Combining fluorescence microscopy and machine learning for a powerful method to analyse nematic domain growth in quantum dot nanocomposites
Year: 2025
Type: article
Abstract: Nanoparticle transport by a moving liquid crystal phase boundary was recently shown to facilitate the formation of nanoparticle-based porous micro-assemblies (capsules, foams, etc). A key challenge is that it can be very difficult to accurately image this process and thus to gain better insight into the mechanisms that control particle transport and assembly. The first stage in micro-assembly formation involves nematic domain nucleation and growth from an isotropic phase that includes dispersed nanoparticles. Here, we report a fluorescence method to quantify domain growth using luminescent nanoparticles (surface-modified quantum dots) to visualise domain growth following a controlled temperature quench. This technique provides enhanced resolution of domain edges when compared with birefringence imaging, especially for very small domains (<2 µm). We compare manual and automated measures of domain growth using ilastik, a machine-learning-enabled segmentation tool. This high-throughput method allows us to measure the growth rate of thousands of domains simultaneously with a high degree of confidence. From these measurements, we report time dependent nematic domain size distributions and growth exponents at two different quantum dot concentrations and cooling rates. The results match well with theoretical predictions and show that under our experimental conditions, the nanoparticles do not significantly impact domain growth.
Source: Figshare
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117377044