4.6 Article

Tailoring the electrical resistivity of zeolite Y by carbon addition to allow resistive heating

期刊

JOURNAL OF MATERIALS CHEMISTRY A
卷 6, 期 25, 页码 12082-12090

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ROYAL SOC CHEMISTRY
DOI: 10.1039/c8ta01882k

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  1. Natural Science and Engineering Research Council (NSERC) of Canada
  2. Alberta Innovates Technology

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Resistive heating or electrothermal regeneration is a valuable alternative to conventional adsorbent regeneration methods because of cost, energy, and time efficiency; however, it only works for electrically conductive adsorbents such as activated carbon. Zeolites are widely used adsorbents for gas separation; however their high resistivity prevents their regeneration with resistive heating. This paper describes a proof of concept study assessing the feasibility and performance of multiple carbon loading techniques to decrease zeolite resistivity and allow its resistive heating without sacrificing its adsorption properties. Carbon was added to zeolite by physical mixing, chemical vapor deposition of ethanol and benzene, carbon nanotube growth via catalytic CH4 decomposition, and thermal decomposition of polyvinyl-alcohol. Similar thermal conditions were applied (2 hours at 600 degrees C) for all carbon deposition methods to avoid the impact of multiple variables. Elemental analyses and scanning electron micrographs confirmed the addition of carbon, and resistivity decreased by up to 8 orders of magnitude. Resistivity decrease was greatest when carbon was added directly to the zeolite's surface compared to physical mixing with zeolite. Carbon nanotube growth decreased zeolite resistivity from >10(7) m to 0.7 m, but caused partial loss of adsorption capacity. The reduction in zeolite resistivity, however, allowed resistive heating of zeolite at a heating rate as high as 120 degrees C min(-1). This work presents CNT-addition as a potential method to allow resistive heating for zeolite adsorbent regeneration. By considering multiple carbon addition methods and identifying CNT-addition as preferred, these findings inform future research efforts that will focus on optimization and application testing.

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