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Showing 1–7 of 7 results
Advanced filters: Author: Heechan Lee Clear advanced filters

  • Current AI methods struggle to accurately predict the protein folding stability. Here, the authors introduce IFUM, a deep learning model that jointly predicts unfolding free energy and folded–unfolded ensembles, enabling accurate stability estimation and guiding protein design beyond existing AIs.

    • Heechan Lee
    • Yugyeong Cho
    • Hahnbeom Park
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-12

  • Commonly, large π-conjugated systems facilitate low-energy electronic transitions. Here, the authors demonstrate that the relief of excited-state antiaromaticity of the benzene core leads to large Stokes shifts, and allows the construction of emitters covering the entire visible spectrum without the need of extending π-conjugation.

    • Heechan Kim
    • Woojin Park
    • Dongwhan Lee
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-9

  • Unidirectional spin Hall magnetoresistance (USMR) is a directionally dependent feature of a ferromagnetic/normal metal bilayer for which the underlying mechanisms are still under debate. Here, the authors investigate the crystallographic dependence of USMR in epitaxial Cr/Fe bilayers finding that electron-magnon scattering plays an important role.

    • Thanh Huong Thi Nguyen
    • Van Quang Nguyen
    • Sanghoon Kim
    ResearchOpen Access
    Communications Physics
    Volume: 4, P: 1-8

  • In this study, a novel sensing strategy to identify gas species selectively and to estimate the concentrations of multiple gases was proposed. Highly sensitive gold NPs coated GLAD In2O3 was used as a gas-sensing material, and time-variant pseudorandom illumination of a single monolithic micro-LED (μLED) photoactivated (μLP) gas sensor was applied. Transient sensor signals, owing to the rapid changes in the light intensity of μLED and different reaction kinetics of various gas species, facilitate the identification of gas species. A deep convolutional neural network (CNN) was used to effectively analyze the complex frequency spectrogram of the transient sensor signals. As such, the identification of four mono-gas environments and binary gas mixtures (mixed ethanol and methanol) were successfully demonstrated with high accuracy. The total power consumption of the μLP was only 0.53 mW, one-hundredth of the conventional electronic nose (e-nose) system. Therefore, the proposed method may significantly improve the efficiency of e-nose technology in terms of cost, space, and power consumption.

    • Incheol Cho
    • Kichul Lee
    • Inkyu Park
    ResearchOpen Access
    Light: Science & Applications
    Volume: 12, P: 1-12