Perovskite solar cells (PSCs) have achieved remarkable power conversion efficiencies (PCEs) exceeding 25.7% in lab-scale devices, rivaling traditional silicon-based photovoltaics. However, their operational stability remains a critical challenge, with degradation rates often exceeding 10% within 1,000 hours under standard illumination. Recent advances in interfacial engineering, such as the use of 2D/3D heterostructures, have extended device lifetimes to over 10,000 hours while maintaining PCEs above 22%. These structures mitigate ion migration and phase segregation, key degradation mechanisms in perovskites.
Encapsulation techniques have also evolved to address environmental stressors like moisture and oxygen. Atomic layer deposition (ALD) of Al2O3 layers has reduced water vapor transmission rates to below 10^-6 g/m²/day, significantly enhancing device durability. Additionally, hydrophobic polymer coatings have been shown to improve humidity tolerance by up to 85% in accelerated aging tests at 85°C and 85% relative humidity. These innovations are critical for commercial viability.
The integration of machine learning (ML) in PSC research has accelerated material discovery and optimization. ML algorithms trained on datasets of over 10,000 perovskite compositions have identified novel additives like formamidinium thiocyanate (FASCN), which stabilize the perovskite lattice and boost PCEs by up to 1.5%. This data-driven approach reduces experimental trial-and-error time by over 70%, enabling rapid progress in the field.
Scalability remains a hurdle for PSCs, with large-area modules (>100 cm²) often exhibiting PCEs below 20%. Advanced deposition techniques like slot-die coating and vapor-assisted crystallization have improved uniformity and reduced defect densities to below 10^15 cm^-3. These methods are paving the way for industrial-scale production with projected costs as low as $0.10/Watt by 2030.
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