arXiv subjects

Alexander Hinderhofer

Publications and source records attributed to Alexander Hinderhofer.

15 recordsLinked to original sources

arXiv2026

Phase behavior and electrical transport in DBTTF-HATCN donor-acceptor mixtures

The formation of donor-acceptor complexes (DACs) between the electron donor Dibenzotetrathiafulvalene (DBTTF) and the acceptor Hexaaza\-triphenylene\-hexacarbo\-nitrile (HATCN) results in a new phase with a distinctly different crystal structure as well as new optical absorption bands below the energy gaps of the two pristine materials. X-ray scattering and atomic force microscopy provide detailed insights into the film structure and morphology by systematic variation of the mixing ratio from pristine DBTTF to pristine HATCN. The measured electrical conductivity of thin films depends in a highly non-monotonic manner on the composition of the mixture and shows significantly improved charge transport compared to the pristine films. The temperature-dependent conductivity, charge carrier concentration, and mobility were investigated across these compositions. Surprisingly, all compositions exhibited n-type behavior, except for pristine DBTTF. This behavior is explained by the electronic structure of the mixtures, as revealed by ultraviolet photoelectron spectroscopy, which indicates that charge injection and transport occur via the lowest unoccupied molecular orbital of the DAC and HATCN. Additionally, the observed electrical conductivity is strongly influenced by morphology and structural ordering of the films. These findings offer valuable insights for the design of advanced materials with enhanced electrical performance.

cond-mat.mtrl-sci↗

arXiv2026

Non-monotonic roughness evolution in film growth on weakly interacting substrates

Thin film deposition on weakly interacting substrates exhibits a unique growth mode characterized by initially strong island formation and rapidly increasing roughness, which reaches a maximum and subsequently decreases as the film returns to a smooth morphology. Here we show this rough-to-smooth growth mode experimentally for two molecular systems with substantially different geometries, namely, the effectively spherical buckminsterfullerene (C$_{60}$) and the disk-like 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN). This growth mode is explained by a geometrical model that captures the basic mechanisms of multilayer island growth, island coalescence, and formation of a continuous film. Additionally, kinetic Monte Carlo simulations with minimal ingredients demonstrate that this mode generally occurs for weakly interacting substrates, providing quantitative estimates of parameters that characterize adsorbate-adsorbate and adsorbate-substrate interactions. Both the model and simulations accurately describe the experimental data and highlight the generic nature of the phenomenon, independently of the details of the interactions and the molecular flux, which opens up a path for controlling nanoscale film roughness.

cond-mat.mtrl-sci↗

arXiv2024

Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation

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, setting new standards in reflectometry. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel 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 co-refinement of several experimental data sets, and can be adapted to provide fast, reliable, and flexible inference across many other inverse problems.

physics.app-ph↗

arXiv2024

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 were 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. In this work, we investigate 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 we identify as FAI-PbI2-xDMSO. The intermediate phase templates the crystallisation of the perovskite layer, resulting in highly oriented perovskite layers. The formation of this DMSO containing intermediate is triggered by the selective removal of DMF when alcohols are used as an antisolvent, consequently leading to differing degrees of orientation depending on the antisolvent properties. Finally, we demonstrate 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.

physics.app-ph↗

arXiv2023

Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem

Due 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 so-called phase problem poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this, we present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. By leveraging the input of prior knowledge, we can improve the training dynamics and address the underdetermined ("ill-posed") nature of the problem. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem, working properly even for a 5-layer multilayer model and an N-layer periodic multilayer model with up to 17 open parameters.

eess.SP↗

arXiv2023

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 X-ray scattering experiments, which proves to be a valuable tool for enhancing research that involves large or rapidly generated datasets. ML allows for the automated interpretation of experimental results, particularly those obtained from synchrotron or neutron facilities. The speed at which ML models can process data presents an important opportunity to establish a closed-loop feedback system, enabling real-time decision-making based on online data analysis. In this study, we describe the incorporation of ML into 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. We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment. Our data demonstrates the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup.

physics.data-an↗

arXiv2022

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

This work demonstrates the Python package mlreflect which implements an optimized pipeline for the automized 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. It is shown that for a large dataset 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. Furthermore, 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. Furthermore, the extremely fast prediction times of the neural network are leveraged to compensate for systematic errors by sampling slight variations of the data.

physics.data-an↗

arXiv2022

Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data

Understanding the processes of perovskite crystallization is essential for improving the properties of organic solar cells. In situ real-time grazing-incidence X-ray diffraction (GIXD) is a key technique for this task, but it produces large amounts of data, frequently exceeding the capabilities of traditional data processing methods. We propose an automated pipeline for the analysis of GIXD images, based on the Faster R-CNN deep learning architecture for object detection, modified to conform to the specifics of the scattering data. The model exhibits high accuracy in detecting diffraction features on noisy patterns with various experimental artifacts. We demonstrate our method on real-time tracking of organic-inorganic perovskite structure crystallization and test it on two applications: 1. the automated phase identification and unit-cell determination of two coexisting phases of Ruddlesden-Popper 2D perovskites, and 2. the fast tracking of MAPbI$_3$ perovskite formation. By design, our approach is equally suitable for other crystalline thin-film materials.

cs.CV↗

arXiv2022

Preserving the Stoichiometry of Triple-Cation Perovskites by Carrier-Gas-Free Antisolvent Spraying

The use of antisolvents during the fabrication of solution-processed lead halide perovskite layers is increasingly common. Usually, the antisolvent is applied by pipetting during the spin-coating process, which often irreversibly alters the composition of the perovskite layer, resulting in the formation of PbI2 at the surface and bulk of the perovskite layer. Here, we demonstrate that by applying the antisolvent via carrier-gas free spraying, the stoichiometry of the perovskite layer is far better preserved. Consequently, the photovoltaic performance of triple cation photovoltaic devices fabricated in an inverted architecture is enhanced, mainly due to an increase in the open-circuit voltage. By exploring different volumes of antisolvent, we show that spraying as little as 60 uL results in devices with power conversion efficiencies as high as 21%. Moreover, solar cells with sprayed antisolvent are more stable than those fabricated by pipetting the antisolvent.

cond-mat.mtrl-sci↗

arXiv2021

Non-equilibrium Roughness Evolution of Small Molecule Mixed Films Reflecting Equilibrium Phase Behavior

Understanding non-equilibrium phenomena, such as growth, and connecting them to equilibrium phase behavior is a major challenge, in particular for complex multicomponent materials. We use X-ray reflectivity to determine the surface roughness of binary mixtures of several prototypical organic compounds. By analyzing the roughness as a function of composition, we find a systematic behavior depending on the bulk phase behavior in terms of intermixing, co-crystallization or phase separation. Supported by kinetic Monte Carlo simulations, we provide evidence that the growth behavior can be rationalized by a lowered step edge barrier in the mixed films which is induced by reduced in-plane crystallinity.

physics.app-ph↗

arXiv2021

Thin Film Growth of Phase-Separating Phthalocyanine-Fullerene Blends: A Combined Experimental and Computational Study

Blended organic thin films have been studied during the last decades due to their applicability in organic solar cells. Although their optical and electronic features have been examined intensively, there is still lack of detailed knowledge about their growth processes and resulting morphologies, which play a key role for the efficiency of optoelectronic devices such as organic solar cells. In this study, pure and blended thin films of copper phthalocyanine (CuPc) and the Buckminster fullerene (C60) were grown by vacuum deposition onto a native silicon oxide substrate at two different substrate temperatures, 310 K and 400 K. The evolution of roughness was followed by in-situ real-time X-ray reflectivity. Crystal orientation, island densities and morphology were examined after the growth by X-ray diffraction experiments and microscopy techniques. The formation of a smooth wetting layer followed by rapid roughening was found in pure CuPc thin films, whereas C60 shows a fast formation of distinct islands at a very early stage of growth. The growth of needle-like CuPc crystals loosing their alignment with the substrate was identified in co-deposited thin films. Furthermore, the data demonstrates that structural features become larger and more pronounced and that the island density decreases by a factor of four when going from 310 K to 400 K. Finally, the key parameters roughness and island density were well reproduced on a smaller scale by kinetic Monte-Carlo simulations of a generic, binary lattice model with simple nearest-neighbor interaction energies.

cond-mat.mtrl-sci↗

arXiv2021

Lattice gas study of thin film growth scenarios and transitions between them: Role of substrate

Thin film growth is investigated in two types of lattice gas models where substrate and film particles are different, expressed by unequal interaction energy parameters. The first is of solid-on-solid type, whereas the second type incorporates desorption, diffusion in the gas phase above the film and re-adsorption at the film (appropriate for growth in colloidal systems). The difference between particle-substrate and particle-particle interactions plays a central role for the evolution of the film morphology at intermediate times. The models exhibit a dynamic layering transition which occurs at generally lower substrate attraction strengths than the equilibrium layering transition. A second, flattening transition is found where initial island growth transforms to layer-by-layer growth at intermediate deposition times. Combined with the known roughening behavior in such models for very large deposition times, we present four global growth scenarios, charting out the possible types of roughness evolution.

cond-mat.soft↗

arXiv2019

Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks

X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. In this study, we show how a simple artificial neural network model can be used to predict the thickness, roughness and density of thin films of different organic semiconductors (diindenoperylene, copper(II) phthalocyanine and $α$-sexithiophene) on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental dataset of 372 XRR curves, we show that a simple fully connected model can already provide good predictions with a mean absolute percentage error of 8-18 % when compared to the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.

cond-mat.soft↗

arXiv2017

Multivalent Ion-Activated Protein Adsorption Reflecting Bulk Reentrant Behavior

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 cofficient 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 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.

physics.bio-ph↗

arXiv2007

Optical Properties of Pentacene and Perfluoropentacene Thin Films

The optical properties of pentacene (PEN) and perfluoropentacene(PFP) thin films on various SiO2 substrates were studied using variable angle spectroscopic ellipsometry. Structural characterization was performed using X-ray reflectivity and atomic force microscopy. A uniaxial model with the optic axis normal to the sample surface was used to analyze the ellipsometry data. A Strong optical anisotropy was observed and enabled the direction of the transition dipole of the absorption bands to be determined. Furthermore, comparison of the optical constants of PEN and PFP thin films with the absorption spectra of the monomers in solution shows significant changes due to the crystalline environment. Relative to the monomer spectrum the HOMO-LUMO transition observed in PEN (PFP) thin film is reduced by 210 meV (280 meV). Surprisingly, a second absorption band in the PFP thin film shows a slight blueshift (40 meV) compared to the spectrum of the monomer with its transition dipole perpendicular to that of the first absorption band.

cond-mat.soft↗