Nano-Engineered Interfaces and Charge Transport Layers in Perovskite Solar Cells: Progress, Degradation Dynamics, and Future Scalability
DOI:
https://doi.org/10.65421/jibas.v2i3.122Keywords:
Perovskite solar cells, Nano-engineered interfaces, Charge transport layers, Interfacial degradation, Scalable manufacturingAbstract
Nano-engineered interfaces and charge transport layers (CTLs) have become fundamental to the advancement of perovskite solar cells (PSCs) due to their simultaneous control over carrier selectivity, interfacial recombination, and operational durability. This critical review systematically evaluates the efficiency-stability trade-off governed by nanostructured transport matrices, emphasizing how interfacial energy alignment, defect density, and mechanical mismatch collectively dictate device behavior under real-world operating conditions. Particular attention is devoted to coupled degradation cascades, including field-driven ion and defect migration along grain boundaries, trap-assisted non-radiative recombination pathways, chemical corrosion and volatilization at reactive boundaries, and thermomechanical modulus mismatches induced by diurnal thermal cycling. Furthermore, this review dissects recent milestones in nano-engineered electron and hole transport materials configured to mitigate these losses through optimized band alignment and enhanced interface selectivity. We highlight quantum-confined tin dioxide SnO₂ quantum dots (QDs) for their ideal conduction band minimum (CBM) matching with the perovskite layer, enabling downhill electron extraction while providing a robust hole-blocking barrier. Concurrently, homogenized nickel oxide NiOₓ nanoparticles, specifically m-NiOₓ, are evaluated as promising hole-selective contacts. The deeper valence band maximum (VBM) of m-NiOₓ (≈ –5.36 eV versus –5.22 eV in conventional c-NiOₓ) offers a superior energetic match with the perovskite valence edge, accelerating hole extraction and improving electron blocking. A forward-looking roadmap is outlined, focusing on Machine Learning (ML)-assisted screening of interfaces, standardized multi-stress stability tracking under ISOS protocols, and eco-friendly scalable manufacturing.

