Correction to "A Templating Approach to Controlling the Growth of Coevaporated Halide Perovskites".

[This corrects the article DOI: 10.1021/acsenergylett.3c01368.].

A Templating Approach to Controlling the Growth of Coevaporated Halide Perovskites.

Metal halide perovskite semiconductors have shown significant potential for use in photovoltaic (PV) devices. While fabrication of perovskite thin films can be achieved through a variety of techniques, thermal vapor deposition is particularly promising, allowing for high-throughput fabrication. However, the ability to control the nucleation and growth of these materials, particularly at the charge-transport layer/perovskite interface, is critical to unlocking the full potential of vapor-deposited perovskite PV. In this study, we explore the use of a templating layer to control the growth of coevaporated perovskite films and find that such templating leads to highly oriented films with identical morphology, crystal structure, and optoelectronic properties independent of the underlying layers. Solar cells incorporating templated FA0.9Cs0.1PbI3-xClx show marked improvements with steady-state power conversion efficiency over 19.8%. Our findings provide a straightforward and reproducible method of controlling the charge-transport layer/coevaporated perovskite interface, further clearing the path toward large-scale fabrication of efficient PV devices.

Deep Learning-Assisted Multivariate Analysis for Nanoscale Characterization of Heterogeneous Beam-Sensitive Materials.

Nanoscale materials characterization often uses highly energetic probes which can rapidly damage beam-sensitive materials, such as hybrid organic-inorganic compounds. Reducing the probe dose minimizes the damage, but often at the cost of lower signal-to-noise ratio (SNR) in the acquired data. This work reports the optimization and validation of principal component analysis (PCA) and nonnegative matrix factorization for the postprocessing of low-dose nanoscale characterization data. PCA is found to be the best approach for data denoising. However, the popular scree plot-based method for separation of principal and noise components results in inaccurate or excessively noisy models of the heterogeneous original data, even after Poissonian noise weighting. Manual separation of principal and noise components produces a denoised model which more accurately reproduces physical features present in the raw data while improving SNR by an order of magnitude. However, manual selection is time-consuming and potentially subjective. To suppress these disadvantages, a deep learning-based component classification method is proposed. The neural network model can examine PCA components and automatically classify them with an accuracy of >99% and a rate of ∼2 component/s. Together, multivariate analysis and deep learning enable a deeper analysis of nanoscale materials' characterization, allowing as much information as possible to be extracted.

Overcoming Nanoscale Inhomogeneities in Thin-Film Perovskites via Exceptional Post-annealing Grain Growth for Enhanced Photodetection.

Antisolvent-assisted spin coating has been widely used for fabricating metal halide perovskite films with smooth and compact morphology. However, localized nanoscale inhomogeneities exist in these films owing to rapid crystallization, undermining their overall optoelectronic performance. Here, we show that by relaxing the requirement for film smoothness, outstanding film quality can be obtained simply through a post-annealing grain growth process without passivation agents. The morphological changes, driven by a vaporized methylammonium chloride (MACl)-dimethylformamide (DMF) solution, lead to comprehensive defect elimination. Our nanoscale characterization visualizes the local defective clusters in the as-deposited film and their elimination following treatment, which couples with the observation of emissive grain boundaries and excellent inter- and intragrain optoelectronic uniformity in the polycrystalline film. Overcoming these performance-limiting inhomogeneities results in the enhancement of the photoresponse to low-light (<0.1 mW cm-2) illumination by up to 40-fold, yielding high-performance photodiodes with superior low-light detection.

Determining Out-of-Plane Hole Mobility in CuSCN via the Time-of-Flight Technique To Elucidate Its Function in Perovskite Solar Cells.

Copper(I) thiocyanate (CuSCN) is a stable, low-cost, solution-processable p-type inorganic semiconductor used in numerous optoelectronic applications. Here, for the first time, we employ the time-of-flight (ToF) technique to measure the out-of-plane hole mobility of CuSCN films, enabled by the deposition of 4 µm-thick films using aerosol-assisted chemical vapor deposition (AACVD). A hole mobility of ∼10-3 cm2/V s was measured with a weak electric field dependence of 0.005 cm/V1/2. Additionally, by measuring several 1.5 µm CuSCN films, we show that the mobility is independent of thickness. To further validate the suitability of our AACVD-prepared 1.5 µm-thick CuSCN film in device applications, we demonstrate its incorporation as a hole transport layer (HTL) in methylammonium lead iodide (MAPbI3) perovskite solar cells (PSCs). Our AACVD films result in devices with measured power conversion efficiencies of 10.4%, which compares favorably with devices prepared using spin-coated CuSCN HTLs (12.6%), despite the AACVD HTLs being an order of magnitude thicker than their spin-coated analogues. Improved reproducibility and decreased hysteresis were observed, owing to a combination of excellent film quality, high charge-carrier mobility, and favorable interface energetics. In addition to providing a fundamental insight into charge-carrier mobility in CuSCN, our work highlights the AACVD methodology as a scalable, versatile tool suitable for film deposition for use in optoelectronic devices.

Unusual Thermal Boundary Resistance in Halide Perovskites: A Way To Tune Ultralow Thermal Conductivity for Thermoelectrics.

Halide perovskites have emerged as promising candidates as the active material in photovoltaics and light-emitting diodes. They possess unusual bulk thermal transport properties that have been the focus of a number of studies, but there is much less understanding of thermal transport in thin films where a diverse range of structures and morphologies are accessible. Here, we report on the tuning of in-plane thermal conductivity in methylammonium lead iodide thin films by morphological control. Using 3-ω measurements, we find that the room temperature thermal conductivity of thermally evaporated methylammonium lead iodide perovskite films ranges from 0.31 to 0.59 W/(m K). We measure a discontinuity in thermal conductivity at the orthorhombic-tetragonal phase transition and explore this using density functional theory and attributing it to a collapse in the phonon group velocity along the c-axis of the tetragonal crystal. Moreover, we have quantified the thermal boundary resistance (Kapitza resistance) for thermally evaporated films, allowing us to estimate the Kapitza length, which is 36 ± 2 nm at room temperature and 15 ± 2 nm at 100 K. Curiously, the Kapitza resistance has a strong temperature dependence which we also explore using density functional theory, with these results suggesting an important role of methylammonium rotational modes in scattering phonons at the crystallite boundaries.