Author:
Takamura Ayari,Tsukamoto Kaede,Sakata Kenji,Kikuchi Jun
Abstract
AbstractIntegrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.
Publisher
Springer Science and Business Media LLC
Reference69 articles.
1. Ma, R., Huang, D., Zhang, T. & Luo, T. Determining influential descriptors for polymer chain conformation based on empirical force-fields and molecular dynamics simulations. Chem. Phys. Lett. 704, 49–54. https://doi.org/10.1016/j.cplett.2018.05.035 (2018).
2. Hayashi, Y. & Kawauchi, S. Development of a quantum chemical descriptor expressing aromatic/quinoidal character for designing narrow-bandgap π-conjugated polymers. Polym. Chem. 10, 5584–5593. https://doi.org/10.1039/C9PY00987F (2019).
3. Wu, S., Yamada, H., Hayashi, Y., Zamengo, M. & Yoshida, R. Potentials and challenges of polymer informatics: Exploiting machine learning for polymer design. (2020). arXiv preprint arXiv:2010.07683.
4. Kim, C., Chandrasekaran, A., Huan, T. D., Das, D. & Ramprasad, R. Polymer genome: A data-powered polymer informatics platform for property predictions. J. Phys. Chem. C 122, 17575–17585. https://doi.org/10.1021/acs.jpcc.8b02913 (2018).
5. Khan, P. M., Rasulev, B. & Roy, K. QSPR modeling of the refractive index for diverse polymers using 2D descriptors. ACS Omega 3, 13374–13386. https://doi.org/10.1021/acsomega.8b01834 (2018).
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