Design and Characterization of Nanostructured Materials for High-Efficiency Photovoltaic and Optoelectronic Applications Synthesis Strategies, Structure-Property Relationships, and Device Integration

Abstract

Nanostructuring has become one of the most powerful design levers available for improving the efficiency, stability, and functionality of photovoltaic (PV) and optoelectronic devices. By confining charge carriers to length scales comparable to or smaller than their de Broglie wavelength, and by sculpting material morphology at the sub-wavelength scale, nanostructured absorbers, transport layers, and light-management architectures can address several loss mechanisms simultaneously: parasitic reflection, insufficient absorption in thin films, non-radiative recombination at interfaces, and poor spectral matching to the solar spectrum. This paper reviews the principal classes of nanostructured materials used in modern photovoltaics and optoelectronics  zero-dimensional quantum dots, one-dimensional nanowires and nanorods, two-dimensional nanosheets, and three-dimensional nanotextured and plasmonic architectures  together with the synthesis routes, structural and optical characterization methods, and device integration strategies associated with each. Particular attention is given to halide perovskite quantum dots and nanocrystals, silicon and III–V nanowire arrays for light trapping and tandem integration, and plasmonic nanostructures for near-field and far-field light management. Representative case studies are discussed, including ligand- and interface-engineered perovskite quantum-dot solar cells achieving certified efficiencies above 24%, embedded silicon-nanowire interlayers proposed for perovskite/silicon tandem cells, and nanocone and nanopillar antireflection textures for ultrathin crystalline-silicon absorbers. The review concludes with a discussion of persistent challenges in stability, scalable and reproducible synthesis, and toxicity, and outlines future directions including lead-free nanocrystal absorbers, machine-learning-guided synthesis optimization, and integration with tandem and multi-junction architectures.