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Assessing field-flow fractionation and light scattering for the characterization of extracellular vesicles and polymer colloids
Plavchak, Christine Lynn
Plavchak, Christine Lynn
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2023
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2024-11-29
Abstract
Nanoparticle characterization is centered around understanding how properties such as size and composition as well as count correlate with synthetic methodologies, observed behaviors, and end product performances. Current ensemble methods that examine these properties (light scattering, electron microscopy, nanoparticle tracking analysis (NTA), zeta potential, etc.) provide average values and cannot provide important information regarding distributions within the sample. These techniques are also compromised by sample polydispersity and may not be sensitive enough to examine particles that span the range of 1 nm to 1 µm in diameter. To overcome this, samples can be separated to create more monodisperse subpopulations, yet only a few ensemble methods have been readily coupled to separation techniques like field-flow fractionation (FFF).
FFF is a family of analytical techniques that has been used to separate and characterize macromolecules and particles since the mid-1960s. Improvements to FFF instrumentation and theory along with the coupling to multiple detectors such as light scattering, differential refractive index, spectrophotometry, and mass spectrometry has enhanced FFF’s capabilities for particle characterization. More recent advancements include nanoparticle tracking analysis (NTA) and on-line Raman spectroscopy for determining the and number/size of nanoparticles as well as composition of polymeric particles, respectively. Together, they represent critical challenges and frontiers for nanoparticle analysis. The work in this thesis takes a different approach by first critically assessing multiangle light scattering (MALS) as a particle counting technique and then exploring the sensitivity of thermal field-flow fractionation (ThFFF) for compositional analyses.
The former is particularly relevant as the FFF-MALS platform is now commonly used across disciplines and products. This, in combination with particle counting component of the European Union’s definition of a nanomaterial will undoubtedly lead to an increase in particle counting using MALS for particle counting. However, there has been no work published to date that critically assesses the impact of the uncertainty in nanoparticle refractive indices (a difficult to obtain value for core-shell type structures) and light scattering models used in data analysis to calculate the number of nanoparticles. This work seeks to address this gap in knowledge particularly for complex bioparticles such as outer membrane vesicles (OMVs). The thermal FFF work builds on previously published studies but differs in that the compositional sensitivity of this technique and the use of additives to improve retention and sample recovery are explored.
Asymmetrical flow FFF (AF4), coupled to multiangle light scattering (MALS), has recently gained attention for the characterization of bacterial OMVs for nanomedicine and renewable energy applications. A major analytical challenge of OMVs is understanding how particle size and count impact their biogenesis and the cargo sorting proteins in different-sized vesicles. AF4-MALS can be used as an initial separation and enumeration step prior to further analyses with techniques tandem mass spectrometry (i.e., proteomics). While MALS has been used to count biological particles and has shown similar counts to offline methods like NTA, the influence of analyte-dependent parameters (e.g., refractive index (RI) and particle shape/model) with MALS has not been examined. Polystyrene latex standards (known RI and shape) and complex OMVs (unknown RI and shape) were chosen as the model and sample systems. Particle counts from PSL differ upwards of 13 % between sphere or Lorenz-Mie models while OMV particle counts can vary up to 200% depending on the model and refractive index used. Additionally, signal-to-noise in the light scattering signal intensity can lead to erroneous particle counts (i.e., > 1018 particles/mL), which was observed when using the coated sphere model for OMVs. While a promising enumeration technique, the need of particle count standards and accurate RI values impede the determination of absolute particle counts.
Thermal FFF (ThFFF) is another technique under the FFF umbrella that can yield particle size as well as composition. The latter differentiates ThFFF from its better-known sibling, AF4. The driving force for ThFFF is imparted by a temperature gradient that is applied perpendicular to the separation axis. Analyte retention is dependent on the Soret coefficient (ST), a ratio of the analytes thermal diffusion coefficient (DT) to its translational diffusion coefficient (D). While ThFFF is mainly done in organic solvents, there is an interest in moving towards aqueous solvents (AqThFFF) in order to characterize aqueous based (biological or synthetic) materials. Two major challenges in performing AqThFFF experiments are the need to use additives (i.e., salts or surfactants) to improve analyte retention and understanding how these additives influence particle thermophoresis. Additionally, little work has been done to assess the compositional sensitivity of ThFFF. To investigate the impact of additives and compositional sensitivity, a model set of butylacrylate: methyl methacrylate: acrylic acid (BA:MMA:AA) particles with subtle differences in acidic comonomer (0-3 %) were examined. A key component of this work was examining the impact of commonly used additives such as tetrabutylammonium perchlorate (TBAP) and FL-70 detergent on analyte retention and recovery. TBAP governed retention of the colloids while the incorporation of FL-70 increased sample recovery. An incremental component of calculating DT values is utilizing accurate D values. Examining D values through DLS (online and offline), via AF4 theory, and transformations from radius of gyration (Rg) data show that flow rates as low as 0.3 mL/min during FFF separations can cause D values to be larger than anticipated. While these changes in D do not impact overall trends across latex samples, they change the overall magnitude of DT. AqThFFF can distinguish between 1 % of acidic comonomer between samples, based on significant differences in DT values and demonstrates a higher sensitivity than the ~9% previously reported in literature.
Overall, the work presented in this thesis provides insight into the importance of refractive index on particle counting analyses by MALS as well as subtle nuances and considerations in the data analysis for MALS particle counting. Additives that enhance retention and sample recovery in AqThFFF provide a useful foundation for future advancements and applications. This thesis serves as a platform for future work with biological particles and insight into particle thermophoresis.
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