Showing results (1-10 of 333) with videos related to
Sort By:
Pageof 34
International Journal of Molecular Sciences|January 27, 2021
Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component MaterialsShunji Yamada, Eisuke Chikayama, Jun KikuchiInternational Journal of Molecular Sciences|April 29, 2020
Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic Sparse Matrix FactorizationShunji Yamada, Atsushi Kurotani, Eisuke Chikayama, et al.Scientific Reports|June 22, 2022
Materials informatics approach using domain modelling for exploring structure-property relationships of polymersKoki Hara, Shunji Yamada, Atsushi Kurotani, et al.ACS Omega|August 29, 2019
InterSpin: Integrated Supportive Webtools for Low- and High-Field NMR Analyses Toward Molecular ComplexityShunji Yamada, Kengo Ito, Atsushi Kurotani, et al.Biomacromolecules|April 12, 2012
Solubilization mechanism and characterization of the structural change of bacterial cellulose in regenerated states through ionic liquid treatmentKeiko Okushita, Eisuke Chikayama, Jun KikuchiAnalytical Chemistry|January 7, 2011
Evaluation of a semipolar solvent system as a step toward heteronuclear multidimensional NMR-based metabolomics for 13C-labeled bacteria, plants, and animalsYasuyo Sekiyama, Eisuke Chikayama, Jun KikuchiAnalytical Chemistry|February 4, 2010
Profiling polar and semipolar plant metabolites throughout extraction processes using a combined solution-state and high-resolution magic angle spinning NMR approachYasuyo Sekiyama, Eisuke Chikayama, Jun KikuchiThe Journal of Physical Chemistry. B|March 11, 2016
The Effect of Molecular Conformation on the Accuracy of Theoretical (1)H and (13)C Chemical Shifts Calculated by Ab Initio Methods for Metabolic Mixture AnalysisEisuke Chikayama, Yudai Shimbo, Keiko Komatsu, et al.RSC Advances|April 28, 2022
The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data scienceJun Kikuchi, Shunji YamadaChemical Science|December 14, 2018
Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signalsKengo Ito, Yuka Obuchi, Eisuke Chikayama, et al.Pageof 34