Polymer passivation layers can improve the open-circuit voltage of perovskite solar cells when inserted at the perovskite–charge transport layer interfaces. Unfortunately, many such layers are poor conductors, leading to a trade-off between passivation quality (voltage) and series resistance (fill factor, FF). Here, we introduce a nanopatterned electron transport layer that overcomes this trade-off by modifying the spatial distribution of the passivation layer to form nanoscale localized charge transport pathways through an otherwise passivated interface, thereby providing both effective passivation and excellent charge extraction. By combining the nanopatterned electron transport layer with a dopant-free hole transport layer, we achieved a certified power conversion efficiency of 21.6% for a 1-square-centimeter cell with FF of 0.839, and demonstrate an encapsulated cell that retains ~91.7% of its initial efficiency after 1000 hours of damp heat exposure.

Defect‐mediated carrier recombination at the interfaces between perovskite and neighboring charge transport layers limits the efficiency of most state‐of‐the‐art perovskite solar cells. Passivation of interfacial defects is thus essential for attaining cell efficiencies close to the theoretical limit. In this work, a novel double‐sided passivation of 3D perovskite films is demonstrated with thin surface layers of bulky organic cation–based halide compound forming 2D layered perovskite. Highly efficient (22.77%) mixed‐dimensional perovskite devices with a remarkable open‐circuit voltage of 1.2 V are reported for a perovskite film having an optical bandgap of ≈1.6 eV. Using a combination of experimental and numerical analyses, it is shown that the double‐sided surface layers provide effective defect passivation at both the electron and hole transport layer interfaces, suppressing surface recombination on both sides of the active layer. Despite the semi‐insulating nature of the passivation layers, an increase in the fill factor of optimized cells is observed. The efficient carrier extraction is explained by incomplete surface coverage of the 2D perovskite layer, allowing charge transport through localized unpassivated regions, similar to tunnel‐oxide passivation layers used in silicon photovoltaics. Optimization of the defect passivation properties of these films has the potential to further increase cell efficiencies.

Organic solar cells possess multiple desirable traits, such as low cost, flexibility, and semitransparency, which opens up potential avenues unavailable to other solar technologies, a prime example of this being window applications. For this specific application, a delicate balance between the transmission of light through the device and power conversion efficiency (PCE), dependent on the amount of light absorbed, must be optimized. Here, we report a high-efficiency semitransparent device based on a novel fullerene-free material system. Using an active layer based on the material system PBDB-T:ITIC, optimized devices exhibited PCEs exceeding 7% while also achieving an average visible transmittance (AVT) of 25%. The concurrent demonstration of high efficiency with an AVT of 25% represents a notable step forward for semitransparent organic solar cells. Additionally, the influence of the active layer thickness on the color rendering properties of these cells was studied. Optimization of the active layer thickness can lead to high-efficiency cells, with high visible transmission as well as the ability to display an image accurately.

Heart disease, one of the main reasons behind the high mortality rate around the world, requires a sophisticated and expensive diagnosis process. In the recent past, much literature has demonstrated machine learning approaches as an opportunity to efficiently diagnose heart disease patients. However, challenges associated with datasets such as missing data, inconsistent data, and mixed data (containing inconsistent missing data both as numerical and categorical) are often obstacles in medical diagnosis. This inconsistency led to a higher probability of misprediction and a misled result. Data preprocessing steps like feature reduction, data conversion, and data scaling are employed to form a standard dataset—such measures play a crucial role in reducing inaccuracy in final prediction. This paper aims to evaluate eleven machine learning (ML) algorithms—Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Naive Bayes (NB), Support Vector Machine (SVM), XGBoost (XGB), Random Forest Classifier (RF), Gradient Boost (GB), AdaBoost (AB), Extra Tree Classifier (ET)—and six different data scaling methods—Normalization (NR), Standscale (SS), MinMax (MM), MaxAbs (MA), Robust Scaler (RS), and Quantile Transformer (QT) on a dataset comprising of information of patients with heart disease. The result shows that CART, along with RS or QT, outperforms all other ML algorithms with 100% accuracy, 100% precision, 99% recall, and 100% F1 score. The study outcomes demonstrate that the model’s performance varies depending on the data scaling method.

Perovskite solar cells are a potential game changer for the photovoltaics industry, courtesy of their facile fabrication and high efficiency. Despite this, commercialization is being held back by poor stability. To become economically feasible for commercial production, perovskite solar cells must meet or exceed industry standards for operational lifetime and reliability. In this regard, mixed dimensional 2D‐3D perovskite solar cells, incorporating long carbon‐chain organic spacer cations, have shown promising results, with enhancement in both device efficiency and stability. Dimensional engineering of perovskite films requires a delicate balance of 2D and 3D perovskite composition to take advantage of the specific properties of each material phase. This review summarizes and assesses the current understanding, and apparent contradictions in the state‐of‐the‐art mixed dimensional perovskite solar cell literature regarding the origin of stability and performance enhancement. By combining and comparing results from experimental and theoretical studies it is focused on how the perovskite composition, film formation methods, additive and solvent engineering influence efficiency and stability, and identify future research directions to further improve both key performance metrics.