Passivation Engineering Using Ultrahydrophobic Donor–π–Acceptor Organic Dye with Machine Learning Insights for Efficient and Stable Perovskite Solar Cells

Abstract

To prevent the degradation of perovskite solar cells (PSCs) and optimize the solar energy conversion process, a donor–π–acceptor (D–π–A) organic blue dye as a passivation layer and as a hole‐transporting layer is introduced. The terminal chains of D–π–A dye confer the ultrahydrophobic character (contact angle > 100°) of the interface layer, protecting the perovskite from ambient moisture while mitigating ionic diffusion in the device. The dye interlayer primarily improves the perovskite by reducing grain boundary defects. The perovskite/D–π–A architecture enhances the interfacial hole extraction, suppressing nonradiative carrier recombination and enabling power conversion efficiency (PCE) reaching 20.90%, outperforming by 2.05% the PCE of control cells. Unsealed PSCs retain 84% and 62% of their efficiency after photovoltaic operation for 1000 and 3000 h, respectively. Statistical correlation of bivariant and multivariant analyses of photovoltaic parameters is performed and Pearson's correlation identifies underlying patterns in experimental data collections. Machine learning (ML) of regression algorithms is used to predict the minimum errors and the coefficient of determination, which confirm the analysis quality. The linear regression ML model suggests the importance of photovoltaic parameters (Rs > Vmpp > Jsc > Voc > fill factor > Jmpp > Rsh) toward higher PCE. An efficient online prediction model is also developed to support the estimation of PCEs with high accuracy.

3-Thiophenemalonic Acid Additive Enhanced Performance in Perovskite Solar Cells

“…To improve both the PCE and stability of the OIHPSCs, researchers are extensively exploring several approaches to reduce high trap density states in the OIHP polycrystalline thin films. In this regard, compositional engineering, incorporation of additives, interface, and surface passivation of the light absorber layer, careful selection of charge transport layers, solvent engineering, and use of proper electrode layers have been investigated extensively to minimize the barrier toward commercialization of OIHPSCs. − Structural engineering of a three-dimensional/two-dimensional (3D/2D) mixed perovskite structure has also been investigated to get stable and reproducible OIHPSCs. , In addition to this, the role of deposition techniques on whole performance has also been studied as one of the mechanisms to fabricate stable organic–inorganic halide perovskite (OIHP) devices …”

A universal platform of molecular orbital energy level prediction and molecular design for organic materials

“…'s team, such as Random Forest Regression, Ensemble Neural Networks, and Genetic Algorithms, can predict the optimal level of HOMO for Organic Photovoltaics (OPVs), increasing their e ciency to 10.2% [36]. Mohamed M. and other used an organic blue dye to enhance the stability and e ciency of perovskite solar cells, and applied machine learning with regression algorithms for correlation analysis and prediction models of photovoltaic parameters [37].…”

Insight into Controlled Surface Passivation of PEDOT:PSS for Defect Density Modulation and Efficient Charge Transport for Perovskite Solar Cells

“…Different techniques or compounds have been used to passivate the defects in the surface as well as the bulk along with grain boundaries. TAS is used to understand the type of defects and changes in defect states due to the passivation effect. − To get a clear insight into the hole extraction process at the interface, TAS can be used to probe the perovskite/HTL interface, which allows probing of the excited-state dynamics on different time scales. It is proven that surface passivation is more effective than bulk passivation in improving the PSC performance.…”