Utilizing Machine Learning and Diode Physics to Investigate the Effects of Stoichiometry on Photovoltaic Performance in Sequentially Processed Perovskite Solar Cells.

Organic-inorganic metal halide perovskite solar cells are renowned for their extensive solution processability, although the production of uniformly crystalline perovskite films can necessitate intricate deposition methods. In our study, we harmonized Shockley diode-based numerical analysis with machine learning techniques to extract the device characteristics of perovskite solar cells and optimize their photovoltaic performance in light of the experimental variables. The application of the Shockley diode equation facilitated the extraction of photovoltaic parameters and the prediction of power conversion efficiencies, thus aiding the understanding of device physics and charge recombination. Through machine learning, specifically Gaussian process regression, we trained models on current-voltage curves sensitive to variations in fabrication conditions, thereby pinpointing the optimal settings for enhanced device performance. Our multifaceted approach not only clarifies the interplay between experimental conditions and device performance but also streamlines the optimization process, diminishing the need for exhaustive trial-and-error experiments. This methodology holds substantial promise for advancing the development and fine-tuning of next-generation perovskite solar cells.

Correction: Optical humidity sensors based on lead-free Cu-based perovskite nanomaterials.

[This corrects the article DOI: 10.1039/D2NA00168C.].

Optical humidity sensors based on lead-free Cu-based perovskite nanomaterials.

Organometallic halide perovskite materials possess unique and tunable optical properties with a wide range of optoelectronic applications. However, these materials suffer from humidity-driven degradation in ambient atmospheres. In this paper we investigate stable copper-based perovskite nanocrystals for potential use in humidity sensors, specifically examining their unique humidity-dependent optical properties and reversibility. We controlled stoichiometric ratios of Cu-based perovskites and demonstrated that (methylammonium)2CuBr4 nanocrystals showed excellent reversible physisorption of water molecules. These perovskite nanocrystals exhibited reversible hydro-optical properties, including transparency changes in response to variations in relative humidity under ambient conditions. The perovskite nanomaterial humidity sensor was highly reliable and stable, with a linear correlation in a relative humidity range of 7% to 98%. Accordingly, the lead-free Cu-based perovskite materials developed herein have the potential to be employed as real-time, self-consistent humidity sensors.