Journal of Materials Science

, Volume 50, Issue 4, pp 1544–1552 | Cite as

Effect of molecular weight on conformational changes of PEO: an infrared spectroscopic analysis

  • Shuyan Yang
  • Zhimeng Liu
  • Yuping Liu
  • Yuanqi Jiao
Original Paper


Effect of molecular weight on conformation, helix structure (H structure) and trans planar structure (T structure), of Poly(ethylene oxide) (PEO) has been investigated in detail by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimeter. Two main diffraction peaks at about  = 19° and 23° are discovered, and XRD patterns reveal that the unit cell of crystalline PEO belongs to the monoclinic lattice. The crystallinity decreases from 93.82 to 59.62 %, and the deviation of crystalline temperature of PEO-0.5 is larger than those of the other three under four reheated cycles. From FTIR results, a red shift about 11 cm−1 is observed in the stretching vibration of –C–O–C– with increasing molecular weight, suggesting the presence of chain–chain interactions to restrict the stretching vibration of -C–O–C– in main chains. Meanwhile, the bending region of –C–C–O– at about 533 cm−1 sensitive to tension shifts to lower wavenumber, and a new peak at about 510 cm−1 emerges with increasing molecular weight, which is the indicator of internal tension/strain and orientation. Furthermore, the peak intensity ratios of H structure decrease with increasing molecular weight. In contrast, T structure increases dramatically. Consequently, with respect to molecular weight, the possible interactions, entanglements and tie molecules, of PEO molecular chains to explain the difference between H and T structure is proposed, which is in agreement with the experimental observations quite well.


Polymer Electrolyte Peak Intensity Ratio Lamellar Crystal Differential Scanning Calorimeter Measurement Monoclinic Crystal Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the financial support from The National Natural Science Funds of China (No.51303026) and the foundation of Chemical Industrial Cleaner Production and Green Chemical R&D Center of Guang Dong Universities (201401).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Shuyan Yang
    • 1
    • 2
  • Zhimeng Liu
    • 1
    • 2
  • Yuping Liu
    • 1
    • 2
  • Yuanqi Jiao
    • 1
    • 2
  1. 1.Chemical Industrial Cleaner Production and Green Chemical R&D Center of Guang Dong UniversitiesDongguan University of TechnologyDongguanPeople’s Republic of China
  2. 2.Dongguan Cleaner Production CenterDongguanPeople’s Republic of China

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