journal article·2023-03-28

Determination of 2D Particle Size Distributions in Plasmonic Nanoparticle Colloids via Analytical Ultracentrifugation: Application to Gold Bipyramids

Uwe Frank, Dominik Drobek, Ana Sánchez-Iglesias, Simon E. Wawra, Nico Nees, Johannes Walter, Lukas Pflug, Benjamin Apeleo Zubiri, Erdmann Spiecker, Luis M. Liz-Marzán, Wolfgang Peukert

ACS Nano· Volume 17· Issue 6
Publisher page

Abstract

Multidimensional particle properties determine the product properties in numerous advanced applications. Accurate and statistically meaningful measurements of complex particles and their multidimensional distributions are highly challenging but strongly needed. 2D particle size distributions of plasmonic nanoparticles of complex regular shape can be obtained from analytical ultracentrifugation experiments via the optical back coupling method. A workflow for the calculation of frictional properties of arbitrarily shaped nanoparticles was developed based on bead shell models and applied to gold bipyramids with a pentagonal cross-section. The obtained 2D particle length-diameter distributions and the reduced cumulative 1D length and diameter distributions were compared to transmission electron microscopy measurements. While we find very good agreement for most measurements, the obtained length and diameter distributions were shifted by a few nanometers for some samples. Transmission electron microscopy, energy-dispersive X-ray spectroscopy, electron tomography, and finite element modeling indicate that the shift originated from a slight mismatch between the assumed shape of the simulated perfect bipyramids and the real particle shape and composition due to the presence of silver in the particles. This study demonstrates the feasibility of the applied techniques for complex shape analysis of nanoparticle ensembles with unmatched particle count numbers.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Coagulation and Flocculation Studies · nanoparticles nucleation surface interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Particle Technology (LFG), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 4, 91058 Erlangen, Germany

Institute of Micro- and Nanostructure Research and Center for Nanoanalysis and Electron Microscopy (CENEM), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 3, 91058 Erlangen, Germany

CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Paseo de Miramon 194, 20014 Donostia-San Sebastián, Spain

Centro de Investigación en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Paseo de Miramon 194, 20014 Donostia-San Sebastián, Spain

Department of Mathematics, Chair of Applied Mathematics (Continuous Optimization), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 11, 91058 Erlangen, Germany

Interdisciplinary Center for Functional Particle Systems (FPS), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Haberstraße 9a, 91058 Erlangen, Germany

Competence Unit for Scientific Computing, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 5a, 91058 Erlangen, Germany

Interdisciplinary Center for Nanostructure Films (IZNF), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstraße 3, 91058 Erlangen, Germany

Ikerbasque, Basque Foundation for Science, 43009 Bilbao, Spain

Funding

Deutsche Forschungsgemeinschaft · Agencia Estatal de Investigaci?n,Ministerio de Ciencia, Innovaci?n y Universidades

References (53)

Show source-provided references
1. 10.14356/kona.202200510.14356/kona.2022005 ↗
2. 10.1080/17415977.2020.184918110.1080/17415977.2020.1849181 ↗
3. 10.1002/ppsc.20180055410.1002/ppsc.201800554 ↗
4. 10.1007/s12551-013-0106-210.1007/s12551-013-0106-2 ↗
5. 10.1021/la404892k10.1021/la404892k ↗
6. 10.1021/ac501722r10.1021/ac501722r ↗
7. 10.1002/smll.20140194010.1002/smll.201401940 ↗
8. 10.1038/ncomms133810.1038/ncomms1338 ↗
9. 10.1039/C6AN02236G10.1039/C6AN02236G ↗
10. 10.1007/s00396-007-1815-510.1007/s00396-007-1815-5 ↗
11. 10.1021/nn503205k10.1021/nn503205k ↗
12. 10.1021/ac504649c10.1021/ac504649c ↗
13. Pearson, J. Z.; Krause, F.; Haffke, D.; Demeler, B.; Schilling, K.; Cölfen, H. In Methods in Enzymology; Cole, J. L., Ed. Academic Press, 2015; Vol. 562; pp 1–26.10.1016/bs.mie.2015.06.033 ↗
14. Gorbet, G. E.; Pearson, J. Z.; Demeler, A. K.; Cölfen, H.; Demeler, B. In Methods in Enzymology; James, L. C., Ed. Academic Press, 2015; Vol. Vol. 562; pp 27–47.10.1016/bs.mie.2015.04.013 ↗
15. 10.1002/anie.20160384410.1002/anie.201603844 ↗
16. 10.1021/acs.analchem.7b0405610.1021/acs.analchem.7b04056 ↗
17. 10.1038/s41467-018-07366-910.1038/s41467-018-07366-9 ↗
18. 10.1039/D2NR02633C10.1039/D2NR02633C ↗
19. 10.1002/adom.20140050510.1002/adom.201400505 ↗
20. 10.1002/smll.20070037910.1002/smll.200700379 ↗
21. 10.1021/la300210h10.1021/la300210h ↗
22. 10.1039/C4NR06323F10.1039/C4NR06323F ↗
23. 10.1021/acsnano.5b0620610.1021/acsnano.5b06206 ↗
24. Murthy S. K. · Int. J. Nanomedicine · 2007 · 2 · 129
25. 10.1039/c2cp43642f10.1039/c2cp43642f ↗
26. Peukert, W.; Segets, D.; Pflug, L.; Leugering, G. In Mesoscale Modeling in Chemical Engineering Part I; Marin, G. B., Li, J., Eds.; Advances in Chemical Engineering; Academic Press, 2015; Vol. 46; pp 1–81.10.1016/bs.ache.2015.10.004 ↗
27. 10.1016/S0006-3495(99)77457-610.1016/S0006-3495(99)77457-6 ↗
28. 10.1007/s00249-003-0292-010.1007/s00249-003-0292-0 ↗
29. 10.1039/C6CS00919K10.1039/C6CS00919K ↗
30. 10.1364/JOSAA.11.00149110.1364/JOSAA.11.001491 ↗
31. 10.1039/C5NR08547K10.1039/C5NR08547K ↗
32. Bohren C. F. · 199810.1002/9783527618156 ↗
33. 10.1063/1.180353310.1063/1.1803533 ↗
34. 10.1051/jphysrad:0193400501004970010.1051/jphysrad:01934005010049700 ↗
35. 10.6028/jres.122.02010.6028/jres.122.020 ↗
36. Aragon, S. R. Analytical Ultracentrifugation; Springer Japan, 2016; pp 219–247.10.1007/978-4-431-55985-6_12 ↗
37. 10.1103/PhysRevB.6.437010.1103/PhysRevB.6.4370 ↗
38. 10.1021/jacs.6b1214310.1021/jacs.6b12143 ↗
39. 10.1016/j.scriptamat.2020.10.00210.1016/j.scriptamat.2020.10.002 ↗
40. 10.1039/B708958A10.1039/B708958A ↗
41. 10.1021/jp200642d10.1021/jp200642d ↗
42. 10.1039/D0NR05061J10.1039/D0NR05061J ↗
43. 10.1021/nn405941m10.1021/nn405941m ↗
44. 10.1002/adfm.20060107110.1002/adfm.200601071 ↗
45. 10.1093/jmicro/dfh07110.1093/jmicro/dfh071 ↗
46. 10.1023/B:VISI.0000029664.99615.9410.1023/B:VISI.0000029664.99615.94 ↗
47. 10.1109/TCI.2014.237175110.1109/TCI.2014.2371751 ↗
48. Sommer C. · 2011
49. Semmler, J.; Pflug, L.; Stingl, M.; Leugering, G. New Trends in Shape Optimization; Springer, 2015; pp 251–269.10.1007/978-3-319-17563-8_11 ↗
50. 10.1108/0332164051058601510.1108/03321640510586015 ↗
51. 10.1006/jcph.1994.115910.1006/jcph.1994.1159 ↗
52. HSL. A collection of Fortran codes for large scale scientific computation. http://www.hsl.rl.ac.uk/ (accessed: 2015-01-10).
53. 10.1137/09075721610.1137/090757216 ↗