Crystallization is one of the most widely used purification and separation processes applied in a multitude of industries such as pharmaceuticals, food & beverages, agriculture, and fine chemicals. However, the initial step of the crystallization process, nucleation, is still poorly understood and highly stochastic. As a result, most crystallization processes lack proper control over the properties of the crystals produced. Among many techniques for achieving better control over the nucleation process, the application of non-photochemical laser induced nucleation (NPLIN) has gathered significant interest. This is because of its potential to improve product quality in crystallization processes by directly controlling the nucleation rate, both spatially and temporally. Additionally, NPLIN can induce crystallization in solutions that would otherwise take a long time to nucleate, offering a unique advantage over traditional methods. However, despite its promising capabilities, NPLIN is not widely used in practice yet. The fundamental mechanism behind NPLIN is not fully understood, making it unclear how it should be applied effectively in practice and for which systems NPLIN could be beneficial.
This Ph.D. project aims to delve into the fundamental mechanisms of NPLIN, by examining how specific laser and solution parameters influence nucleation kinetics, leveraging innovative experimental setups. Laser parameters being studied include laser-exposed volume, laser irradiation position, laser intensity, and laser wavelength, and solution parameters include supersaturation levels, solution filtration, and the presence of impurities or dopants, particularly nanoparticles.
The thesis begins with a comprehensive review of the experimental and computational literature on NPLIN. It then presents a detailed study on the effect of the laser-exposed volume and laser irradiation position on the nucleation probability within partly illuminated supersaturated aqueous potassium chloride solutions. An increase in the laser-exposed volume resulted in a higher nucleation probability and a higher number of crystals per nucleated sample. Furthermore, laser irradiation, particularly through the air/solution interface, not only enhances nucleation probability but also influences the formation of different crystal morphologies. These observations are partly explained by the Nanoparticle Heating mechanism and the Dielectric Polarization model (Chapter 2).
The research then transitions to a microfluidic platform, which allows for high-throughput and crystallization detection using the deep learning method. This innovative approach addresses the need for large data sets in NPLIN research, which has been a significant challenge due to the manual nature of traditional experiments. The study examines the effects of laser intensity, wavelength, supersaturation, solution filtration, and intentional doping on nucleation probability in supersaturated potassium chloride solutions. Higher laser intensities and increased supersaturation significantly enhance nucleation probabilities. The laser wavelength effect was only observed for 355 nm at higher laser intensities. Solution filtration suppresses the NPLIN effect, whereas the addition of nanoparticles as dopants into the solution not only increases the NPLIN probabilities but also affects the crystal morphology. The results highlight the importance of impurities in the solution and support the hypothesis that nanoparticle or impurity heating could be the key mechanism in understanding NPLIN (Chapter 3).
The study finally investigated the effects of solution filtration, laser intensity, and nanoparticle properties including nanoparticle concentration and material on NPLIN probability in supersaturated aqueous urea solutions. The study highlights the significant role of impurities in NPLIN, demonstrating that doping with different nanoparticle materials leads to varied nucleation probabilities. In particular, gold nanoparticles were found to enhance nucleation more effectively than silica nanoparticles. Additionally, it was observed that NPLIN probabilities followed a Poisson distribution to changes in nanoparticle concentration and laser intensity respectively. The findings in this chapter enhance our understanding of the critical role of impurities in comprehending the NPLIN mechanism (Chapter 4).
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