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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.
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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.
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Small π-conjugated organic molecules have attracted substantial attention in the past decade as they are considered as candidates for future organic-based (opto-)electronic applications. The molecular arrangement in the organic layer is one of the crucial parameters that determine the efficiency of a given device. The desired orientation of the molecules is achieved by a proper choice of the underlying substrate and growth conditions. Typically, one underlying material supports only one inherent molecular orientation at its interface. Here, we report on two different orientations of diindenoperylene (DIP) molecules on the same underlayer, i.e. on a few-layer MoS2 substrate. We show that DIP molecules adopt a lying-down orientation when deposited on few-layer MoS2 with horizontally oriented layers. In contrast, for vertically aligned MoS2 layers, DIP molecules are arranged in a standing-up manner. Employing in situ and real-time grazing-incidence wide-angle X-ray scattering (GIWAXS), we monitored the stress evolution within the thin DIP layer from the early stages of the growth, revealing different substrate-induced phases for the two molecular orientations. Our study opens up new possibilities for the next-generation of flexible electronics, which might benefit from the combination of MoS2 layers with unique optical and electronic properties and an extensive reservoir of small organic molecules.
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As a result of their attractive optoelectronic properties, metal halide APbI3 perovskites employing formamidinium (FA+ ) as the Aâ 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 500â h.
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We investigate blended donor:acceptor (D:A) thin films of the two donors diindenoperylene (DIP) and poly(3-hexylthiophene) (P3HT) mixed with the strong acceptor 1,3,4,5,7,8-hexafluorotetracyanonaphthoquinodimethane (F6TCNNQ) using Polarization-Modulation Infrared Reflection-Absorption Spectroscopy (PMIRRAS). For DIP:F6TCNNQ thin films we first carry out a comprehensive study of the structure as a function of the D : A mixing ratio, which guides the analysis of the PMIRRAS spectra. In particular, from the red-shift of the nitrile (C[triple bond, length as m-dash]N) stretching of F6TCNNQ in the different mixtures with DIP, we quantify the average ground-state charge-transfer (GS-CT) to be ρavg = (0.84 ± 0.04) e. The PMIRRAS data for P3HT:F6TCNNQ blended films reveal nearly the same shift of the CT-affected C[triple bond, length as m-dash]N stretching peak for this system. This points towards a very similar CT strength for the two systems. We extend the analysis to the relative intensity of the C[triple bond, length as m-dash]N to the C[double bond, length as m-dash]C stretching modes of F6TCNNQ in the mixtures with DIP and P3HT, respectively, and support it with DFT calculations for the isolated F6TCNNQ. Such comparison allows to identify the vibrational signatures of the acceptor mono-anion in P3HT:F6TCNNQ, thus indicating a much stronger, integer CT-type interactions for this system, in agreement with available optical spectroscopy data. Our findings stress the importance of a simultaneous analysis of C[triple bond, length as m-dash]N and C[double bond, length as m-dash]C stretching vibrations in F6TCNNQ, or similar quinoid systems, for a reliable picture of the nature of GS-CT interactions.
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In order to investigate the effects of intermolecular interactions on the optical properties of organic semiconductors, we employ mixing of the organic semiconductor perfluoropentacene (PFP; C22F14) with the wide band-gap organic semiconductor picene (PIC; C22H14). The binary mixed thin films are prepared by simultaneous coevaporation of PIC and PFP in vacuum. We determine the optical properties of the blends by differential reflectance spectroscopy (absorption) and photoluminescence (emission). PFP:PIC thin films are a rare case of mixed thin films with a known molecular packing. The formation of equimolar mixed domains with a crystal structure clearly different from that of the pure compounds is, in the case of nonequimolar blends, accompanied by pure domains of the excess compound. Due to the wide band gap of PIC, the effect of reduced intermolecular interactions between PFP molecules can be studied in detail without any direct contributions of PIC to the spectra. We find a strongly enhanced emission from PFP in the mixed thin films, which can be explained by decoupling. Real-time investigations of the absorption spectra during growth provide further insight into intermolecular coupling effects on optical properties.
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We present a comprehensive investigation of the charge-transfer (CT) effect in weakly interacting organic semiconductor mixtures. The donor-acceptor pair diindenoperylene (DIP) and N,N'-bis(2-ethylhexyl)-1,7-dicyanoperylene-3,4/9,10-bis(dicarboxyimide) (PDIR-CN2) has been chosen as a model system. A wide range of experimental methods was used in order to characterize the structural, optical, electronic, and device properties of the intermolecular interactions. By detailed analysis, we demonstrate that the partial CT in this weakly interacting mixture does not have a strong effect on the ground state and does not generate a hybrid orbital. We also find a strong CT transition in light absorption as well as in photo- and electroluminescence. By using different layer sequences and compositions, we are able to distinguish electronic coupling in-plane vs out-of-plane and, thus, characterize the anisotropy of the CT state. Finally, we discuss the impact of CT exciton generation on charge-carrier transport and on the efficiency of photovoltaic devices.
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Protein adsorption at the solid-liquid interface is an important phenomenon that often can be observed as a first step in biological processes. Despite its inherent importance, still relatively little is known about the underlying microscopic mechanisms. Here, using multivalent ions, we demonstrate the control of the interactions and the corresponding adsorption of net-negatively charged proteins (bovine serum albumin) at a solid-liquid interface. This is demonstrated by ellipsometry and corroborated by neutron reflectivity and quartz-crystal microbalance experiments. We show that the reentrant condensation observed within the rich bulk phase behavior of the system featuring a nonmonotonic dependence of the second virial coefficient on salt concentration c_{s} is reflected in an intriguing way in the protein adsorption d(c_{s}) at the interface. Our findings are successfully described and understood by a model of ion-activated patchy interactions within the framework of the classical density functional theory. In addition to the general challenge of connecting bulk and interface behavior, our work has implications for, inter alia, nucleation at interfaces.
Asunto(s)
Modelos Químicos , Proteínas/química , Dióxido de Silicio/química , Adsorción , Albúmina Sérica Bovina/químicaRESUMEN
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.
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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.
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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.
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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.
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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%.
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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.
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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.
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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.
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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 (â¼3µm 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.
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We review basic concepts as well as recent examples and applications of organic-organic heterostructures. We organize the different types of heterostructures according to material A deposited on material B (A/B), A co-deposited with B (A:B), heterostructures in the monolayer regime including nanostructuring concepts and systems involving self-assembled monolayers, as well as various other architectures, including superlattices. While most examples are related to small-molecule organic semiconductors, many of the ideas can be applied to other systems. The central theme is growth and structure as well as optical and electronic properties. Finally, we comment on implications for device applications.
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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.
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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.