arXiv subjects

Frank Schreiber

Publications and source records attributed to Frank Schreiber.

At least 19 recordsLinked to original sources

arXiv2026

Nanoscale Protein Diffusion in Supercooled Cryoprotectant Solutions

Vitrification during cryopreservation requires a quantitative understanding of protein transport in deeply supercooled cryoprotectant solutions, yet direct measurements at molecular length scales remain scarce. Here, we combine X-ray Photon Correlation Spectroscopy (XPCS) and small-angle X-ray scattering (SAXS) to investigate ferritin diffusion in glycerol-water mixtures from ambient conditions down to 210 K. The measured diffusion coefficients reveal that ferritin retains a higher mobility upon cooling than expected from hydrodynamic scaling based on measurements of larger silica reference tracers, with the difference emerging below approximately 230 K. A minimal fluctuating-friction model reproduces the observed relative enhancement in diffusion, illustrating how local variations in the effective friction can give rise to such behavior. These measurements provide direct experimental benchmarks for future theoretical and simulation studies aimed at understanding molecular transport in deeply supercooled liquids approaching the glass transition.

cond-mat.soft↗

arXiv2026

Conditions for Bacterial Selection and Extinction Driven by Growth-Kill Trade-Off in Cyclic Antimicrobial Treatments

Antimicrobial protocols - using substances such as antibiotics or disinfectants - remain the preferred option for preventing the spread of pathogenic bacteria. However, bacteria can develop mechanisms to reduce their antimicrobial susceptibility, which can lead to treatment failure and the selection of resistance or tolerance. In this work, we propose a minimal population dynamics model to study bacterial selection during cyclic antimicrobial application, a commonly used protocol. Selection in bacterial populations with heterogeneous antimicrobial susceptibility is modelled here as a trade-off between survival advantage (reduction in antimicrobial killing) and potential fitness costs (reduction in growth rate) of the less susceptible strains. The proposed model allows us to derive useful expressions for determining the success of cyclic antimicrobial treatments based on two bacterial traits: growth and kill rates. The results obtained here are directly applicable to preventing the selection and spread of resistant and tolerant bacterial strains in real-life protocols.

q-bio.PE↗

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↗

arXiv2025

Coherent X-rays reveal anomalous molecular diffusion and cage effects in crowded protein solutions

Understanding protein motion within the cell is crucial for predicting reaction rates and macromolecular transport in the cytoplasm. A key question is how crowded environments affect protein dynamics through hydrodynamic and direct interactions at molecular length scales. Using megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) at the European X-ray Free Electron Laser (EuXFEL), we investigate ferritin diffusion at microsecond time scales. Our results reveal anomalous diffusion, indicated by the non-exponential decay of the intensity autocorrelation function $g_2(q,t)$ at high concentrations. This behavior is consistent with the presence of cage-trapping in between the short- and long-time protein diffusion regimes. Modeling with the $δγ$-theory of hydrodynamically interacting colloidal spheres successfully reproduces the experimental data by including a scaling factor linked to the protein direct interactions. These findings offer new insights into the complex molecular motion in crowded protein solutions, with potential applications for optimizing ferritin-based drug delivery, where protein diffusion is the rate-limiting step.

cond-mat.soft↗

arXiv2025

(Dis-)appearance of liquid-liquid phase transitions in a heterogeneous activated patchy particle model and experiment

The ion-activated patchy particle model is an important theoretical framework to investigate the phase behaviour of globular proteins in the presence of multivalent ions. In this work, we study and highlight the influence of patch heterogeneity on the extension, appearance and disappearance of the liquid-liquid coexistence region of the phase diagram. We demonstrate that within this model the binding energy between salt ions and patches of different type is a key factor in determining the phase behavior. Specifically, we show under which conditions liquid-liquid phase separation (LLPS) in these systems can appear or disappear for varying binding energy and ion-mediated attraction energy between ion-occupied and unoccupied patches. In particular we address the influence of the patch type dependence of these energies on the (dis)appearance of LLPS. These results rationalize our new results on ion-dependent liquid-liquid phase separation in solutions of bovine serum albumine with trivalent cations. In comparison with models with non-activated patches, where the gas-liquid transition disappears when the number of patches approaches two, we find the complementary mechanism that ions may shift the attractions from stronger to weaker patches (with an accompanying disappearance of the transition), if their binding energy to the patches changes. The results have implications for the understanding of charge-driven LLPS in biological systems and its suppression.

cond-mat.soft↗

arXiv2025

Depletion-Induced Interactions Modulate Nanoscale Protein Diffusion in Polymeric Crowder Solutions

Macromolecular crowding plays a crucial role in modulating protein dynamics in cellular and in vitro environments. Polymeric crowders such as dextran and Ficoll are known to induce entropic forces, including depletion interactions, that promote structural organization, but the nanoscale consequences for protein dynamics remain less well understood. Here, we employ megahertz X-ray photon correlation spectroscopy (MHz-XPCS) at the European XFEL to probe the dynamics of the protein ferritin in solutions containing sucrose, dextran, and Ficoll. We find that depletion-driven short-range attractions combined with long-range repulsions give rise to intermediate-range order (IRO) once the polysaccharide overlap concentration $c^*$ is exceeded. These IRO features fluctuate on microsecond to millisecond timescales, strongly modulating the collective dynamics of ferritin. The magnitude of these effects depends sensitively on crowder type, concentration, and molecular weight. Normalizing the crowder concentration by $c^*$ reveals scaling behavior in ferritin self-diffusion with a crossover near 2$c^*$, marking a transition from depletion-enhanced mobility to viscosity-dominated slowing. Our results demonstrate that bulk properties alone cannot account for protein dynamics in crowded solutions, underscoring the need to include polymer-specific interactions and depletion theory in models of crowded environments.

cond-mat.soft↗

arXiv2025

A pipeline for Megahertz X-ray Photon Correlation Spectroscopy on soft matter samples at the MID instrument of European XFEL

In this paper we present the experimental protocol and data processing framework for Megahertz X-ray Photon Correlation Spectroscopy (MHz-XPCS) experiments on soft matter samples, implemented at the Materials Imaging and Dynamics (MID) instrument of the European X-ray Free-Electron Laser (EuXFEL). Due to the introduction of a standard configuration and the implementation of a highly automated data processing pipeline, MHz-XPCS measurements can now be conducted and analyzed with minimal user intervention. A key challenge lies in managing the extremely large data volumes generated by the Adaptive Gain Integrating Pixel Detector (AGIPD) - often reaching several petabytes within a single experiment. We describe the technical implementation, discuss the hardware requirements related to effective parallel data processing, and propose strategies to enhance data quality, in particular related to data reduction strategies and an improvement of the signal-to-noise ratio. Finally, we address strategies for making the processed data FAIR (Findable, Accessible, Interoperable, Reusable), in alignment with the goals of the DAPHNE4NFDI project.

physics.ins-det↗

arXiv2025

Softness and Hydrodynamic Interactions Regulate Lipoprotein Transport in Crowded Yolk Environments

Low-density lipoproteins (LDLs) serve as nutrient reservoirs in egg yolk for embryonic development and as promising drug carriers. Both roles critically depend on their mobility in densely crowded biological environments. Under these crowded conditions, diffusion is hindered by transient confinement within dynamic cages formed by neighboring particles, driven by solvent-mediated hydrodynamic interactions and memory effects -- phenomena that have remained challenging to characterize computationally and experimentally. Here, we employ megahertz X-ray photon correlation spectroscopy to directly probe the cage dynamics of LDLs in yolk-plasma across various concentrations. We find that LDLs undergo anomalous diffusion, experiencing $\approx$ 100-fold reduction in self-diffusion at high concentrations compared to dilute solutions. This drastic slowing-down is attributed to a combination of hydrodynamic interactions, direct particle-particle interactions, and the inherent softness of LDL particles. Despite reduced dynamics, yolk-plasma remains as a liquid, yet sluggish, balancing dense packing, structural stability, and fluidity essential for controlled lipid release during embryogenesis.

cond-mat.soft↗

arXiv2024

An alternative approach to the osmotic second virial coefficient of protein solutions and its application to liquid liquid phase separation

The osmotic second virial coefficient B2 is an important parameter to describe the interactions and phase behavior of protein solutions, including colloidal systems and macromolecular solutions. Another key parameter to describe the driving force of the nucleation of a new phase is the supersaturation, which is used in the classical nucleation theory framework and is connected with the favorable contribution in the Gibbs free energy in the bulk solution. In this article, we establish a connection between B2 calculated from small angle Xray scattering (SAXS) data and the values of B2 obtained from supersaturation measurements using thermodynamics considerations. The values of the second virial coefficient calculated employing this method agree with those determined via SAXS in the region near the liquid liquid phase separation border for human serum albumin and bovine serum albumin. The general relations adopted are shown to be useful for the estimation of the second virial coefficient B2 for globular proteins, in the proximity of the binodal biphasic coexistent region.

cond-mat.soft↗

arXiv2024

The ion activated attractive patchy particles model and its application to the liquid-vapour phase transitions

Patchy particles are an intriguing subject of study and indeed a model system in the field of soft matter physics. In recent years, patchy particle models have been applied to describe a wide variety of systems, including colloidal crystals, macromolecular interactions, liquid crystals, and nanoparticle assemblies. Given the importance of the topic, rationalizing and capturing the basic features of these models is crucial to their correct application in specific systems. In this study, we extend the ion-activated attractive patchy particles model previously employed to elucidate the phase behavior of protein solutions in the presence of trivalent salts. Our extension incorporates the effect of repulsion between unoccupied and occupied binding sites, depicted as patches. Furthermore, we examine the influence of model parameters on the liquid-vapor coexistence region within the phase diagram, employing numerical methods. A deeper understanding of this model will facilitate a better comprehension of the effects observed in experiments.

cond-mat.soft↗

arXiv2024

Sub-meV Linewidths in Polarized Low-Temperature Photoluminescence of 2D PbS Nanoplatelets

Colloidal semiconductor nanocrystals are promising materials for classical and quantum light sources due to their versatile chemistry and efficient photoluminescence (PL) properties. While visible emitters are well-established, the pursuit of excellent (near-)infrared sources continues. One notable candidate in this regard are photoluminescent two-dimensional (2D) PbS nanoplatelets (NPLs) exhibiting excitonic emission at 720 nm (1.7 eV) directly tying to the typical emission range limit of CdSe NPLs. Here, we present the first comprehensive analysis of low-temperature PL from this material class. Ultrathin 2D PbS NPLs exhibit high crystallinity confirmed by scanning transmission electron microscopy, and revealing Moire patterns in overlapping structures. At 4K, we observe unique PL features in single PbS NPLs, including narrow zero-phonon lines with line widths down to 0.6 meV and a linear degree of polarization up to 90%. Time-resolved measurements identify trions as the dominant emission source with a 2.3 ns decay time. Sub-meV spectral diffusion and no immanent blinking over minutes is observed, as well as discrete spectral jumps without memory effects. These findings advance the understanding and underpin the potential of colloidal PbS NPLs for optical and quantum technologies.

physics.optics↗

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

On the analysis of two-time correlation functions: equilibrium vs non-equilibrium systems

X-ray photon correlation spectroscopy (XPCS) is a powerful tool for the investigation of dynamics covering a broad range of time and length scales. The two-time correlation function (TTC) is commonly used to track non-equilibrium dynamical evolution in XPCS measurements, followed by the extraction of one-time correlations. While the theoretical foundation for the quantitative analysis of TTCs is primarily established for equilibrium systems, where key parameters such as diffusion remain constant, non-equilibrium systems pose a unique challenge. In such systems, different projections ("cuts") of the TTC may lead to divergent results if the underlying fundamental parameters themselves are subject to temporal variations. This article explores widely used approaches for TTC calculations and common methods for extracting relevant information from correlation functions on case studies, particularly in the light of comparing dynamics in equilibrium and non-equilibrium systems.

cond-mat.soft↗

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

Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation

Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe and Matter with emphasis on the digital transformation. A portfolio of measures is described in detail and then summarized according to the timescales required for their implementation. The measures will both contribute to sustainable research and accelerate scientific progress through increased awareness of resource usage. This work is based on a three-days workshop on sustainability in digital transformation held in May 2023.

physics.comp-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↗