Challenges in nanoparticle technology – New prospects inflow syntheses and property classification
Wolfgang Peukert
Institute of Particle Technology, Center of Functional Particle Systems
Friedrich-Alexander University Erlangen-Nürnberg (FAU)
Erlangen, Germany
Email:

wolfgang.peukert@fau.deAbstract

A long-term vision in nanoparticle science and technology is the targeted design of particulate
products by rigorous optimization based on predictive structure-property and process-structure
functions. Particulate products consist in the simplest case of dispersed single particles and in
more complex cases of hierarchically organized assemblies of particles in the form functional thinfilms or other formulated products, e.g. for pharmaceutical applications. We target scientificbreakthroughs in the product engineering of nanoparticles (NPs) with optimized properties
produced by continuous synthesis. A major challenge is the property classification of fine particles
with respect to size or shape. These challenges will be addressed from different perspectives andwill be underpinned by developments in synthesis, classification, characterization as well as
modelling and simulation.

CVWolfgang Peukert is a chemical engineer who worked 7 years in industry, two of which he spentin Japan with Hosokawa Micron. He is professor at the university Erlangen and director of theinterdisciplinary center of Functional Particle Systems. His research interests focus on productdesign and formulation. He is seeking for unifying principles in the design of particulate products
in combination with modelling and optimization strategies. His activities include particle
formation via bottom-up and top down techniques both in the gas and liquid phase. He uses
methods from interface science for tailoring particle interactions, colloidal stabilization andultimately particle properties. Recent activities include comprehensive particle characterization
for size, shape and surface properties. He likes to work in interdisciplinary teams, his motto is:
Innovation occurs at the interfaces.

Unifying principles of product design
Product design is the formation, formulation, handling, manufacturing, and characterization ofcomplex multiphase products with specific properties. The applications define the required productproperties which cover both classical fields of process technology in the chemical industry as well as
new emerging fields of electronics, energy and environmental technologies, life sciences, materials
science and engineering, nanotechnology, and photonic technologies highlighting the broad relevanceof mesoscale science. The following five unifying principles of particulate product design are proposed
which are widely applicable to many different kinds of products including solid, liquid, and evengaseous particles [1]: i) Particle formation by top-down and bottom-up approaches, ii) interactions
between the building blocks, iii) structure formation by self-organization and by transport processes,iv) characterization along the process chain, v) multiscale modelling and simulation. A particularimportant aspect in the hierarchical design of advanced materials made from (nano-)particles is
the multidimensional characterization of particle systems [2] including particle surfaces andparticle interactions which are highly relevant for colloidal stabilization, self-assembly andformulation. In particular, solid-liquid interfaces of particles are poorly understood. We present
examples for multidimensional particle characterization based on linear and nonlinear optical
spectroscopy, analytical ultracentrifugation and related SAXS/SANS studies. Other important
challenges are related to continuous formation of nanoparticles and their size classification which
will be briefly addressed in the following sections.

Nanoparticle Precipitation
Precipitation of nanoparticles is used in various fields with a rising interest in the continuous
formation and formulation of poorly soluble drugs. We analyze the impact of fluid mixing on theprecipitation of organic nanoparticles in a T-Mixer. Direct numerical simulations are applied todetermine the spatio-temporal evolution of the liquid phase composition and to estimate theparticle evolution along Lagrangian trajectories. The simulation results are compared withexperiments of mixing and particle size evolution, which use a recently developed approach to
rapidly stabilize the precipitated nanoparticles. We reveal the impact of mixing on precipitation,
thereby enabling a parameter-free estimation of the mean particle sizes and the particle size
distributions. The distributions of residence time, supersaturation time and particle size are selfsimilar for Reynolds numbers in the turbulent regime and allow the derivation of scale-up rules[3].

Nanoparticle chromatogaphy
For high-quality particulate products, a defined and narrow particle size distribution (PSD) isdesired, since a small change in PSD can have a large impact on their properties, for instance theinteraction of nanoparticles (NPs) with light is strongly size- and shape-dependent. Most of thetechniques to produce NPs lead to size distributions and despite the remarkable progress in liquid
phase syntheses of well-defined NPs of controlled size and shape. Therefore, a classification stepis mandatory to adjust or modify the obtained PSD according to the needs of the later product.
Specific targets for the classification are: Separation of a broader feed into two or more fractions,the removal of fine or coarse tail fractions, shape separation as many NP syntheses lead to shape
distributions, or the separation of by-products when several phases are formed. For instance,
narrowing of the initial PSD or exclusion of an unwanted particle fraction might alreadysignificantly improve the NP performance of the later application.
Currently, no technical solution for nanoparticle property classification is known. We propose
chromatography as promising and scalable method for classification of NPs. We study theclassification of different NPs in the size range from 1 nm up to 50 nm by separating C 60 fromC 70 fullerenes roughly 1 nm in diameter [4], carbon nanodots of similar size as highly lumincent
NPs [5], high precision classification of semiconducting ZnS quantum dots with sizes between
1.5 and 3 nm and plasmonic gold NPs below 50 nm [6], see Figure 1.

Figure 1. Examples of realized size-selective separations as function of retention time for (a)C 60 /C 70 fullerenes, (b) semiconducting ZnS quantum dots, and c.) plasmonic Au NPs.
C60 fullerenes are perfectly round, chemically well-defined and of uniform size of around 1 nm.
Thus, they are perfect probe molecules to study particle interactions. We show how to measure
Hamaker constants via adsorption equilibria obtained during their chromatographic separation.
ZnS and Au NPs are classified by size-exclusion chromatography (SEC). First, we investigated
the interactions of the NPs with potential stationary phases in order to identify a suitable material

for the chromatographic process where irreversible NP adhesion is excluded. Then, wedemonstrate the high reproducibility of our SEC experiments by multiple sample injections thatlead to constant peak areas. In particular, we show the size-dependent elution behavior of NP
mixtures resulting in baseline-separated elution peaks, where size separation was confirmed byinline measured UV/Vis spectra. Finally, NP classification results by using a fraction collector
are characterized by retention time, mass balances and size-dependent separation efficiencies. The
fine-tuning of the PSDs is demonstrated by changing the switching time of the fraction collector.
Our study provides fundamentals of preparative chromatographic separation of NPs. We
demonstrate the high effectivity and reproducibility of size-selected separation for varying
particle types, particle concentrations and particle sizes < 50 nm. The huge potential of NP
chromatography is demonstrated together with the strength of theoretical concepts for thequantitative analysis and the prediction of nanoparticle classification.

References[1]

Peukert W., Segets D., Pflug L., Leugering G., Unified design strategies for particulate products.
In Jinghai Li, Guy B. Marin (eds.) Mesoscale Modeling in Chemical Engineering Part I, Advancesin Chemical Engineering 46, pages 1-81, Academic Press 2015.

[2]

Wawra S., Thajudeen T., Pflug L., Kryschi C., Stingl M., Peukert W., Determination of the twodimensional distributions of gold nanorods by multiwavelength analytical ultracentrifugation,
Nature Communications 9 (2018) 4898

[3]

Schikarski T., Trzenschiok H., Avila M., Peukert W., The influence of mixing on the precipitationof organic nanoparticles: A Lagrangian perspective on scale-up based on self-similar distributions,Chem. Eng. Techn. 42 (2019) 8, 1635-1642[4]

Süß S., Michaud V., Amsharov K., Akhmetov V., Kaspereit M., Damm C., Peukert W., Quantitativeevaluation of fullerene separation by liquid chromatography, J. Phys. Chem. C 123 (2019) 1674716756[5]

Hinterberger, V., Damm C., Guldi D., Peukert W., Purification and structural elucidation of carbon
dots by column chromatography, Nanoscale 11 (2019) 8464-8474[6]

Süß S., Metzger C., Damm C., Segets D., Peukert W., Quantitative evaluation of nanoparticle
classification by size-exclusion chromatography, Powder Technology 339 (2018) 264-272