Machine Learning-Assisted Fabrication of Polyelemental CNP@MOF Core@Shell Nanostructures for Asymmetric Catalysis
Asymmetric synthesis of single-enantiomer chemicals plays a vital role in producing single-enantiomer drugs in pharmaceutical industry. This project will produce the polyelemental CNP@MOF core@shell nanoparticles as the asymmetric catalysts to trigger the asymmetric synthesis, that is, the heterogeneous asymmetric catalysis. GLAD will be used to fabricate polyelemental CNPs, which will serve as the seeds to guide the growth of MOF to form the polyelemental CNP@MOF nanoparticles. Precursor molecular substrates will be selectively grafted on the core polyelemental CNPs via the diffusion through MOF, followed by the asymmetric catalysis. The shell MOF will function as the molecular filter to enhance the catalytical selectivity and co-catalysts. Chiral HPLC will be operated to analyse an enantiomeric excess (ee) of the enantioselectivity. ML-assisted design of the growth of the CNP@MOF nanoparticles will be operated to maximize the ee values. According to the ML-based design, the enantioselectivity will be experimental optimized.
Academic background in Chemistry, Material Science and/or Physics. Experimental experience in nanomaterial fabrication and characterization, as well as chemical synthesis and characterization.