Journal of Materials Science

, Volume 47, Issue 16, pp 6066–6075 | Cite as

Bacterial cellulose-based membrane-like biodegradable composites using cross-linked and noncross-linked polyvinyl alcohol

  • Kaiyan Qiu
  • Anil N. NetravaliEmail author


Bacterial cellulose (BC)-based membrane-like biodegradable composites were produced by immersing wet BC pellicles in polyvinyl alcohol (PVA) solution. The BC content in the BC–PVA composites can be adjusted by varying the concentration of PVA solution. Chemical cross-linking of PVA was carried out using glutaraldehyde to increase the mechanical properties of the composites as well as to make the PVA partially to highly water insoluble. Examination by scanning electron microscopy indicated that the PVA not only penetrated the BC network, but also filled the pores within the BC pellicle. Attenuated total reflectance-Fourier transform infrared spectroscopy showed that acetal linkages could be formed in the BC–PVA composites by a cross-linking reaction. Sol–gel results indicated that cross-linking reaction increasingly made PVA insoluble in water resulting in higher gel (cross-linked fraction) content in the PVA. Wide-angle X-ray diffraction results showed decreased crystallinity in cross-linked BC and PVA, as expected. It was also found that crystal size was smaller in PVA after cross-linking. The BC–PVA composites had excellent tensile properties and cross-linking increased these properties further. Thermogravimetric analysis showed higher thermal stability for BC–PVA composites compared to PVA. The cross-linked specimens, especially the highly cross-linked ones, showed even higher thermal stability. The methods developed in this study make it possible to control the PVA content in the composites as well as the cross-linking level of PVA. These composites could be good candidates for replacing traditional non-biodegradable plastics.


Fracture Stress Bacterial Cellulose Bacterial Cellulose Production Bacterial Cellulose Pellicle Acetal Linkage 
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.



This work was partly supported by the National Textile Center (NTC) and the Wallace Foundation. The authors would like to thank Profs. Dan Luo, John March, and Antje Baeumner of the Cornell University for allowing the use of their laboratory facilities. The authors also thank the Cornell Center for Materials Research (CCMR) for the use of their facilities.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Fiber Science ProgramCornell UniversityIthacaUSA

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