W
Woo Youn Kim
Researcher at KAIST
Publications - 83
Citations - 5804
Woo Youn Kim is an academic researcher from KAIST. The author has contributed to research in topics: Density functional theory & Molecular electronics. The author has an hindex of 30, co-authored 79 publications receiving 4641 citations. Previous affiliations of Woo Youn Kim include Pohang University of Science and Technology.
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Non-Covalent Self-Assembly and Covalent Polymerization Co-Contribute to Polydopamine Formation
TL;DR: The study reveals a different perspective of polydopamine formation, where it forms in part by the self‐assembly of dopamine and DHI, providing a new clue toward understanding the structures of catecholamines such as melanin.
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Prediction of very large values of magnetoresistance in a graphene nanoribbon device.
Woo Youn Kim,Kwang S. Kim +1 more
TL;DR: First-principles simulations predict that spin-valve devices based on graphene nanoribbons will exhibit magnetoresistance values that are thousands of times higher than previously reported experimental values and it is shown that it is possible to manipulate the band structure of the nan oribbons to generate highly spin-polarized currents.
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Fast DNA sequencing with a graphene-based nanochannel device
TL;DR: It is shown that as a DNA strand passes through the nanochannel, the distinct conductance characteristics of the nanoribbon allow the different nucleobases to be distinguished using a data-mining technique and a two-dimensional transient autocorrelation analysis.
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Near-field focusing and magnification through self-assembled nanoscale spherical lenses
Ju Young Lee,Byung Hee Hong,Byung Hee Hong,Woo Youn Kim,Seung Kyu Min,Yukyung Kim,Mikhail V. Jouravlev,Ranojoy Bose,Keun Soo Kim,In-Chul Hwang,Laura J. Kaufman,Chee Wei Wong,Philip Kim,Kwang S. Kim +13 more
TL;DR: Lee et al. as mentioned in this paper used nanoscale spherical lenses that self-assemble by bottom-up integration of cup-shaped organic molecules called calixarenes to obtain near-field features of the order of 200 nm.
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Molecular generative model based on conditional variational autoencoder for de novo molecular design.
TL;DR: A molecular generative model based on the conditional variational autoencoder that can be used to generate drug-like molecules with five target properties and to adjust a single property without changing the others and to manipulate it beyond the range of the dataset is proposed.