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In this study, we explore the integration of carbon quantum dots (QDs) in cesium lead halide perovskite solar cells (PSCs) across the electron transport layer (ETL), hole transport layer (HTL), and the perovskite absorber to enhance power conversion efficiency (PCE). We conduct a comprehensive investigation from thin film analysis to complete device characterization, encompassing eight different device topologies. Our results reveal that the integration of QDs in various layers significantly impacts the performance of the PSCs. Notably, adding QDs in the HTL and ETL improves charge transport and reduces recombination, enhancing device efficiency. Furthermore, introducing QDs in the perovskite layer leads to modifications in the energy landscape, reducing charge trapping and enhancing stability. We observe a trade-off between short-circuit current and overall PCE, with different QD integration strategies yielding distinct performance outcomes. Additionally, incorporating QDs in the ETL layer reduces hysteresis, attributed to mitigated ion migration and charge-trapping effects. Overall, the addition of QDs in these layers demonstrates improved charge transport, reduced recombination, and enhanced stability, ultimately contributing to the enhanced performance and efficiency of perovskite solar cells, reaching 22.5%. This study paves the way for future investigations into the potential of QDs in PSC technology and their impact on device forecasting and operational stability. [ABSTRACT FROM AUTHOR]

Organic field effect transistors (OFETs), used in the fabrication of nano‐sensors, are one of the most promising devices in organic electronics because of their lightweight, flexible, and low fabrication cost. However, the numerical modeling of such OFETs is still in an early stage due to the minimal analytical as well as numerical models presented in the literature. This research aims to demonstrate an experimentally verified machine‐learning model by investigating an OFET with polyaniline as a p‐type organic semiconductor. OFET's threshold voltage, on/off current ratio, subthreshold swing, and device mobilities are studied as the primary output chiasmatic parameters. The random‐forest machine learning model has shown the criticality of the doping effect on turning the OFET to depletion mode, with positive threshold voltage, under doping higher than 5×1014$$ 5\times {10}^{14} $$ cm−3. Additionally, the study highlights the effectiveness of the gate oxide thickness in controlling the OFET threshold voltage. A 50 nm oxide thickness showed sufficiency to have a non‐depleted OFET operation. [ABSTRACT FROM AUTHOR]

The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively. [ABSTRACT FROM AUTHOR]