A method for the quantitative extraction of gold nanoparticles from human bronchoalveolar lavage fluids through a glycerol gradient

  • Dimitrios Bitounis
  • , Vincent Barnier
  • , Cyril Guibert
  • , Jérémie Pourchez
  • , Valérie Forest
  • , Delphine Boudard
  • , Jean François Hochepied
  • , Pierre Chelle
  • , Jean Michel Vergnon
  • , Michèle Cottier

Research output: Contribution to journalArticlepeer-review

Abstract

Bronchoalveolar lavage (BAL) is a diagnostic procedure which samples the cellular and non-cellular components of the pulmonary epithelial surface. The inherent biological noise of BAL fluids inhibits their direct mineralogical analysis while currently available particle retrieval protocols are suspected to impose quantitative and qualitative bias on the studied particle load. This study presents a simple method for the near-lossless extraction of citrate-capped gold nanoparticles from human BAL fluids at sub-ppm levels which enables their quantitation and surface characterization. This procedure was modeled according to fundamental principles of particle sedimentation and liquid-liquid interdiffusion and was evaluated by a battery of analytical techniques. The extraction yield of gold nanoparticles ranged from 61 to 86%, with a quantitation limit at 0.5 μg ml-1, as measured by inductively-coupled optical emission spectroscopy. Dynamic light scattering could resolve the hydrodynamic size distribution of extracted particles which returned significantly different photon count rates at various concentrations. Their shape and primary size were easily observable by electron microscopy while atomic force microscopy, Auger electron spectroscopy and X-ray photoelectron spectroscopy could respectively probe the particles' biomolecular corona, detect surface-adsorbed S- and N- species, and identify carbon-based covalent bonds.

Original languageEnglish
Pages (from-to)2955-2969
Number of pages15
JournalNanoscale
Volume10
Issue number6
DOIs
Publication statusPublished - 14 Feb 2018
Externally publishedYes

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