Christian Schwaferts successfully defended his PhD Theasis with summa cum laude - Congratulations!
"Characterization and Identification of Micro- and Nanoplastic by Raman Microspectroscopy, Scanning Electron Microscopy, Field-Flow Fractionation and Chemometrics"
Abstract
Nanoplastic contamination is an emerging issue with the potential to negatively human health and the environment. For a reliable risk assessment, suitable analytical methods are necessary. However, established techniques from microplastic (MP) analysis face limitations, due to the small sizes and low masses of nanoparticles. On the other hand, current techniques for nanoparticle characterization do not allow for a chemical identification of the polymer, which is essential for a quantitative characterization. The presented thesis aims to solve this analytical challenge by developing new methods for Raman microspectroscopy (RM) in combination with scanning electron microscopy (SEM), field-flow fractionation (FFF), and chemometrics.
As an initial step, a critical review presents the state of the art in nanoplastic analysis, including a discussion of the methodological gap and the techniques that can potentially be adapted from nanoparticle analysis and MP analysis. This results in a roadmap laying out the requirements and possible techniques for each step of the nanoplastic analysis. It begins with the analytical question and sample treatment, discusses methods for particle separation, visualization, and physical characterization, and addresses the chemical identification of nanoplastic.
In a second step, the lower size limit of RM, combined with SEM, for the analysis of MP and nanoplastic was evaluated and demonstrated to be applicable down to the theoretical diffraction limit at around 0.25 µm. This was experimentally tested for spherical and irregular, fragmented particles, as they are expected in the environment. Following this qualitative assessment of lower particle size limits for MP/sub-MP analysis, quantification of particle number and size distribution on the Raman filter was approached. Here, a key challenge is to enable a statistically sound determination of the MP number and the MP/non-MP ratio, respectively. This was solved by delineating a particle-by-particle measurement algorithm based on window sampling, for which the bias and standard deviation of random and systematic window placement was investigated using simulated filters. Results show that random window sampling prevents the introduction of bias. It is, however, accompanied by an increased standard deviation. Furthermore, increasing the size of the windows also increases the bias or standard deviation for systematic and random windows, respectively. To obtain a confidence interval (CI) even though the total particle number is unknown, a bootstrap method is used. This approach enables an on-the-fly measurement algorithm where first, a smaller increment of particles/windows is measured and analyzed. Subsequently, the CI of the data up to this point is estimated by bootstrap, and it is assessed whether the error margin and error probability are below an acceptance criterion, in which case the measurement can be stopped, else it is continued with the next increment of particles. This on-the-fly procedure will enable that an optimal end-point is found such that no measurement time is wasted.
In a third step, RM was online-coupled to FFF by developing an optical tweezer-based flow-cell in order to facilitate automated nanoplastic analysis, which does not rely on image analysis, but instead on particle separation followed by chemical characterization. The setup was validated for particles in the size range from 200 nm to 10 µm, concentrations in the order of 1 mg/L (109 particles L-1), and different material (polymers and inorganic). Using two variants of FFF (asymmetric flow field-flow fractionation & centrifugal field-flow fractionation), it was shown that the online-coupling can be implemented for multiple particle separation techniques. Thus, the optimal technique can be selected based on their respective advantages and disadvantages. In addition to the chemical identification by the novel RM flow-cell, physical characterization of the particles was performed by a UV and multi angle light scattering (MALS) detector, providing the particle size distribution. Thus, the foundation for a multi-detector system has been laid, which will enable a comprehensive nanoplastic analysis, as well as the application to a broader set of particulate samples.