Journal Article FZJ-2016-06160

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Do Goethite Surfaces Really Control the Transport and Retention of Multi-Walled Carbon Nanotubes in Chemically Heterogeneous Porous Media?

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2016
American Chemical Society Columbus, Ohio

Environmental science & technology 50(23), 12713–12721 () [10.1021/acs.est.6b03285]

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Abstract: Transport and retention behavior of multi-walled carbon nanotubes (MWCNTs) was studied in mixtures of negatively charged quartz sand (QS) and positively charged goethite-coated sand (GQS) to assess the role of chemical heterogeneity. The linear equilibrium sorption model provided a good description of batch results, and the distribution coefficients (KD) drastically increased with the GQS fraction that was electrostatically favorable for retention. Similarly, retention of MWCNTs increased with the GQS fraction in packed column experiments. However, calculated values of KD on GQS were around 2 orders of magnitude smaller in batch than packed column experiments due to differences in lever arms associated with hydrodynamic and adhesive torques at microscopic roughness locations. Furthermore, the fraction of the sand surface area that was favorable for retention (Sf) was much smaller than the GQS fraction because nanoscale roughness produced shallow interactions that were susceptible to removal. These observations indicate that only a minor fraction of the GQS was favorable for MWCNT retention. These same observations held for several different sand sizes. Column breakthrough curves were always well described using an advective-dispersive transport model that included retention and blocking. However, depth-dependent retention also needed to be included to accurately describe the retention profile when the GQS fraction was small. Results from this research indicate that roughness primarily controlled the retention of MWCNTs, although goethite surfaces played an important secondary role.

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Contributing Institute(s):
  1. Agrosphäre (IBG-3)
Research Program(s):
  1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2016
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