Semiconductor Nanoparticles: From Synthetic Design to Structure Elucidation

As semiconductor nanomaterials continue to gain interest across various fields, the development of synthetic methodologies and engineering their unique properties have become significant. In this thesis, organometallic compounds were utilized as reagents and as nucleating catalysts to not only tailor nanomaterial characteristics but also develop synthetic designs. Cu-doped Cs4PbCl6 nanowires were synthesized using (NMe4)2[Cu4(SPh)6] as a dopant source in a batch process. Cluster-seeded CdSe quantum dots were synthesized in a microfluidic reactor to explore the potential integration of artificial intelligence (AI). One issue with cluster seeding is the need to optimize reaction conditions, which can be facilitated by AI. Training the AI algorithm needs a microfluidic reactor system that enables reproducibility and swift adjustment of reaction conditions. Incorporation of the cluster seed method into a microfluidic system enables simplified fluidic design for quantum dot synthesis due to the low nucleation temperature using organometallic cluster nucleation catalysts. The thesis also demonstrates an analysis technique known as the Warren-Averbach method to characterize nanomaterial sizes using a powder X-ray diffraction. This approach is comparable to TEM in the ability to elucidate the structure of nanomaterials at the whole specimen level. The Warren-Averbach method was applied to study various shapes of lead chalcogenide nanomaterials. Spherical and cubic shapes were differentiated from anisotropic morphologies, and nanowires created via oriented attachment were distinguished from those prepared through the solution-liquid solid mechanism. All the experimental results were compared with simulated diffraction data generated from modeled structures.