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The determination of band gaps in unidentified materials has substantial importance for photovoltaic applications. In this research we used machine learning techniques to predict the band gap of perovskite materials within an acceptable margin of error. We developed a model to predict the band gaps of inorganic perovskites using machine learning algorithms. Our methodology utilizes a comprehensive dataset of 3720 ABX3-type perovskites and 2660 A2B(I)B(II)X6-type double perovskites, encompassing key properties such as band gap and formation energy. The features include nearly 300 descriptors generated by Matminer python. We applied six machine learning models, including XGBoost. The most effective model, XGBoost, demonstrated a notable R2 coefficient of 0.873 and a root mean square error (RMSE) of 0.5868 eV. Lastly, we conducted SHAP (SHapley Additive exPlanation) analysis to identify the most influential descriptors. The findings indicate that a higher formation energy, a significant proportion of transition metals, and a large number of d orbital valence electrons contribute to the formation of narrow bandgap perovskites. Conversely, a substantial number of f-orbital electrons and electronegativity differences between elements tend to result in wide-bandgap perovskites. This comprehensive analysis not only offers insights into the fundamental factors influencing the band gap of perovskite materials but also underscores the potential of machine learning in expediting materials research. [ABSTRACT FROM AUTHOR]

Carbon electrode‐based perovskite solar cells (C‐PSCs) without hole transport layer (HTL) have been emerging as a promising low‐cost photovoltaic technology with excellent stability for commercialization. However, the loose physical contact between the carbon electrode and perovskite layer, as well as the relatively poor conductivity of the carbon film, contributes mainly to the large gap in the power conversion efficiency (PCE) between C‐PSCs and the metal (Ag, Au, etc.,) electrode‐based counterparts. To this end, a simple but effective mechanical compression strategy for efficient C‐PSCs is developed. The mechanical compression densifies the porous carbon electrode for high film conductivity and also provides intimate contact between carbon and perovskite layers for fast charge extraction. Consequently, the resulting HTL‐free C‐PSCs using MAPbI3 (MA = methylammonium) absorber yield a PCE of 15.29%, corresponding to a 27.6% improvement compared to the counterpart without mechanical pressing treatment. Moreover, the compacted carbon film also serves as an enhanced barrier against the intrusion of water and oxygen, and the unencapsulated device retains 88.9% of its initial PCE after 1000 h of aging in ambient conditions with 35 ± 2% humidity. This work paves a simple and effective way toward efficient and stable C‐PSC. [ABSTRACT FROM AUTHOR]

A multi-metal Ti/Zr/V based conversion coating was employed to improve the adhesive strength of the epoxy coating on aluminum alloy 6063 (AA6063). The drying temperature of Ti/Zr/V conversion coating (TZVCC) was studied as a factor that could influence the adhesion performance of the epoxy coating on the substrate. The surface morphologies, cross-section micrograph and chemical compositions of different samples were fully characterized by scanning electron microscope (SEM), transmission electron microscopy (TEM) and energy disperse spectroscopy (EDS), respectively. The tape measurement and pull-off tests were used to investigate the adhesive strength of samples under three conditions (dry, wet and recovery). The TZVCC after conversion treatment for 50 s had a golden appearance and exhibited a smooth and compact structure. Drying the TZVCC at high temperature could reduce the sizes of the holes disposed on the TZVCC/epoxy coating interface, as well as improve the adhesive strength. The highest pull-off strength was obtained at drying temperatures of 80°C, 100°C and 100°C for dry, wet and recovery conditions. TEM results revealed that the epoxy resin powder would permeate into TZVCC during the curing process, which significantly improved the adhesive strength of the epoxy coating on the matrix. [ABSTRACT FROM AUTHOR]

As one of the main components of ionic liquid-based composite electrolytes, nanoparticles play a major role in determining the performance of such electrolytes. In this study, four different TiO2-based nanoparticles are introduced into ionic liquid electrolytes to systematically investigate the influence of their conductivities and surface chemistries on the performance of composite electrolytes. Compared with conventional insulating nanoparticles, high-conductivity nanoparticles are free of blocking junctions, and the surface region of the nanoparticles can act as an additional pathway for ionic conduction. Meanwhile, the surface chemistry of the nanoparticle strongly determines the viscosity, network stability, and microscopic morphology of an ionic liquid-based composite electrolyte, thus affecting the ionic conductivity. Therefore, the highest conductivity of 4.29 mS cm–1 is achieved by the ionic liquid-based composite electrolyte with the SC-HC-TiO2 nanoparticle at a weight of 10 wt %, which has high conductivity and rich amino functional groups. DSCs using SC-HC-TiO2 show a high efficiency of 8.12% due to the simultaneous improvement of photocurrent density (Jsc), open-circuit voltage (Voc), and fill factor (FF). [ABSTRACT FROM AUTHOR]