Computational Modelling and Numerical Simulation of a Lead-free Perovskite Photovoltaic Solar Cell, Optimised for the Specific Climatological Profile of Zimbabwe

Abstract

This study entails the computational design and numerical simulation of a lead-free perovskite photovoltaic solar cell, optimised for the specific climatological profile of Zimbabwe. The investigation addresses the critical limitations of conventional perovskite solar cells, namely Pb-toxicity and thermo-environmental instability, by proposing a novel heterostructure NiO/CsSn₀.₅Ge₀.₅I₃/ZnO/FTO in the context of the Zimbabwean climatic conditions. The core of the design is the all-inorganic halide perovskite absorber layer, CsSn₀.₅Ge₀.₅I₃, where the isovalent cation substitution of Sn²⁺ and Ge²⁺ for Pb²⁺ constitutes an eco-friendly alternative. The device architecture employs a hole transport layer of NiO and an electron transport layer of ZnO, deposited on an FTO substrate, forming a conventional n-i-p configuration. Using the Solar Cell Capacitance Simulator software, the device's performance metrics were evaluated under a temperature gradient (8°C to 35°C) reflective of the operational environment. The results demonstrate exceptional thermal resilience. In addition, the analysis reveals a minor degradation in open-circuit voltage from 1.2376 V to 1.2045 V, attributable to increased intrinsic carrier concentration and enhanced Shockley-Read-Hall recombination at elevated temperatures. Notably, the short-circuit current density remained invariant at ~27.24 mA/cm², indicating stable photogeneration and charge collection efficiency. Consequently, the fill factor and power conversion efficiency exhibited only marginal declines from 90.39% to 89.52% and 30.48% to 29.38%, respectively. The simulation confirms the viability of the proposed multi-cation perovskite composition and device stack, achieving a stabilised PCE exceeding 29% across the specified thermal range. This work underscores the efficacy of computational modelling with SCAPS-1D for the a priori optimisation of PV device architectures, thereby de-risking the R&D pipeline by minimising empirical iteration. The findings contribute to the development of regionally tailored, high-performance, and sustainable third-generation photovoltaics.