Organic–inorganic lead halide perovskites have recently emerged as highly competitive light absorbing materials for low cost solution-processable photovoltaic devices. With the high efficiency already achieved, removing the toxicity, i.e., lead-free and stability are the key obstacles for perovskite solar cells. Here, we report the synthesis of an antimony (Sb)-based hybrid material having the composition of A₃Sb₂I₉ [A = CH₃NH₃ (MA), Cs] and an investigation of its potential photovoltaic applications. Sb-based perovskite-like materials exhibited attractive absorbance properties, with the band gaps of MA₃Sb₂I₉ and Cs₃Sb₂I₉ measured to be 1.95 and 2.0 eV, respectively. X-ray photoelectron spectroscopy confirmed the formation of stoichiometric perovskites from appropriate precursor molar ratios incorporated with hydroiodic acid (HI). Planar hybrid Sb-based solar cells exhibited negligible hysteresis and reproducible power output under working conditions. A power conversion efficiency of 2.04% was achieved by the MA₃Sb₂I₉ perovskite-based device—the highest reported to date for a Sb-based perovskite solar cell.

Predicting the soluble solid content (SSC) of peaches based on visible/near infrared spectroscopy has attracted widespread attention. Due to the anisotropic structure of peach fruit, spectra collected from different orientations and regions of peach fruit will bring variations in the performance of SSC prediction models. In this study, the effects of spectra collection orientations and regions on online SSC prediction models for peaches were investigated. Full transmittance spectra were collected in two orientations: stem-calyx axis vertical (Orientation1) and stem-calyx axis horizontal (Orientation2). A partial least squares (PLS) method was used to evaluate the spectra collected in the two orientations. Then, each peach fruit was divided into three parts. PLS was used to evaluate the corresponding spectra of combinations of these three parts. Finally, effective wavelengths were selected using the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Both orientations were ideal for spectra acquisition. Regions without peach pit were ideal for modeling, and the effective wavelengths selected by the SPA led to better performance. The correlation coefficient and root mean square error of validation of the optimal models were 0.90 and 0.65%, respectively, indicating that the optimal model has potential for online prediction of peach SSC.