Article
Chemistry, Multidisciplinary
Waldemar Klauser, Fabian T. von Kleist-Retzow, Sergej Fatikow
Summary: Despite extensive research efforts, understanding the influence of contact line tension during wetting at the nanoscale and its experimental determination remains challenging. The study shows that the contact angle is dependent on drop size for small diameters, and the magnitude of apparent line tension was determined using liquid-metal based measurements inside a scanning electron microscope.
Article
Chemistry, Physical
JingCun Fan, Joel De Coninck, HengAn Wu, FengChao Wang
Summary: This study investigates the capillary force balance at the contact line on rough solid surfaces and in two-liquid systems. It confirms the significant influence of solid-liquid interactions on the lateral component of capillary force and proposes a quantitative relation between surface roughness and transfer strategy. Moreover, the theoretical model includes capillary forces from both liquids in a two-liquid system, and the findings are supported by molecular dynamics simulations.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
(2021)
Article
Chemistry, Physical
Johannes Knorr, Junwei Cui, Thomas M. Koller, Andreas P. Froba
Summary: This study proposes a novel evaluation strategy for surface light scattering (SLS) measurements, which allows for the accurate determination of viscosity and surface tension in the range of small wave vectors. The application of this strategy extends the range of SLS in connection with opaque and non-transparent fluids.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
(2022)
Article
Chemistry, Physical
Marc Durand
Summary: This study provides a new proof that the fluctuations of a membrane are driven by the frame tension, and identifies a miscounting of membrane configurations as the origin of the issue with the standard description.
Article
Physics, Applied
Elham Sharafedini, Hossein Hamzehpour, Mohammad Alidoust
Summary: We have developed a method to study the charge conductivity of a porous system or a metallic matrix alloyed by randomly distributed nonmetallic grains and vacancies. By incorporating Schrodinger's equation and Poisson's equation, we used Monte Carlo sampling to generate random alloys and statistically evaluate the charge conductance. Our parametric study revealed that the charge conductance shows highly nonlinear behavior in the low-voltage regime due to quantum scattering processes, while it remains constant in the high-voltage regime. These findings provide valuable insights for future experiments in designing circuital elements involving random alloy systems.
JOURNAL OF APPLIED PHYSICS
(2023)
Article
Chemistry, Physical
Antonio Malpica-Morales, Peter Yatsyshin, Miguel A. Duran-Olivencia, Serafim Kalliadasis
Summary: This article introduces a statistical learning framework to infer the external potential of a classical many-particle system. The framework combines Bayesian inference and classical density-functional theory, providing a probabilistic description with uncertainty quantification. The accuracy of the method is demonstrated through a case study of a one-dimensional particle ensemble, and its potential applications are highlighted.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Physics, Fluids & Plasmas
M. Maeritz, M. Oettel
Summary: A density functional based on fundamental measure theory is employed to study droplet states of lattice gas with next-neighbor attractions in three-dimensional finite systems. The findings show a sequence of droplets transitioning to cylinders and planar slabs, along with additional effects observed in the chemical potential curve upon temperature decrease. The analysis of the surface tension behavior reveals a dominant 1/Rs2 contribution with a hint of weak logarithmic contribution, which is smaller than the universal value predicted by field-theoretic methods.
Article
Chemistry, Multidisciplinary
Lijie He, Guangming Cheng, Yong Zhu, Harold S. Park
Summary: We use a hybrid diffusion- and nucleation-based kinetic Monte Carlo model to explain the significant influence of adatom diffusion on incipient surface dislocation nucleation in metal nanowires. We discover a stress-regulated diffusion mechanism that promotes the accumulation of diffusing adatoms near nucleation sites, explaining the experimental observations of temperature-dependent nucleation strength but weak strain-rate dependence. Additionally, our model shows that a decreasing rate of adatom diffusion with increasing strain rate leads to stress-controlled nucleation becoming the dominant mechanism at higher strain rates. Overall, our model provides new mechanistic insights into how surface adatom diffusion directly affects the nucleation process and mechanical properties of metal nanowires.
Article
Physics, Particles & Fields
Enrico Bothmann, Andy Buckley, Ilektra A. Christidi, Christian Gutschow, Stefan Hoche, Max Knobbe, Tim Martin, Marek Schonherr
Summary: The poor computing efficiency of precision event generators for LHC physics is a bottleneck for Monte-Carlo event simulations. By focusing on the PDF evaluator and matrix-element generator, we have found solutions to reduce the computing footprint while maintaining the accuracy of the event sample.
EUROPEAN PHYSICAL JOURNAL C
(2022)
Article
Computer Science, Interdisciplinary Applications
Elad Steinberg, Shay Heizler
Summary: This study investigates the multi-dimensional radiative transfer phenomena using the ISMC scheme, including gray and multi-frequency problems. A new implicit scheme based on the semi-analog scheme is introduced and tested, showing the elimination of teleportation errors.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Engineering, Chemical
Leif-Thore Deck, Marco Mazzotti
Summary: The stochastic nature of ice nucleation poses a challenge in the freezing and freeze-drying processes of biopharmaceuticals in vials. This study presents a method to estimate nucleation kinetic parameters and their uncertainty in order to facilitate model-based freezing process design. The methodology accounts for both the inherent stochasticity and variability in nucleation sites among vials, and the extended model demonstrates good agreement with experimental data.
CHEMICAL ENGINEERING SCIENCE
(2023)
Article
Pharmacology & Pharmacy
Leif-Thore Deck, David R. Ochsenbein, Marco Mazzotti
Summary: Freezing and freeze-drying processes are commonly used in pharmaceutical formulations to improve stability. However, batch heterogeneity can cause process failure. In this study, a modeling framework for large-scale freezing processes was developed and an open-source implementation was published. The model couples heat transfer with ice nucleation kinetics and showed how ice nucleation leads to heterogeneity. Various cooling protocols were investigated, and holding schemes were found to have similar solidification times as controlled nucleation, suggesting a potential pathway for process optimization.
INTERNATIONAL JOURNAL OF PHARMACEUTICS
(2022)
Article
Multidisciplinary Sciences
J. Lasave, S. Koval, A. Laio, E. Tosatti
Summary: Research has shown that light doping with KOH at low temperature leads to a transition to ferroelectric ice, but the microscopic mechanism behind this transition still needs further clarification. A lattice model based on dipolar interactions was introduced to explain this phenomenon, highlighting the impact of proton ring configurations on the stability of ice.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Physics, Multidisciplinary
Minghui Hu, Youjin Deng, Jian-Ping Lv
Summary: The concept of logarithmic universality presents a new model for describing critical phenomena, characterized by distinct features from the standard scenario. Monte Carlo simulations on the three-dimensional XY model provide strong evidence for the emergence of logarithmic universality. Discussions on finite-size scaling and critical exponent offer a new perspective on understanding critical universality.
PHYSICAL REVIEW LETTERS
(2021)
Article
Chemistry, Physical
Z. Schaetzle, P. B. Szabo, M. Mezera, J. Hermann, F. Noe
Summary: Computing accurate and efficient approximations to solve the Schrödinger equation in computational chemistry has been a challenge for decades. Quantum Monte Carlo methods, with their highly parallel and scalable algorithm, show promise in achieving high accuracy in a variety of molecular systems. The use of machine-learned parametrizations, relying on neural networks as universal function approximators, has further improved the accuracy of these methods. The development of software libraries like DEEPQMC aims to provide a common framework for future investigations and make this field accessible to practitioners from both the quantum chemistry and machine learning communities.
JOURNAL OF CHEMICAL PHYSICS
(2023)