Despite being the most commonly used hole transport layer for p-i-n perovskite solar cells, the conventional PEDOT:PSS layer is far from being optimal for the best photovoltaic performance. Herein, we demonstrate highly conductive thin DMSO-doped PEDOT:PSS layers which significantly enhance the light harvesting, charge extraction, and photocurrent production of organo-lead iodide devices. Both imaging and X-ray analysis reveal that the perovskite thin films grown on DMSO-doped PEDOT:PSS exhibit larger grains with increased crystallinity. Altogether, these improvements result in a 37% boost in the power conversion efficiency (PCE) compared to standard p-i-n photovoltaics with pristine PEDOT:PSS. Furthermore, we demonstrate that DMSO-doped PEDOT:PSS devices possess enhanced PCE durability over time which we attribute primarily to fill factor stability.

Bandgap engineering of lead halide perovskite materials is critical to achieve highly efficient and stable perovskite solar cells and color tunable stable perovskite light-emitting diodes. Herein, we propose the use of machine learning as a tool to predict the bandgap of the perovskite materials from their compositions. By learning from the experimental results, machine learning algorithms present reliable performance in predicting the bandgap of the lead halide perovskites. The linear regression model can be used to manually predict the bandgap of the perovskite with the formula of Cs_aFA_bMA_(1−a−b)Pb(Cl_xBr_yI_(1−x−y))₃ (FA = formamidinium, MA = methylammonium). The neural network (NN) algorithm, which takes the interplay of cations and halide ions into account in predicting the bandgap, presents higher accuracy (with a RMSE of 0.05 eV and a Pearson coefficient larger than 0.99). Furthermore, the compositions of the mixed halide perovskites with desirable bandgaps and high iodide ratio for suppressing halide segregation are predicted by NN algorithm. These results highlight the power of machine learning in predicting the bandgap of the perovskites from their compositions and provide bandgap tuning directions for experiments.

In this paper, Nd³⁺–Yb³⁺–Er³⁺-doped β-NaYF₄ nanocrystals with different Nd³⁺ concentrations are synthesized, and the luminescence properties of the upconversion nanoparticles (UCNPs) have been studied under 808-nm excitation for sensitive biological applications. The upconversion luminescence spectra of NaYF₄ nanoparticles with different dopants under 808-nm excitation proves that the Nd³⁺ ion can absorb the photons effectively, and the Yb³⁺ ion can play the role of an energy-transfer bridging ion between the Nd³⁺ ion and Er³⁺ ion. To investigate the effect of the Nd³⁺ ion, the decay curves of the ⁴S_3/2 → ⁴I_15/2 transition at 540 nm are measured and analyzed. The NaYF₄: 20% Yb³⁺, 2% Er³⁺, 0.5% Nd³⁺ nanocrystals have the highest emission intensity among all samples under 808-nm excitation. The UC (upconversion) mechanism under 808-nm excitation is discussed in terms of the experimental results.