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Nano-particle dynamics during capillary suction.

C J Kuijpers1, H P Huinink1, N Tomozeiu2

  • 1Applied Physics Department, Eindhoven University of Technology, PO Box 513, 5600MB Eindhoven, The Netherlands.

Journal of Colloid and Interface Science
|March 19, 2018
PubMed
Summary

A new model describes nanoparticle retention in porous materials during liquid transport. This research helps predict how nanoparticles move and bind in systems like filters, crucial for nanotechnology applications.

Keywords:
Darcy’s lawFe(2)O(3) nanoparticlesNMR imagingParticle adsorptionPorous mediaSintered Al(2)O(3)

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Physics

Background:

  • Increasing use of nanoparticles in consumer products necessitates understanding their behavior in porous media.
  • Existing models lack comprehensive descriptions of nanoparticle transport and retention.

Purpose of the Study:

  • To develop a one-dimensional theoretical model for nanoparticle retention during capillary transport in porous media.
  • To experimentally validate the model using water-glycerol-nanoparticle mixtures and porous alumina (Al2O3) samples.

Main Methods:

  • Preparation of water-glycerol-nanoparticle mixtures.
  • Nuclear Magnetic Resonance (NMR) imaging to monitor liquid and particle front penetration in Al2O3 samples.
  • Utilizing T2 relaxation effects from paramagnetic nanoparticles (Fe2O3) to track particle movement.

Main Results:

  • Experimental data showed good agreement with the developed theoretical model for particle retention.
  • The model allowed determination of the binding constant for Fe2O3 nanoparticles on Al2O3 and maximum surface coverage.
  • The liquid front penetration followed a square root of time behavior, but a simple scaling with liquid parameters was insufficient for different mixtures.

Conclusions:

  • The developed model accurately predicts nanoparticle retention in porous media.
  • The Darcy's law model requires extension to account for particle-laden and particle-free domains for accurate liquid front prediction.
  • This work provides a theoretical framework for understanding and predicting nanoparticle behavior in complex porous systems.