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Journal of Materials Science

, Volume 52, Issue 9, pp 4894–4902 | Cite as

Dehydrogenation properties of ammonia borane–polyacrylamide nanofiber hydrogen storage composites

  • Krishna Kharel
  • Radhika Gangineni
  • Lauren Ware
  • Yang Lu
  • Evan K. Wujcik
  • Suying Wei
  • Özge Günaydın-Şen
Original Paper

Abstract

The current investigation seeks to measure the thermal and vibrational response of ammonia borane (NH3BH3, AB)/polyacrylamide (PAM, M n ~ 150,000) composites in bulk and electrospun fiber forms. The hydrogen release and melting temperature profiles for the composites were found to be lower than pristine AB. The kinetic analysis of the first dehydrogenation peak with respect to the heating ramp rates showed that the corresponding activation energy (E a) revealed the greatest decrease for the electrospun fibers (~61 kJ/mol), as compared to the bulk composites (~95 kJ/mol) and the pristine AB (~133 kJ/mol). Overall, the nanofibers showed the greatest decrease in E a, suggesting improved kinetic behavior. In addition to the enhanced kinetic properties, thermal gravimetric analysis showed significantly reduced weight loss for the composites. We have hypothesized that this is due to the suppression of the unwanted boracic byproducts and NH3. The weight loss decreased from 57.8% (AB) to 21.8% (fibers). Fourier-transform infrared study shows the interaction between the AB and PAM indication for the mentioned improvements. Decomposition IR studies revealed the disruption of the bonds with the broadening of the peaks and the disappearance of B–H stretch due to the dehydrogenation. These results imply that the novel composites revealed tuned properties by confining the AB molecules within the polymer matrix, having major implications in potential hydrogen storage applications.

Keywords

Dehydrogenation Hydrogen Storage Fiber Composite Thermal Gravimetric Analysis Nanofiber Composite 
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.

Notes

Acknowledgements

The authors acknowledge Dan Rutman who helped acquire the SEM images at Lamar University. This research was supported by Welch Foundation (V-0004) and Lamar University Research Enhancement Grant (REG-420240).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryLamar UniversityBeaumontUSA
  2. 2.Dan F. Smith Department of Chemical EngineeringLamar UniversityBeaumontUSA
  3. 3.Materials Engineering and Nanosensor [MEAN] Laboratory, Department of Chemical and Biological EngineeringThe University of AlabamaTuscaloosaUSA

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