Lead mixed-halide perovskite nanocrystals offer exceptional optical properties but suffer from ionic instability and ion migration under external stimuli, challenging their integration into devices. While such effects have been well studied in individual NCs and films, their impact on nanocrystal assemblies remains less understood. Here, we investigate the effect of strong external electric fields on self-assembled CsPbBr2.4Cl0.6 nanocrystal superlattices. By positioning individual superlattices between micrometer-sized capacitor plates, we analyze field-induced changes in photoluminescence, elemental composition, and morphology. We observe position-dependent changes in emission energy correlated with halide ion redistribution, revealed by energy-dispersive X-ray analysis, resulting from a nonuniform electric field across the superlattice, and supported by finite-element simulations. In situ mass spectrometry detects bromide sublimation, suggesting a combination of inter- and intraparticle halide diffusion. Irreversibility of photoluminescence and morphological changes further support a field-driven reorganization. These findings reveal responses of CsPbBr2.4Cl0.6 superlattices subject to external electric fields, relevant for their implementation in optoelectronic applications.

Supercrystals of lead-halide perovskite nanocrystals combine the semiconducting properties of bulk perovskites with quantum confinement effects and extend them to the macroscopic scale. Supercrystals assembled via a two-layer phase diffusion process using an acetonitrile antisolvent were recently shown to be unusually robust. We investigate how the acetonitrile-assisted self-assembly process influences surface chemistry, the atomic lattice of nanocrystals, and the structure of the supercrystal. Using quantitative NMR spectroscopy, nanofocused X-ray diffraction, and optical spectroscopy, we show that a reduced density of the ligand shell caused by the exposure to acetonitrile in the assembly underlies the mechanical robustness of these supercrystals. Ligand stripping further drives a highly size-selecting lateral growth of the supercrystal and induces anisotropic relaxation of the nanocrystal atomic lattice while preserving the electronic coupling and robust light-emitting properties of the assembly. That enables the mechanical manipulation of supercrystals such as stacking, thereby opening new avenues for integration into optoelectronic devices.

Metal-free halide perovskites have recently emerged as promising candidates for optoelectronic applications. However, their synthesis has largely depended on water-based single-crystal growth that limits material diversity, scalability, and practical implementation. Here, we present a mechanochemical route to synthesize N,N-diazabicyclo[2.2.2]octonium (H-DABCO)-based halide perovskites from the (DABCO)(NH4)X3 (X = I, Br) compositions. The structural properties were confirmed by X-ray diffraction and solid-state nuclear magnetic resonance spectroscopy. Thin films were prepared from mechanosynthetic powders by spin-coating and characterized by in-situ grazing incidence wide-angle scattering measurements, as well as by UV-vis absorption and steady-state photoluminescence spectroscopy. This mechanosynthetic strategy provides a scalable, environmentally friendly pathway to broaden the scope of metal-free perovskites and advance their potential in sustainable optoelectronic technologies.

Cesium lead bromide perovskite nanocrystals (NCs) covered with lecithin ligands in liquid toluene suspensions were investigated with a range of complementary scattering techniques and nuclear magnetic resonance to reveal their surface chemistry, solution structure, and diffusive dynamics. Distinct self-diffusion coefficients were determined and analyzed, namely the center-of-mass diffusion of the NCs and of coexisting ligand micelles, as well as the lateral diffusion of the l-α-lecithin ligand relative to the NC surface and within the micelles. We find a dynamic surface equilibrium, represented by a tunable lateral diffusion coefficient dependent on the ligand surface density. This phenomenon can be rationalized by the extraordinary binding of this zwitterionic ligand and its ability to bind via two different binding sites. These results highlight the dynamic nature of the ligand binding to lead halide perovskite NCs.

Reflectometry experiments can benefit substantially from recent advances in machine learning, enabling real-time data analysis, informed decision making during measurements, optimized experimental conditions and ultimately closed-loop experimental workflows. While most prior automation efforts have focused on X-ray reflectometry, neutron reflectometry can also benefit to the same extent. We report the first machine-learning-based pipeline for real-time neutron reflectometry deployed at the Institut Laue-Langevin (ILL), Grenoble, France. The system integrates the reflectorch package into the data acquisition workflow using the IT infrastructure of the facility. This enables analysis that is two orders of magnitude faster than conventional tools, allowing real-time estimation of physical parameters with associated uncertainties and feedback through a graphical user interface. The pipeline has been successfully tested at the ILL, and can be adapted to work at other neutron facilities seeking to enable real-time feedback-driven reflectometry analysis.

We compare the fluorescence properties of CsPbBr2Cl nanocrystals, obtained via two distinct synthetic procedures and self-assembled into supercrystals using the same antisolvent crystallization technique. By spatially resolved fluorescence (lifetime) measurements we demonstrate that the optical properties of the supercrystals depend on the specific synthesis conditions of the constituting nanocrystals. Using scanning electron microscopy, small-angle X-ray scattering, and nuclear magnetic resonance spectroscopy, we find evidence that spatial fluctuations in the supercrystal fluorescence correlate with the ligand sphere of the nanocrystals. Specifically, homogeneous surface passivation of the nanocrystals leads to consistent interparticle distances and increased structural order within the supercrystals, resulting in a uniform fluorescence center wavelength and lifetime. The results of this study emphasize the importance of the relationship between crystalline structure and ligand configuration in controlling the optical properties of lead halide perovskite supercrystals.

Hybrid lead halide perovskites are considered a highly promising material for future advancements of photovoltaic technologies due to their outstanding optoelectronic properties. Nevertheless, their instability under ambient conditions hinders the large-scale adoption of perovskite solar cells. Two-dimensional (2D) perovskites have been proposed to address the issue of instability, but these structures display anisotropic optoelectronic properties related to their layered structure. Moreover, their crystallization pathways are not well understood. Here we present insight into the crystallization process of 2-phenyethylammonium (PEA+) Ruddlesden-Popper 2D perovskites with iodide (I−) and bromide (Br−) anions by performing in situ grazing incidence wide-angle X-ray scattering (GIWAXS) measurements of thin films with various compositions during their fabrication via spin-coating and thermal annealing. The GIWAXS data reveals the structure of coexisting 2D perovskite phases and their orientation with respect to the substrate for different precursor compositions. The bromide-based compositions exhibited crystallization already during spin-coating, while iodide-based compositions required thermal annealing to induce the crystallization process. For the latter ones, additional polymorphs were found, suggesting the intrinsic differences for compositions with different halides.

The adoption of grazing-incidence wide-angle x-ray scattering (GIWAXS) has expanded rapidly, as it has proven invaluable for extracting a wealth of structural information from thin films and surfaces on the atomic and molecular scale. The amount of scattering data collected in such experiments creates new challenges associated with relatively slow conventional data analysis due to the need for human input. Existing machine learning-based Bragg-peak detection approaches have demonstrated the potential of automation, but significant challenges such as weak peaks, peaks on top of Debye-Scherrer rings, and difficulties in differentiating closely spaced peaks remain. We introduce a powerful model for GIWAXS peak detection based on a visual transformer that achieves a performance strongly surpassing existing models. We evaluate our new approach using a diverse set of labelled, experimental GIWAXS patterns from perovskite and organic thin films. Notably, we demonstrate for the first time the reliable detection of Bragg peaks superimposed on other peaks and Debye-Scherrer rings, a significant improvement in detecting low-intensity peaks and a substantially stronger overall performance. In addition, we evaluate both pre- and postprocessing latency, confirming that the overall analysis speed is sufficient for real-time applications. With its ability to deliver a fast and high-performance analysis, our approach enables new types of GIWAXS measurements, including closed-loop experiments, that were previously limited by analysis bottlenecks. The studied model could potentially also be applied to other datasets with a large dynamic range and weak signal-to-noise ratio.

Hybrid metal halide perovskites have emerged as some of the leading semiconductors in photovoltaics. Despite their remarkable power conversion efficiencies, these materials remain unstable under device operating conditions. One of the main instabilities relates to the interface with the contact layers in photovoltaic devices, such as metal oxides. We rely on halogen bonding (XB) using 1,4-diiodotetrafluorobenzene (TFDIB) to modulate the interface of the TiO2 electron-transport layer, demonstrating the improvement of perovskite solar cell operational stability. Furthermore, we complement this strategy with the use of iodo-functionalized Zn-phthalocyanine modulator of the hole-transporting material, which passivate the interface while enhancing the power conversion efficiency, showcasing the potential of XB in hybrid photovoltaics.

Hybrid halide perovskites are among the most promising candidates for next‐generation photovoltaics. The most investigated perovskite solar cells are lead based, which poses environmental concerns, making finding sustainable alternatives a pressing issue. Tin‐based halide perovskites are attracting interest as an alternative. However, their application in photovoltaics is hindered by the high concentration of defects and sensitivity to oxidation, compromising their performance and stability. Herein, perfluoroarene organic cations, namely 2‐(perfluorophenyl)methylammonium (F‐BNA) and 1,4‐(perfluorophenyl)dimethylammonium (F‐PDMA), are applied to form layered (2D) Ruddlesden–Popper and Dion–Jacobson tin‐based perovskites, respectively. Following a detailed structural and optoelectronic characterization, the perfluoroarenes are applied to formamidinium (FA)‐based FASnI 3 perovskite solar cells and an effective solvent is identified for their processing, 2‐pentanol. While F‐PDMA forms a 2D/3D heterostructure, F‐BNA remains assembled as a molecular interlayer, demonstrating higher photovoltaic performance with limited operational stability. This challenges the conventional role of mixed‐dimensional heterostructures in tin perovskite photovoltaics and opens new perspectives for advanced material design and device engineering.

Hybrid organic-inorganic layered (2D) halide perovskites have demonstrated advantages in improving the performance and stability of perovskite solar cells, and there is an ongoing interest in tailoring organic cations for their application in photovoltaics. We apply tailored molecular systems based on perfluorinated benzylammonium (F-BNA) and 1,4-phenylenedimethylammonium (F-PDMA) cations, forming Ruddlesden-Popper and Dion-Jacobson perovskite phases, respectively, at the interface with 3D perovskite layers in conventional n-i-p perovskite solar cells. The characteristics of 2D/3D perovskite phases are investigated through a combination of techniques including X-ray diffraction, UV-vis absorption, and photoluminescence spectroscopy. We demonstrate the beneficial effects of perfluoroarene perovskite phases in improving the stability and performance toward advancing photovoltaics.

Superlattices of quantum‐confined perovskite nanocrystals (5–6 nm) present an interesting example of colloidal crystals because of the interplay between nanoscopic parameters (nanocrystal sizes, shapes, and colloidal softness) and the microscopic shapes of their assemblies. These superlattices are reported as rectangular or rhombic, with little discussion of the outcomes of self‐assembly experiments which are worthwhile to study given the rising interest in the optical properties of these nanomaterials. It is observed that various superlattice shapes are produced in a single solvent evaporation experiment from a nanocrystal dispersion drop‐casted onto a tilted substrate. The observed shapes are categorized as rhombi, rectangles, and hollow frames (including hollow rectangular frames, nested structures, and interconnected fragments). The influence of self‐assembly conditions is studied by optical microscopy, and the nanocrystal circularity, aspect ratio, and size are quantified by transmission electron microscopy with additional insights into the superlattice structure provided by X‐ray nanodiffraction. The results suggest that rhombic shapes arise from a subpopulation of nanocrystals with broader size and shape dispersions, whereas more uniform nanocrystals form rectangular structures (either solid or hollow). The solvent evaporation dynamics and diffusion of the drying liquid contribute to forming more complex shapes, such as nested frames and cracked and multidomain superlattices.

The preparation of perovskite solar cells from the liquid phase is a cornerstone of their immense potential. However, a clear relationship between the precursor ink and the formation of the resulting perovskite is missing. Established theories, such as heterogeneous nucleation and lead complex colloid formation, often prove unreliable, which has led to an overreliance on heuristics. Most high-performing perovskites use additives to control crystallization. Their role during crystallization is, however, elusive. Here, we provide evidence that typical crystallization additives do not predominantly impact the nucleation phase but rather facilitate coarsening grain growth by increasing ion mobility across grain boundaries. Drawing from the insights of our broad, interdisciplinary study that combines ex and in situ characterization methods, devices, simulations, and density function theory calculation, we propose a concept that proves valid for various additives and perovskite formulations. Moreover, we establish a direct link between additive engineering and perovskite post-processing, offering a unified framework for advancing material design and process engineering.

In this work, Au nanoparticles (NPs) were synthesized by laser ablation in liquids (LASiS) by using chlorobenzene, resulting in a stable suspension with a plasmon resonance band around 560 nm. This Au NPs suspension was subsequently used in the antisolvent step for the preparation of the CsFAMA perovskite films. Morphological analyses revealed an increase in the grain size in the Au NPs-modified films, attributed to Au NPs-assisted heterogeneous nucleation. In situ GIWAXS measurements were conducted during film crystallization, pointing out that in the Au NPs-modified films prepared with diluted suspension, the peaks corresponding to the cubic α-phase formed faster and with reduced PbI2 content, when compared to the control film produced without Au NPs. The characterization of solar cell devices fabricated with Au NPs-modified CsFAMA films presented the influence of the NPs concentration on photovoltaic performance. Devices prepared with diluted Au NPs suspensions exhibited a higher power conversion efficiency (PCE) over time, improved stability, and a reduced hysteresis index.

Recent advancements in X-ray sources and detectors have dramatically increased data generation, leading to a greater demand for automated data processing. This is particularly relevant for real-time grazing-incidence wide-angle X-ray scattering (GIWAXS) experiments which can produce hundreds of thousands of diffraction images in a single day at a synchrotron beamline. Deep learning (DL)-based peak-detection techniques are becoming prominent in this field, but rigorous benchmarking is essential to evaluate their reliability, identify potential problems, explore avenues for improvement and build confidence among researchers for seamless integration into their workflows. However, the systematic evaluation of these techniques has been hampered by the lack of annotated GIWAXS datasets, standardized metrics and baseline models. To address these challenges, we introduce a comprehensive framework comprising an annotated experimental dataset, physics-informed metrics adapted to the GIWAXS geometry and a competitive baseline – a classical, non-DL peak-detection algorithm optimized on our dataset. Furthermore, we apply our framework to benchmark a recent DL solution trained on simulated data and discover its superior performance compared with our baseline. This analysis not only highlights the effectiveness of DL methods for identifying diffraction peaks but also provides insights for further development of these solutions.

The application of perovskite photovoltaics is hampered by issues related to the operational stability upon exposure to external stimuli, such as voltage bias and light. The dynamic control of the properties of perovskite materials in response to light could ensure the durability of perovskite solar cells, which is especially critical at the interface with charge-extraction layers. We have applied a functionalized photochromic material based on spiro-indoline naphthoxazine at the interface with hole-transport layers in the corresponding perovskite solar cells with the aim of stabilizing them in response to voltage bias and light. We demonstrate photoinduced transformation by a combination of techniques, including transient absorption spectroscopy and Kelvin probe force microscopy. As a result, the application of the photochromic derivative offers improvements in photovoltaic performance and operational stability, highlighting the potential of dynamic photochromic strategies in perovskite photovoltaics.

Perovskite solar cells have garnered significant interest, yet their limited operational stability remains a major challenge. This is especially pronounced at the interface with charge transport layers. In inverted p-i-n perovskite solar cells, fullerene-based electron transport layers pose critical stability issues. This has stimulated the application of low-dimensional perovskite interlayers featuring alkylammonium-based organic spacers that template perovskite slabs to enhance operational stabilities. However, these materials are traditionally based on organic cations that are electronically insulating, limiting charge extraction and device performance. We demonstrate the capacity to access low-dimensional perovskites incorporating electron-accepting naphthalimide- and naphthalenediimide-based spacers and use the corresponding organic moieties to modify or replace fullerene electron-transport layers, forming an electroactive interface that serves charge-transport. This resulted in superior performance with power conversion efficiencies exceeding 20% and enhanced operational stability, highlighting the potential of electroactive interlayers for advancing inverted perovskite solar cells.

Reconstructing the structure of thin films and multilayers from measurements of scattered x-rays or neutrons is key to progress in physics, chemistry, and biology. However, finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms, which typically results in unreliable analysis with only a single potential solution identified. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds, redefining standards in reflectometry. Our method, prior-amortized neural posterior estimation (PANPE), combines simulation-based inference with adaptive priors that inform the inference network about known structural properties and controllable experimental conditions. PANPE networks support key scenarios such as high-throughput sample characterization, real-time monitoring of evolving structures, or the corefinement of several experimental datasets and can be adapted to provide fast, reliable, and flexible inference across many other inverse problems.