From Chalcogen Bonding to S-π Interactions in Hybrid Perovskite Photovoltaics.

The stability of hybrid organic-inorganic halide perovskite semiconductors remains a significant obstacle to their application in photovoltaics. To this end, the use of low-dimensional (LD) perovskites, which incorporate hydrophobic organic moieties, provides an effective strategy to improve their stability, yet often at the expense of their performance. To address this limitation, supramolecular engineering of noncovalent interactions between organic and inorganic components has shown potential by relying on hydrogen bonding and conventional van der Waals interactions. Here, the capacity to access novel LD perovskite structures that uniquely assemble through unorthodox S-mediated interactions is explored by incorporating benzothiadiazole-based moieties. The formation of S-mediated LD structures is demonstrated, including one-dimensional (1D) and layered two-dimensional (2D) perovskite phases assembled via chalcogen bonding and S-π interactions, through a combination of techniques, such as single crystal and thin film X-ray diffraction, as well as solid-state NMR spectroscopy, complemented by molecular dynamics simulations, density functional theory calculations, and optoelectronic characterization, revealing superior conductivities of S-mediated LD perovskites. The resulting materials are applied in n-i-p and p-i-n perovskite solar cells, demonstrating enhancements in performance and operational stability that reveal a versatile supramolecular strategy in photovoltaics.

Helical interfacial modulation for perovskite photovoltaics.

Hybrid metal halide perovskites have demonstrated remarkable performances in modern photovoltaics, although their stabilities remain limited. We assess the capacity to advance their properties by relying on interfacial modulators featuring helical chirality based on P,M-(1-methylene-3-methyl-imidazolium)[6]helicene iodides. We investigate their characteristics, demonstrating comparable charge injection for enantiomers and the racemic mixture. Overall, they maintain the resulting photovoltaic performance while improving operational stability, challenging the role of helical chirality in the interfacial modulation of perovskite solar cells.

Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures.

Hybrid halide perovskites are attractive candidates for resistive switching memories in neuromorphic computing applications due to their mixed ionic-electronic conductivity. Moreover, their exceptional optoelectronic characteristics make them effective as semiconductors in photovoltaics, opening perspectives for self-powered memory elements. These devices, however, remain unexploited, which is related to the variability in their switching characteristics, weak endurance, and retention, which limit their performance and practical use. To address this challenge, we applied low-dimensional perovskite capping layers onto 3D mixed halide perovskites using two perfluoroarene organic cations, namely (perfluorobenzyl)ammonium and (perfluoro-1,4-phenylene)dimethylammonium iodide, forming Ruddlesden-Popper and Dion-Jacobson 2D perovskite phases, respectively. The corresponding mixed-dimensional perovskite heterostructures were used to fabricate resistive switching memories based on perovskite solar cell architectures, showing that the devices based on perfluoroarene heterostructures exhibited enhanced performance and stability in inert and ambient air atmosphere. This opens perspectives for multidimensional perovskite materials in durable self-powered memory elements in the future.

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Halide Segregated Crystallization of Mixed-Halide Perovskites Revealed by In Situ GIWAXS.

Mixed-halide perovskites of the composition MAPb(BrxI1-x)3, which seem to exhibit a random and uniform distribution of halide ions in the absence of light, segregate into bromide- and iodide-rich phases under illumination. This phenomenon of halide segregation has been widely investigated in the photovoltaics context since it is detrimental for the material properties and ultimately the device performance of these otherwise very attractive materials. A full understanding of the mechanisms and driving forces has remained elusive. In this work, a study of the crystallization pathways and the mixing behavior during deposition of MAPb(BrxI1-x)3 thin films with varying halide ratios is presented. In situ grazing incidence wide-angle scattering (GIWAXS) reveals the distinct crystallization behavior of mixed-halide perovskite compositions during two different fabrication routes: nitrogen gas-quenching and the lead acetate route. The perovskite phase formation of mixed-halide thin films hints toward a segregation tendency since separate crystallization pathways are observed for iodide- and bromide-rich phases within the mixed compositions. Crystallization of the bromide perovskite phase (MAPbBr3) is already observed during spin coating, while the iodide-based fraction of the composition forms solvent complexes as an intermediate phase, only converting into the perovskite phase upon thermal annealing. These parallel crystallization pathways result in mixed-halide perovskites forming from initially halide-segregated phases only under the influence of heating.

Minimizing Interfacial Recombination in 1.8 eV Triple-Halide Perovskites for 27.5% Efficient All-Perovskite Tandems.

All-perovskite tandem solar cells show great potential to enable the highest performance at reasonable costs for a viable market entry in the near future. In particular, wide-bandgap (WBG) perovskites with higher open-circuit voltage (VOC ) are essential to further improve the tandem solar cells' performance. Here, a new 1.8 eV bandgap triple-halide perovskite composition in conjunction with a piperazinium iodide (PI) surface treatment is developed. With structural analysis, it is found that the PI modifies the surface through a reduction of excess lead iodide in the perovskite and additionally penetrates the bulk. Constant light-induced magneto-transport measurements are applied to separately resolve charge carrier properties of electrons and holes. These measurements reveal a reduced deep trap state density, and improved steady-state carrier lifetime (factor 2.6) and diffusion lengths (factor 1.6). As a result, WBG PSCs achieve 1.36 V VOC , reaching 90% of the radiative limit. Combined with a 1.26 eV narrow bandgap (NBG) perovskite with a rubidium iodide additive, this enables a tandem cell with a certified scan efficiency of 27.5%.

Compact sample environment for in situ X-ray scattering during spin-coating.

We demonstrate a compact sample environment for the in situ study of crystallization kinetics of thin films on synchrotron beamlines, featuring atmospheric control, automated deposition, spin-coating, and annealing stages. The setup is suitable for studying thin film growth in real time using grazing-incidence X-ray diffraction techniques. Humidity and oxygen levels are being detected by sensors. The spinning stage exhibits low vertical oscillation amplitude (∼3µm at speeds up to 10 000 rpm) and can optionally be employed for antisolvent application or gas quenching to investigate the impact of these techniques, which are often used to assist thin film growth. Differential reflectance spectroscopy is implemented in the spin-coater environment for inspecting thin film thickness and optical properties. The infrared radiation-based annealing system consists of a halogen lamp and a holder with an adjustable lamp-to-sample distance, while the sample surface temperature is monitored by a pyrometer. All features of the sample environment can be controlled remotely by the control software at synchrotron beamlines. In order to test and demonstrate the performance, the crystallization pathway of the antisolvent-assisted MAPbI3 (MA = methylammonium) perovskite thin film during the spinning and annealing stages is monitored and discussed.

Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments.

Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.

Elucidating Structure Formation in Highly Oriented Triple Cation Perovskite Films.

Metal halide perovskites are an emerging class of crystalline semiconductors of great interest for application in optoelectronics. Their properties are dictated not only by their composition, but also by their crystalline structure and microstructure. While significant efforts are dedicated to the development of strategies for microstructural control, significantly less is known about the processes that govern the formation of their crystalline structure in thin films, in particular in the context of crystalline orientation. This work investigates the formation of highly oriented triple cation perovskite films fabricated by utilizing a range of alcohols as an antisolvent. Examining the film formation by in situ grazing-incidence wide-angle X-ray scattering reveals the presence of a short-lived highly oriented crystalline intermediate, which is identified as FAI-PbI2 -xDMSO. The intermediate phase templates the crystallization of the perovskite layer, resulting in highly oriented perovskite layers. The formation of this dimethylsulfoxide (DMSO) containing intermediate is triggered by the selective removal of N,N-dimethylformamide (DMF) when alcohols are used as an antisolvent, consequently leading to differing degrees of orientation depending on the antisolvent properties. Finally, this work demonstrates that photovoltaic devices fabricated from the highly oriented films, are superior to those with a random polycrystalline structure in terms of both performance and stability.

Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

Optical Properties of Perovskite-Organic Multiple Quantum Wells.

A comprehensive study of the optical properties of CsPbBr3 perovskite multiple quantum wells (MQW) with organic barrier layers is presented. Quantum confinement is observed by a blue-shift in absorption and emission spectra with decreasing well width and agrees well with simulations of the confinement energies. A large increase of emission intensity with thinner layers is observed, with a photoluminescence quantum yield up to 32 times higher than that of bulk layers. Amplified spontaneous emission (ASE) measurements show very low thresholds down to 7.3 µJ cm-2 for a perovskite thickness of 8.7 nm, significantly lower than previously observed for CsPbBr3 thin-films. With their increased photoluminescence efficiency and low ASE thresholds, MQW structures with CsPbBr3 are excellent candidates for high-efficiency perovskite-based LEDs and lasers.

Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering.

The Python package mlreflect is demonstrated, which implements an optimized pipeline for the automated analysis of reflectometry data using machine learning. The package combines several training and data treatment techniques discussed in previous publications. The predictions made by the neural network are accurate and robust enough to serve as good starting parameters for an optional subsequent least-mean-squares (LMS) fit of the data. For a large data set of 242 reflectivity curves of various thin films on silicon substrates, the pipeline reliably finds an LMS minimum very close to a fit produced by a human researcher with the application of physical knowledge and carefully chosen boundary conditions. The differences between simulated and experimental data and their implications for the training and performance of neural networks are discussed. The experimental test set is used to determine the optimal noise level during training. The extremely fast prediction times of the neural network are leveraged to compensate for systematic errors by sampling slight variations in the data.

The Role of Alkyl Chain Length and Halide Counter Ion in Layered Dion-Jacobson Perovskites with Aromatic Spacers.

Layered hybrid perovskites based on Dion-Jacobson phases are of interest to various optoelectronic applications. However, the understanding of their structure-property relationships remains limited. Here, we present a systematic study of Dion-Jacobson perovskites based on (S)PbX4 (n = 1) compositions incorporating phenylene-derived aromatic spacers (S) with different anchoring alkylammonium groups and halides (X = I, Br). We focus our study on 1,4-phenylenediammonium (PDA), 1,4-phenylenedimethylammonium (PDMA), and 1,4-phenylenediethylammonium (PDEA) spacers. Systems based on PDA did not form a well-defined layered structure, showing the formation of a 1D structure instead, whereas the extension of the alkyl chains to PDMA and PDEA rendered them compatible with the formation of a layered structure, as shown by X-ray diffraction and solid-state NMR spectroscopy. In addition, the control of the spacer length affects optical properties and environmental stability, which is enhanced for longer alkyl chains and bromide compositions. This provides insights into their design for optoelectronic applications.

Multimodal host-guest complexation for efficient and stable perovskite photovoltaics.

Formamidinium lead iodide perovskites are promising light-harvesting materials, yet stabilizing them under operating conditions without compromising optimal optoelectronic properties remains challenging. We report a multimodal host-guest complexation strategy to overcome this challenge using a crown ether, dibenzo-21-crown-7, which acts as a vehicle that assembles at the interface and delivers Cs+ ions into the interior while modulating the material. This provides a local gradient of doping at the nanoscale that assists in photoinduced charge separation while passivating surface and bulk defects, stabilizing the perovskite phase through a synergistic effect of the host, guest, and host-guest complex. The resulting solar cells show power conversion efficiencies exceeding 24% and enhanced operational stability, maintaining over 95% of their performance without encapsulation for 500 h under continuous operation. Moreover, the host contributes to binding lead ions, reducing their environmental impact. This supramolecular strategy illustrates the broad implications of host-guest chemistry in photovoltaics.

A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure α-FAPbI3.

It is well established that the lack of understanding the crystallization process in a two-step sequential deposition has a direct impact on efficiency, stability, and reproducibility of perovskite solar cells. Here, we try to understand the solid-solid phase transition occurring during the two-step sequential deposition of methylammonium lead iodide and formamidinium lead iodide. Using metadynamics, x-ray diffraction, and Raman spectroscopy, we reveal the microscopic details of this process. We find that the formation of perovskite proceeds through intermediate structures and report polymorphs found for methylammonium lead iodide and formamidinium lead iodide. From simulations, we discover a possible crystallization pathway for the highly efficient metastable α phase of formamidinium lead iodide. Guided by these simulations, we perform experiments that result in the low-temperature crystallization of phase-pure α-formamidinium lead iodide.

Polymorphism and structure formation in copper phthalocyanine thin films.

Many polymorphic crystal structures of copper phthalocyanine (CuPc) have been reported over the past few decades, but despite its manifold applicability, the structure of the frequently mentioned α polymorph remained unclear. The base-centered unit cell (space group C2/c) suggested in 1966 was ruled out in 2003 and was replaced by a primitive triclinic unit cell (space group P 1). This study proves unequivocally that both α structures coexist in vacuum-deposited CuPc thin films on native silicon oxide by reciprocal space mapping using synchrotron radiation in grazing incidence. The unit-cell parameters and the space group were determined by kinematic scattering theory and provide possible molecular arrangements within the unit cell of the C2/c structure by excluded-volume considerations. In situ X-ray diffraction experiments and ex situ atomic force microscopy complement the experimental data further and provide insight into the formation of a smooth thin film by a temperature-driven downward diffusion of CuPc molecules during growth.

Nanoscale Phase Segregation in Supramolecular π-Templating for Hybrid Perovskite Photovoltaics from NMR Crystallography.

The use of layered perovskites is an important strategy to improve the stability of hybrid perovskite materials and their optoelectronic devices. However, tailoring their properties requires accurate structure determination at the atomic scale, which is a challenge for conventional diffraction-based techniques. We demonstrate the use of nuclear magnetic resonance (NMR) crystallography in determining the structure of layered hybrid perovskites for a mixed-spacer model composed of 2-phenylethylammonium (PEA+) and 2-(perfluorophenyl)ethylammonium (FEA+) moieties, revealing nanoscale phase segregation. Moreover, we illustrate the application of this structure in perovskite solar cells with power conversion efficiencies that exceed 21%, accompanied by enhanced operational stability.

Minimizing the Trade-Off between Photocurrent and Photovoltage in Triple-Cation Mixed-Halide Perovskite Solar Cells.

Its lower bandgap makes formamidinium lead iodide (FAPbI3) a more suitable candidate for single-junction solar cells than pure methylammonium lead iodide (MAPbI3). However, its structural and thermodynamic stability is improved by introducing a significant amount of MA and bromide, both of which increase the bandgap and amplify trade-off between the photocurrent and photovoltage. Here, we simultaneously stabilized FAPbI3 into a cubic lattice and minimized the formation of photoinactive phases such as hexagonal FAPbI3 and PbI2 by introducing 5% MAPbBr3, as revealed by synchrotron X-ray scattering. We were able to stabilize the composition (FA0.95MA0.05Cs0.05)Pb(I0.95Br0.05)3, which exhibits a minimal trade-off between the photocurrent and photovoltage. This material shows low energetic disorder and improved charge-carrier dynamics as revealed by photothermal deflection spectroscopy (PDS) and transient absorption spectroscopy (TAS), respectively. This allowed the fabrication of operationally stable perovskite solar cells yielding reproducible efficiencies approaching 22%.

Enhanced protein adsorption upon bulk phase separation.

In all areas related to protein adsorption, from medicine to biotechnology to heterogeneous nucleation, the question about its dominant forces and control arises. In this study, we used ellipsometry and quartz-crystal microbalance with dissipation (QCM-D), as well as density-functional theory (DFT) to obtain insight into the mechanism behind a wetting transition of a protein solution. We established that using multivalent ions in a net negatively charged globular protein solution (BSA) can either cause simple adsorption on a negatively charged interface, or a (diverging) wetting layer when approaching liquid-liquid phase separation (LLPS) by changing protein concentration (cp) or temperature (T). We observed that the water to protein ratio in the wetting layer is substantially larger compared to simple adsorption. In the corresponding theoretical model, we treated the proteins as limited-valence (patchy) particles and identified a wetting transition for this complex system. This wetting is driven by a bulk instability introduced by metastable LLPS exposed to an ion-activated attractive substrate.

Stabilization of Highly Efficient and Stable Phase-Pure FAPbI3 Perovskite Solar Cells by Molecularly Tailored 2D-Overlayers.

As a result of their attractive optoelectronic properties, metal halide APbI3 perovskites employing formamidinium (FA+ ) as the A cation are the focus of research. The superior chemical and thermal stability of FA+ cations makes α-FAPbI3 more suitable for solar-cell applications than methylammonium lead iodide (MAPbI3 ). However, its spontaneous conversion into the yellow non-perovskite phase (δ-FAPbI3 ) under ambient conditions poses a serious challenge for practical applications. Herein, we report on the stabilization of the desired α-FAPbI3 perovskite phase by protecting it with a two-dimensional (2D) IBA2 FAPb2 I7 (IBA=iso-butylammonium overlayer, formed via stepwise annealing. The α-FAPbI3 /IBA2 FAPb2 I7 based perovskite solar cell (PSC) reached a high power conversion efficiency (PCE) of close to 23 %. In addition, it showed excellent operational stability, retaining around 85 % of its initial efficiency under severe combined heat and light stress, that is, simultaneous exposure with maximum power tracking to full simulated sunlight at 80 °C over 500 h.