The research design involves materials synthesis and characterization, device fabrication, and computational modeling. Semiconductor nanocrystals, mxenes, and perovskites will be synthesized and characterized using UV-Vis, angle-resolved PL, time-resolved fluorescence, and FLIM techniques. LEDs and solar cells will be fabricated using PVD, spin coating, and thermal processing. Device characterization will include I-V measurements, external quantum yield, optical microscopy, and angle-resolved EL and reflectivity. 

Computational methods will employ AI algorithms for inverse design and optimization of materials, structures, and device architectures. Machine learning techniques will be utilized for materials discovery, property prediction, and device performance optimization. Experimental data analysis and visualization techniques will be employed. 

The methods involve chemical synthesis (colloidal, sol-gel, vapor deposition), optical and electrical characterization (spectroscopy, microscopy, I-V), thin-film deposition (thermal evaporator and sputtering, spin coating), thermal processing (annealing), and computational techniques (AI algorithms, machine learning, inverse design, optimization). This integrated approach combines experimental and computational methods, with a focus on AI-driven inverse design and optimization, to develop efficient renewable energy technologies based on emerging semiconductor materials.