Next-Generation Nanomaterials for Solar Cells: Physics, Fabrication, and Performance - A Mini Review
Keywords:
Perovskite solar cell, Quantum Dots, Stability, Photovoltaics, Plasmonic nanostructures, Tandem solar cellAbstract
Through enhanced light harvesting, charge transfer, and spectrum management, the quick development of nanostructured materials has significantly improved photovoltaic performance. With a focus on perovskites, perovskite quantum dots, colloidal quantum dots, plasmonic nanostructures, and emerging 2D materials, this mini-review critically synthesizes recent literature (2013–2025) on next-generation nanomaterials for solar cells. It also assesses the physical mechanisms, fabrication processes, device performance, and translational barriers of these materials. A thorough search of IEEE Xplore, Web of Science, and Scopus was conducted to find important modeling and experimental studies influencing modern device architectures. Through band-gap engineering and sophisticated light-management techniques, we discover that perovskite-based devices and perovskite/silicon tandem architectures have the highest lab-scale power-conversion efficiency (claims approaching ~30% for monolithic tandems). Practical deployment is nevertheless constrained by enduring issues, such as long-term operational stability under heat, moisture, and photo-stress; scaling up low-cost, environmentally friendly manufacturing; a lack of standardized stability testing (such as ISOS-compliant protocols); and a lack of mechanistic studies of degradation pathways.Comparing investigation reveals trade-offs between higher cost and processing complexity and efficiency improvements from complicated nano-additives (such as plasmonic/gold nanostructures). To expedite the transition from laboratory demonstrations to commercially viable photovoltaic technologies, we suggest the following priority actions: adoption of standardized stability metrics, life-cycle and cost analyses, development of lead-free/low-toxicity chemistries, scalable deposition methods, and AI-driven nanostructure optimization.

