Muammer Y. Yaman

The power conversion efficiency (PCE) of spin-coated, ≪1 cm2, perovskite solar cells has exceeded 25%. The PCEs of the large-area perovskite solar cells made by scalable deposition techniques, however, are typically lower. One frequent element to improving performance in perovskites has been the utilization of nonscalable and low materials utilization, spin-based passivation treatments to reduce traps and defects in perovskite thin film absorber layers. Herein, we report a more sustainable passivation technique for large-area perovskite films via subsequent linear slot-die coating of a benzylammonium iodide (BAI) passivant formulation on the surface of previously deposited perovskite absorber layers. The BAI-passivated perovskite films demonstrate apparent larger grain size, higher photoluminescence (PL) intensity, reduced recombination rates as evidenced by longer PL lifetimes, and better spatial PL uniformity. The champion cell with optimized slot-die BAI passivation exhibited an improved PCE of ∼20.3%, as compared to 18.7% for the control device.

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

The molten-salt assisted self-assembly (MASA) process is applicable to fabricate high quality mesoporous metal lithiate thin films that exhibit excellent performance as electrocatalysts for water oxidation.