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Lecture

WEB Multidimensional property characterization of nanoparticles by analytical ultracentrifugation



Knowledge about the size, shape, functionalization and optical properties of nanoparticles is of key importance for the design of particulate products. In our studies we could demonstrate that analytical ultracentrifugation (AUC) is a powerful tool for the multidimensional characterization of particles as it provides access to their hydrodynamic property distributions with unrivalled resolution and accuracy [1].

As a unique feature, hydrodynamic and optical properties can be linked using AUC equipped with multiwavelength extinction [2,3] or emission [4] detection. For quantum dots (QDs), either size-band-gap relations [5] or size dependent emission spectra [4] can be derived. AUC measures particle sizes ranging from Angstrom to micrometre range with excellent statistics. In addition, it provides information on the shape anisotropy of particles. For graphene oxide nanosheets, it was demonstrated that AUC is capable of resolving shape distributions with high accuracy, statistical confidence and experimental throughput [6]. For plasmonic nanorods, full 2D size and shape distributions are accessible [7]. Sedimentation analysis can further be applied to study the functionalization and stabilization of particles. While, a method was developed to determine the thickness of the organic stabilizing layer on QDs [8], the dynamic interaction between silica particles and soft PNiPAm microgels was probed via analytical centrifugation.

Our contribution will demonstrate that sedimentation analysis is a powerful technique, which provides multidimensional access to an extensive range of particulate systems not accessible so far by any other technique.


References:

[1] Walter et al., ACS Nano 2014, 8, 8871.

[2] Walter et al., Anal. Chem. 2015, 87, 3396.

[3] Pearson et al., Anal. Chem. 2018, 90, 1280.

[4] Wawra et al., Nanoscale Adv. 2019, 1, 11.

[5] Karabudak et al., Angew. Chem. Int. Ed. 2016, 55, 11770.

[6] Walter et al., Small 2015, 11, 814.

[7] Wawra et al., Nat. Commun. 2018, 9, 4898.

[8] Walter et al., Analyst 2017, 142, 206.

Speaker:
Dr.-Ing. Johannes Walter
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Additional Authors:
  • Simon Wawra
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
  • Maximilian Uttinger
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
  • Dr. Marcel Rey
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
  • Prof. Dr. Nicolas Vogel
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
  • Prof. Dr. Wolfgang Peukert
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)