High-Throughput Roll-to-Roll Processed Large-Area Perovskite Solar Cells Using Rapid Radiation Annealing Technique.

The development of a stable roll-to-roll (R2R) process for flexible large-area perovskite solar cells (PSCs) and modules is a pressing challenge. In this study, we introduced a new R2R PSC manufacturing system that employs a two-step deposition method for coating perovskite and uses intensive pulsed light (IPL) for annealing. This system has successfully fabricated small-sized cells and the first-ever large-sized, R2R-processed flexible modules. A key focus of our work was to accelerate the conversion of PbI2 to perovskite. To this end, we utilized IPL annealing and incorporated additives into the PbI2 layer. With these modifications, the R2R-processed perovskite films achieved a power conversion efficiency (PCE) of 16.87%, representing the highest reported value for R2R two-step processed PSCs. However, these cells exhibited hysteresis in reverse and forward PCE measurements. To address this, we introduced a dual-annealing process consisting of IPL followed by a 2-min thermal heating step. This approach successfully reduced hysteresis, resulting in low-hysteresis, R2R-processed flexible PSCs. Moreover, we fabricated large-scale flexible modules (10 × 10 cm2) with a PCE of 11.25% using the dual-annealing system, marking a significant milestone in this field.

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.

Disordered Phase-Assisted Growth of Organic Semiconductor Crystals on Self-Assembled Monolayer Templates.

Achieving high mobility and bias stability is a challenging obstacle in the advancement of organic thin-film transistors (OTFTs). To this end, the fabrication of high-quality organic semiconductor (OSC) thin films is critical for OTFTs. Self-assembled monolayers (SAMs) have been used as growth templates for high-crystalline OSC thin films. Despite significant research progress in the growth of OSC on SAMs, a detailed understanding of the growth mechanism of the OSC thin films on a SAM template is lacking, which has limited its use. In this study, the effects of the structure (thickness and molecular packing) of SAM on the nucleation and growth behavior of the OSC thin films were investigated. We found that disordered SAM molecules assisted in the surface diffusion of the OSC molecules and resulted in a small nucleation density and large grain size of the OSC thin films. Moreover, a thick SAM with disordered SAM molecules on the top was found to be beneficial for the high mobility and bias stability of the OTFTs.

Transparent superhydrophobic/translucent superamphiphobic coatings based on silica-fluoropolymer hybrid nanoparticles.

This paper describes a simple approach to prepare a transparent superhydrophobic coating and a translucent superamphiphobic coating via spraying silica-fluoropolymer hybrid nanoparticles (SFNs) without any pre- or post-treatment of substrates; these nanoparticles create both microscale and nanoscale roughness, and fluoropolymer acts as a low surface energy binder. We also demonstrate the effects of varying the concentration of the SFN sol on the water and hexadecane repellency and on the transparency of the coated glass substrates. An increase in the concentration of the sol facilitates the transition between the superhydrophobic/transparent and superamphiphobic/translucent states. This transition results from an increase in the discontinuities in the three-phase (solid-liquid-gas) contact line and in the light scattering properties due to micropapillae tuned by varying the concentration of the sol. This versatile and controllable approach can be applied to a variety of substrates over large areas and may provide a wide range of applications for self-cleaning coatings of optoelectronics, liquid-repellent coatings, and microfluidic systems.