arXiv subjects

Frank Schreiber

Publications and source records attributed to Frank Schreiber.

At least 37 records · Page 2Linked to original sources

arXiv2023

Transiently delocalized states enhance hole mobility in organic molecular semiconductors

There is compelling evidence that charge carriers in organic semiconductors (OSs) self-localize in nano-scale space because of dynamic disorder. Yet, some OSs, in particular recently emerged high-mobility organic molecular crystals, feature reduced mobility at increasing temperature, a hallmark for delocalized band transport. Here we present the temperature-dependent mobility in two record-mobility OSs: DNTT (dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]-thiophene), and its alkylated derivative, C8-DNTT-C8. By combining terahertz photoconductivity measurements with fully atomistic non-adiabatic molecular dynamics simulations, we show that while both crystals display a power-law decrease of the mobility (μ) with temperature (T, following: μ\propto T^(-n)), the exponent n differs substantially. Modelling provides n values in good agreement with experiments and reveals that the differences in the falloff parameter between the two chemically closely related semiconductors can be traced to the delocalization of the different states thermally accessible by charge carriers, which in turn depends on the specific electronic band structure of the two systems. The emerging picture is that of holes surfing on a dynamic manifold of vibrationally-dressed extended states with a temperature-dependent mobility that provides a sensitive fingerprint for the underlying density of states.

cond-mat.soft↗

arXiv2022

Resolving molecular diffusion and aggregation of antibody proteins with megahertz X-ray free-electron laser pulses

X-ray free-electron lasers (XFELs) with megahertz repetition rate can provide novel insights into structural dynamics of biological macromolecule solutions. However, very high dose rates can lead to beam-induced dynamics and structural changes due to radiation damage. Here, we probe the dynamics of dense antibody protein (Ig-PEG) solutions using megahertz X-ray photon correlation spectroscopy (MHz-XPCS) at the European XFEL. By varying the total dose and dose rate, we identify a regime for measuring the motion of proteins in their first coordination shell, quantify XFEL-induced effects such as driven motion, and map out the extent of agglomeration dynamics. The results indicate that for average dose rates below $1.06\,\mathrm{kGy}\mathrm{μs}^{-1}$ in a time window up to $10\,\mathrm{μs}$, it is possible to capture the protein dynamics before the onset of beam induced aggregation. We refer to this approach as correlation before aggregation and demonstrate that MHz-XPCS bridges an important spatio-temporal gap in measurement techniques for biological samples.

physics.bio-ph↗

arXiv2022

The onset of molecule-spanning dynamics in a multi-domain protein

Protein dynamics has been investigated on a wide range of time scales. Nano- and picosecond dynamics have been assigned to local fluctuations, while slower dynamics have been attributed to larger conformational changes. However, it is largely unknown how local fluctuations can lead to global allosteric changes. Here we show that molecule-spanning dynamics on the 100 ns time scale precede larger allosteric changes. We assign global real-space movements to dynamic modes on the 100 ns time scales, which became possible by a combination of single-molecule fluorescence, quasi-elastic neutron scattering and all-atom MD simulations. Additionally, we demonstrate the effect of Sba1, a co-chaperone of Hsp90, on these molecule-spanning dynamics, which implies functional importance of such dynamics. Our integrative approach provides comprehensive insights into molecule-spanning dynamics on the nanosecond time scale for a multi-domain protein and indicates that such dynamics are the molecular basis for allostery and large conformational changes in proteins.

q-bio.BM↗

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

Role of Entropy in Determining the Phase Behavior of Protein Solutions Induced by Multivalent Ions

Recent experiments have reported lower critical solution temperature (LCST) phase behavior of aqueous solutions of proteins induced by multivalent ions, where the solution phase separates upon heating. This phenomenon is linked to complex hydration effects that result in a net entropy gain upon phase separation. To decipher the underlying molecular mechanism, we use all-atom molecular dynamics simulations along with the two-phase thermodynamic method for entropy calculation. Based on simulations of a single BSA protein in various salt solutions (NaCl, CaCl_2, MgCl_2, and YCl_3) at temperatures (T) ranging 283-323 K, we find that the cation-protein binding affinity increases with T, reflecting its thermodynamic driving force to be entropic in origin. We show that in the cation binding process, many tightly bound water molecules from the solvation shells of a cation and the protein are released to the bulk, resulting in entropy gain. To rationalize the LCST behavior, we calculate the ζ-potential that shows charge inversion of the protein for solutions containing multivalent ions. The ζ-potential increases with T. Performing simulations of two BSA proteins, we demonstrate that the protein-protein binding is mediated by multiple cation bridges and involves similar dehydration effects that cause a large entropy gain which more than compensates for rotational and translational entropy losses of the proteins. Thus, the LCST behavior is entropy-driven, but the associated solvation effects are markedly different from hydrophobic hydration. Our findings have direct implications for tuning the phase behavior of biological and soft-matter systems, e.g., protein condensation and crystallization.

cond-mat.soft↗

arXiv2021

Spatially resolved fluorescence of caesium lead halide perovskite supercrystals reveals quasi-atomic behavior of nanocrystals

We correlate spatially resolved fluorescence (-lifetime) measurements with X-ray nanodiffraction to reveal surface defects in supercrystals of self-assembled caesium lead halide perovskite nanocrystals and study their effect on the fluorescence properties. Upon comparison with density functional modelling, we show that a loss in structural coherence, an increasing atomic misalignment between adjacent nanocrystals, and growing compressive strain near the surface of the supercrystal are responsible for the observed fluorescence blueshift and decreased fluorescence lifetimes. Such surface defect-related optical properties extend the frequently assumed analogy between atoms and nanocrystals as so-called quasi-atoms. Our results emphasize the importance of minimizing strain during the self-assembly of perovskite nanocrystals into supercrystals for lighting application such as superfluorescent emitters.

cond-mat.mtrl-sci↗

arXiv2021

Interplay between Kinetics and Dynamics of Liquid-Liquid Phase Separation in a Protein Solution Revealed by Coherent X-ray Spectroscopy

Microscopic dynamics of complex fluids in the early stage of spinodal decomposition (SD) is strongly intertwined with the kinetics of structural evolution, which makes a quantitative characterization challenging. In this work, we use x-ray photon correlation spectroscopy to study the dynamics and kinetics of a protein solution undergoing liquid-liquid phase separation (LLPS). We demonstrate that in the early stage of SD, the structural relaxation kinetics is up to 40 times slower than the dynamics and thus can be decoupled. The kinetic decay rate is inversely proportional to time in the early stage, followed by a nearly constant behavior during the coarsening stage. The microscopic dynamics can be well described by hyper-diffusive ballistic motions with a relaxation time exponentially growing with time in the early stage followed by a power-law increase with fluctuations. These experimental results are further supported by simulations based on the Cahn-Hilliard equation. The established framework is applicable to other condensed matter and biological systems undergoing phase transitions and may also inspire further theoretical work.

cond-mat.soft↗

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↗

arXiv2020

A Neutron Scattering Perspective on the Structure, Softness and Dynamics of the Ligand Shell of PbS Nanocrystals in Solution

Small-angle neutron and X-ray scattering, neutron backscattering and neutron time-of-flight spectroscopy are applied to reveal the structure of the ligand shell, the temperature-dependent diffusion properties and the phonon spectrum of PbS nanocrystals functionalized with oleic acid in deuterated hexane. The nanocrystals decorated with oleic acid as well as the desorbed ligand molecules exhibit simple Brownian diffusion with a Stokes-Einstein temperature-dependence and inhibited freezing. Ligand molecules desorbed from the surface show strong spatial confinement. The phonon spectrum of oleic acid adsorbed to the nanocrystal surface exhibits hybrid modes with a predominant Pb-character. Low-energy surface modes of the NCs are prominent and indicate a large mechanical softness in solution. This work provides comprehensive insights into the ligand-particle interaction of colloidal nanocrystals in solution and highlights its effect on the diffusion and vibrational properties as well as their mechanical softness.

cond-mat.mtrl-sci↗

arXiv2020

Binding and Electronic Level Alignment of $\boldsymbolπ$-Conjugated Systems on Metals

We review the binding and energy level alignment of $π$-conjugated systems on metals, a field which during the last two decades has seen tremendous progress both in terms of experimental characterization as well as in the depth of theoretical understanding. Precise measurements of vertical adsorption distances and the electronic structure together with \textit{ab-initio} calculations have shown that most of the molecular systems have to be considered as intermediate cases between weak physisorption and strong chemisorption. In this regime, the subtle interplay of different effects such as covalent bonding, charge transfer, electrostatic and van der Waals interactions yields a complex situation with different adsorption mechanisms. In order to establish a better understanding of the binding and the electronic level alignment of $π$-conjugated molecules on metals, we provide an up-to-date overview of the literature, explain the fundamental concepts as well as the experimental techniques and discuss typical case studies. Thereby, we relate the geometric with the electronic structure in a consistent picture and over the entire range from weak to strong coupling.

cond-mat.mtrl-sci↗

arXiv2020

Structure-transport correlation reveals anisotropic charge transport in coupled PbS nanocrystal superlattices

Semiconductive nanocrystals (NCs) can be self-assembled into ordered superlattices (SLs) to create artificial solids with emerging collective properties. Computational studies have predicted that properties such as electronic coupling or charge transport are determined not only by the individual NCs but also by the degree of their organization and structure. However, experimental proof for a correlation between structure and charge transport in NC SLs is still pending. Here, we perform X-ray nano-diffraction and apply Angular X-ray Cross-Correlation Analysis (AXCCA) to characterize the structures of coupled PbS NC SLs, which are directly correlated with the electronic properties of the same SL microdomains. We find strong evidence for the effect of SL crystallinity on charge transport and reveal anisotropic charge transport in highly ordered monocrystalline hexagonal close-packed PbS NC SLs, caused by the dominant effect of shortest interparticle distance. This implies that transport anisotropy should be a general feature of weakly coupled NC SLs.

cond-mat.mtrl-sci↗

arXiv2020

Structural order matters: Enhanced electronic coupling in self assembled micro-crystals of Au-nanoclusters

We report an easy and broadly applicable method for the controlled self-assembly of atomically precise Au32(nBu3P)12Cl8 nanoclusters into micro-crystals. This enables the determination of emergent optoelectronic properties resulting from long-range order in such assemblies. Compared to the same nanoclusters in glassy, polycrystalline ensembles, we find a 100-fold increase in the electric conductivity and charge carrier mobility as well as additional optical transitions. We show that these effects are due to a vanishing energetic disorder and a drastically reduced activation energy to charge transport in the highly ordered assemblies. This first structure-transport correlation on self-assembled superstructures of atomically precise gold nanoclusters paves the way towards functional materials with novel collective optoelectronic properties.

cond-mat.mes-hall↗

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↗

arXiv2019

Angular x-ray cross-correlation analysis (AXCCA): Basic concepts and recent applications to soft matter and nanomaterials

Angular x-ray cross-correlation analysis (AXCCA) is a technique which allows quantitative measurement of the angular anisotropy of x-ray diffraction patterns and provides insights into the orientational order in the system under investigation. This method is based on the evaluation of the angular cross-correlation function of the scattered intensity distribution on a two-dimensional (2D) detector and further averaging over many diffraction patterns for enhancement of the anisotropic signal. Over the last decade, AXCCA was successfully used to study the anisotropy in various soft matter systems, such as solutions of anisometric particles, liquid crystals, colloidal crystals, superlattices composed by nanoparticles, etc. This review provides an introduction to the technique and gives a survey of the recent experimental work in which AXCCA in combination with micro- or nanofocused x-ray microscopy was used to study the orientational order in various soft matter systems.

cond-mat.soft↗