Detection, characterization, and abundance of engineered nanoparticles in complex waters by hyperspectral imagery with enhanced Darkfield microscopy.

Published

Journal Article

We introduce a novel methodology based on hyperspectral imagery with enhanced Darkfield microscopy for detection, characterization, and analysis of engineered nanoparticles in both ultrapure water and in complex waters, such as simulated-wetland ecosystem water and wastewater. Hyperspectral imagery analysis of 12 different nanoparticle sample types, scattering the obliquely incident visible and near-infrared light (VNIR: 400-1000 nm) in an enhanced Darkfield background, showed that the sample information in terms of the spatial distribution as well as spectral characteristics unique to each nanoparticle types, at a sensitivity of single nanoparticle (size ≥10 nm) can be obtained. Hyperspectral imagery and Raman spectral analyses of the silver nanoparticles (AgNPs) revealed that the apparent hydrodynamic size of the particle increased while the primary size remained unchanged in the presence of coatings, which is further confirmed by dynamic light scattering measurements. Similar in size, AgNPs with different coatings exhibited similar spectral color (or peak position) but a red-shift in the peak positions by same amount relative to Bare AgNPs was observed. In conclusion, hyperspectral imagery with enhanced Darkfield microscopy can be a promising tool for detection and characterization of engineered nanoparticles in environmental systems, facilitating studies on fate and transformation of these particles in various types of water samples.

Full Text

Duke Authors

Cited Authors

  • Badireddy, AR; Wiesner, MR; Liu, J

Published Date

  • September 4, 2012

Published In

Volume / Issue

  • 46 / 18

Start / End Page

  • 10081 - 10088

PubMed ID

  • 22906208

Pubmed Central ID

  • 22906208

Electronic International Standard Serial Number (EISSN)

  • 1520-5851

International Standard Serial Number (ISSN)

  • 0013-936X

Digital Object Identifier (DOI)

  • 10.1021/es204140s

Language

  • eng