical properties as x was varied between 0 and 0.4, in the temperature range

First principles study of the quasi one-dimensional organic-inorganic hybrid perovskites (MV)AI3Cl2 (A = Bi, Sb) as promising candidates for ferroelectric photovoltaic and high-density memory applications

August 20, 2021
Author(s)
Chao Wang, Yunlin Lei, Winnie Wong-Ng, Xingxing Wu, Qiang Gu, Wei Zhou, Shouyu Wang, Weifang Liu
Many ferroelectric (FE) organic-inorganic hybrid perovskites (OIHPs) show great promise in the fields of photovoltaic cells and information memory devices. We systematically investigated the FE, optical, and electric properties of quasi-one-dimensional

Crystal Structure, Sorption Property and Electronic Structure of the Flexible MOF, (Ni-4,4'azopyridine)[Ni(CN)4]

August 16, 2021
Author(s)
Winnie Wong-Ng, Gregory T. McCandless, Jeffrey T. Culp, Matthew Lawson, Yu-Sheng Chen, Daniel Siderius, Lan Li
The flexible metal organic framework (MOF) compound, Ni(L)[Ni(CN)4], (L = 4,4'azopyridine (C10N4H8), nicknamed AzoPyr) is a 3D porous material that adopts the Hofmann-type structure. This paper reports our synthesis of Ni-AzoPyr and its structural, bonding

Density Functional Theory Study of the Structure of the Pillared Hofmann compound Ni(3-Methy-4,4'-bipyridine)[Ni(CN)4] (Ni-BpyMe or PICNIC 21)

July 14, 2021
Author(s)
Eric J. Cockayne, Winnie Wong-Ng, Yu-Sheng Chen, Jeffrey T. Culp, Andrew J. Allen
We use dispersion-corrected density functional theory (DFT) to investigate the structure of the pillared Hofmann compound Ni(3-Methy-4,4'-bipyridine)[Ni(CN)4] (NiBpyMe for short, or PICNIC 21). We model the disorder found in experimental X-ray structure

Structural and Thermoelectric Properties of Pb4In2.6Bi3.4Se13

July 2, 2021
Author(s)
Winnie Wong-Ng, J Guo, Yonggao Yan, James A. Kaduk
Quaternary selenide, Pb4In2.6Bi3.4Se13 (x = 2.4 member of the Pb4(InxBi6-xSe13 solid solution), was synthesized by a solid-state technique, and its structure was determined using powder X-ray diffraction (XRD). Pb4In2.6Bi3.4Se13 crystallizes in the

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

June 9, 2021
Author(s)
Jason Hattrick-Simpers, Brian DeCost, Aaron Gilad Kusne, Howard Joress, Winnie Wong-Ng, Debra Kaiser, Andriy Zakutayev, Caleb Phillips, Tonio Buonassisi, Shijing Sun, Janak Thapa
Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts