Review
Chemistry, Multidisciplinary
Angel Morales-Garcia, Francesc Vines, Jose R. B. Gomes, Francesc Illas
Summary: Theoretical and computational studies have significantly contributed to the understanding of heterogeneous catalysis, providing predictive insights and assisting in the rationalization of experimental observations and catalyst design. Key aspects include modeling complex systems accurately, exploring potential energy landscapes, and bridging the gap between atomistic insight and experimental data through kinetic modeling and simulations. The power of computer simulations in heterogeneous catalysis is demonstrated by comparing computational information with experimental results, particularly in CO2 conversion catalysis. Future challenges and prospects for computational heterogeneous catalysis are also discussed.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2021)
Article
Chemistry, Physical
Tian-Tian Xiao, Ru-Yi Li, Gui-Chang Wang
Summary: The role of stabilized Cu+ active sites in propylene epoxidation on Ti2CuO6/Cu(1 1 1) and Cu2O (1 1 1) surfaces was investigated using systematic kinetic Monte Carlo (kMC) studies. The simulation showed that Ti2CuO6/Cu(1 1 1) exhibited better selectivity and activity than Cu2O(1 1 1) due to its active open-shell electronic structure. Understanding the factors influencing the catalytic performance can aid in the design of efficient propylene epoxidation catalysts.
APPLIED SURFACE SCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Srikanth Ravipati, Giannis D. Savva, Ilektra-Athanasia Christidi, Roland Guichard, Jens Nielsen, Romain Reocreux, Michail Stamatakis
Summary: Despite the widespread adoption of kinetic Monte Carlo simulations in surface science and heterogeneous catalysis, the sequential nature of the framework limits the accessible length scales. By coupling the Time-Warp algorithm with the Graph-Theoretical KMC framework and implementing it in Zacros, researchers have enabled distributed computing capabilities in KMC simulations. This advancement opens up opportunities for detailed meso-scale studies of heterogeneous catalysts and closer comparisons of theory with experiments.
COMPUTER PHYSICS COMMUNICATIONS
(2022)
Article
Chemistry, Multidisciplinary
Alexandre S. Avaro, Juan G. Santiago
Summary: This article presents a quantification of the uncertainty in the experimental determination of kinetic rate parameters for enzymatic reactions. The authors examine several sources of uncertainty and bias and compute typical uncertainties of kcat, KM, and catalytic efficiency. The extraction of these parameters for CRISPR-Cas systems is analyzed as a salient example. Reports of enzymatic kinetic rates for CRISPR diagnostics have been highly unreliable and inconsistent.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Materials Science, Multidisciplinary
Feifei Yu, Jianqiao Yu, Yunping Jia, Huahai Shen, Xia Xiang, Xiaotao Zu, Shuanglin Hu
Summary: The migration paths, barriers, and prefactors of tritium and helium in titanium tritide are calculated by density functional theory calculations, and further applied in kinetic Monte Carlo simulations to determine the overall diffusion rates. The diffusion coefficients of helium with temperature in different titanium tritides are shown as Arrhenius plots. The results indicate that there are two diffusional regimes, migration more via tetrahedral vacancies or octahedral interstitials, depending on temperature and composition. Tritium concentration, temperature, and axial strain can effectively tune the diffusion mechanism and rate of helium atom in titanium tritides.
MATERIALS TODAY COMMUNICATIONS
(2023)
Article
Materials Science, Multidisciplinary
R. Martinho Vieira, O. Eriksson, T. Bjorkman, A. Bergman, H. C. Herper
Summary: The study presents an efficient computational approach for evaluating the entropy change of magnetocaloric materials, with a focus on hcp Gd. It demonstrates the importance of the mixed-scheme for magnetic Monte Carlo simulations and highlights the dominant contribution of magnetism to the entropy change. The calculated total entropy change is in agreement with experimental measurements at room temperature.
MATERIALS RESEARCH LETTERS
(2022)
Article
Astronomy & Astrophysics
Xue-Ning Bai
Summary: A major uncertainty in understanding the transport and feedback of cosmic rays lies in the unknown cosmic ray scattering rates. This study proposes a novel streaming box framework to study the cosmic ray streaming instability and precisely measure the cosmic ray scattering rates. The measured rates are consistent with existing theories but smaller than expected.
ASTROPHYSICAL JOURNAL
(2022)
Article
Mechanics
Nicolas Moreno, Marco Ellero
Summary: We propose a fully Lagrangian heterogeneous multiscale method (LHMM) for modeling complex fluids with microscopic features. The method discretizes the fluctuating Navier-Stokes equations using smoothed dissipative particle dynamics (SDPD) and exploits the multiscale features of SDPD to account for thermal fluctuations. The LHMM is validated using different flow configurations and fluid types, showing its flexibility in modeling complex fluids at the microscale.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Sungil Yun, Matthew Tom, Feiyang Ou, Gerassimos Orkoulas, Panagiotis D. Christofides
Summary: Atomic layer etching (ALE) is a promising method that can be optimized using a multiscale CFD model to simulate and improve the etching process of aluminum oxide thin films, leading to increased production efficiency and cost reduction.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Materials Science, Multidisciplinary
Sungil Yun, Henrik Wang, Matthew Tom, Feiyang Ou, Gerassimos Orkoulas, Panagiotis D. D. Christofides
Summary: This in silico study investigates the process operation conditions and reactor optimization for area-selective atomic layer deposition (ASALD) of SiO2/Al2O3 by using multiscale computational fluid dynamics (CFD) modeling. The research suggests that annular reaction zones and asymmetrical inlets can enhance uniform exposure to reagents and minimize reagent intermixing, allowing higher rotational speeds for the reactor. Additionally, low rotation speeds and high species mole fractions are required for complete deposition of a cycle of the ASALD process. This study provides insights into the ASALD process operation and contributes to its further industrial versatility.
Review
Green & Sustainable Science & Technology
Dana Marinic, Blaz Likozar
Summary: Direct air capture (DAC) has the potential to decarbonize the atmosphere and provide feedstock for industrial applications, but current DAC systems are costly in terms of energy and resources. Multiscale modeling has proven valuable in the rapid screening of sorbent candidates, integration of machine learning, and process design engineering. This review covers recent advances in DAC research, including theoretical descriptions, atom scale structuring, and the identification of effective sorbents.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Physics, Multidisciplinary
Timothy Foldes, Antony Lesage, Maria Barbi
Summary: This study reexamines the coil-globule transition of a polymer from a spectral perspective, introducing a new possibility and reintroducing overlooked mature spectral methods. This method not only allows for the determination of the polymer state without information about polymer length or interaction strength, but also proposes an experimental implementation.
PHYSICAL REVIEW LETTERS
(2021)
Article
Chemistry, Physical
Eugene A. Ustinov
Summary: In this study, a binary mixture is simulated at various temperatures using an extended version of the grand canonical kinetic Monte Carlo method. The focus is on understanding the thermodynamic properties of binary liquids, gases, and gas-liquid mixtures from a comprehensive perspective. The approach includes considering thermodynamic functions such as chemical potentials, Gibbs free energy, and entropy. The results show a high degree of accuracy and reproducibility when compared to experimental data, with the developed approach able to accurately reproduce the pressure-composition diagrams.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2023)
Article
Physics, Multidisciplinary
Vladimir P. Zhdanov
Summary: In conventional lattice percolation models, the interplay between percolation and kinetics is often ignored. However, in catalytic reactions, coke formation and removal significantly affect the reaction process.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Chemistry, Multidisciplinary
Richard S. Graham, Richard J. Wheatley
Summary: Accurate potential energy surfaces (PES) are required for predicting thermophysical properties from molecular principles. This study presents a widely-applicable method that produces first-principles PES using Gaussian Processes (GP) as a machine learning technique. The method accurately interpolates three-body non-additive interaction data and does not require modification for different molecules. It produces highly accurate interpolation from fewer training points and enables more accurate ab initio calculations. The method is exemplified by computing the PES for CO2-Ar mixtures, which allows for accurate first-principles predictions of various thermophysical properties.
CHEMICAL COMMUNICATIONS
(2022)
Article
Thermodynamics
Matteo Pelucchi, Steffen Schmitt, Nina Gaiser, Alberto Cuoci, Alessio Frassoldati, Hao Zhang, Alessandro Stagni, Patrick Osswald, Katharina Kohse-Hoeinghaus, Tiziano Faravelli
Summary: This study focuses on the interaction chemistry of DME-O2-NO mixtures in the low- to intermediate-temperature regime, investigating the oxidation behavior of DME in the presence of NO. The research combines experimental data with modeling analysis to study the kinetics of DME/NOx interactions, and assesses the uncertainty of key reactions using a polynomial chaos expansion analysis.
COMBUSTION AND FLAME
(2023)
Article
Chemistry, Physical
Johannes T. Margraf, Hyunwook Jung, Christoph Scheurer, Karsten Reuter
Summary: Chemical reaction networks are crucial for understanding heterogeneous catalytic processes. However, inferring networks from experiments alone is challenging due to the lack of microscopic information. Computational approaches provide insights but come with uncertainties. This Perspective highlights the applications of machine learning in catalytic reaction networks, aiding in both experimental inference and computational exploration.
Review
Biotechnology & Applied Microbiology
Yadong Chen, Tianxing Gong, Nan Jiang, Aoxiang Zhao, Tongyu Wang, Xiangdong Wang, Wenfeng Han
Summary: In clinical practice, ACL rupture is commonly repaired by the single-beam reconstruction method. Surgeons make diagnosis based on medical images before surgery. However, the biomechanical effects on the biological nature of femoral tunnel position are not well understood. This study used motion capture and medical image reconstruction to analyze the effects of different femoral tunnel positions on ACL biomechanics. The results showed significant differences in the direct mechanical effects of the ACL at different femoral tunnel locations.
BIOTECHNOLOGY AND GENETIC ENGINEERING REVIEWS
(2023)
Review
Environmental Sciences
Tongyu Wang, Naoko Kaida, Kosuke Kaida
Summary: The review critically summarizes the findings and potential areas for future research on the effects of outdoor artificial light at night (ALAN) on human health and behaviors. The lack of studies on the effects of outdoor ALAN on human behaviors and health, including social interaction, is identified as a crucial gap in scientific research. The review highlights the importance of investigating the complex relationships between outdoor ALAN, health, and behaviors with sleep as a key factor.
ENVIRONMENTAL POLLUTION
(2023)
Article
Chemistry, Physical
Simiam Ghan, Elias Diesen, Christian Kunkel, Karsten Reuter, Harald Oberhofer
Summary: We investigate the electronic coupling between an adsorbate and a metal surface by directly calculating tunneling matrix elements H-ad based on first principles. Using a projection-operator diabatization approach, we project the Kohn-Sham Hamiltonian onto a diabatic basis. By integrating the couplings over the Brillouin zone, we are able to calculate a size-convergent Newns-Anderson chemisorption function, which measures the broadening of an adsorbate frontier state upon adsorption. This function reveals not only the lifetime of the electron state, but also provides rich information on orbital phase interactions on the surface.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Physics, Multidisciplinary
R. Dupuy, J. Filser, C. Richter, T. Buttersack, F. Trinter, S. Gholami, R. Seidel, C. Nicolas, J. Bozek, D. Egger, H. Oberhofer, S. Thuermer, U. Hergenhahn, K. Reuter, B. Winter, H. Bluhm
Summary: The determination of depth profiles across interfaces is crucial in various scientific and technological fields. While photoemission spectroscopy is a suitable method for this purpose, investigating liquid-vapor interfaces has been hindered by a lack of understanding of electron-scattering processes in liquids. However, recent studies have shown that core-level photoelectron angular distributions can provide information on the depth distribution of species across the interface. This study further explores this concept and demonstrates that the experimental anisotropy parameter scales linearly with the average distance of atoms along the surface normal, achieving excellent depth resolution.
PHYSICAL REVIEW LETTERS
(2023)
Article
Psychology, Experimental
Yuqing Zhao, Ting Zeng, Tongyu Wang, Fang Fang, Yi Pan, Jianrong Jia
Summary: This study found that statistical encoding compresses redundant information from multiple items into a single summary metric. The researchers examined the involvement of the subcortex in processing summary statistics and found that invisible circles were automatically included in the statistical representation, but only when presented to the same eye as visible circles.
Article
Chemistry, Physical
Gabriele Contaldo, Matteo Ferri, Chiara Negri, Isabella Nova, Matteo Maestri, Enrico Tronconi
Summary: Dispersion corrected density functional theory calculations reveal that the presence of H2O in the Reduction Half-Cycle (RHC) of NH3-SCR decreases the rate and activation energy by enthalpic stabilization of the kinetically-relevant transition state (TS). Non-specific dispersion forces play a crucial role in reducing the activation enthalpy. However, the enthalpic stabilization is counteracted by additional entropy losses caused by the presence of H2O. The calculated enthalpy and entropy changes agree well with experimental measurements, emphasizing the importance of molecular scale description of reaction environments.
Article
Engineering, Environmental
Claudio Ferroni, Mauro Bracconi, Matteo Ambrosetti, Gianpiero Groppi, Matteo Maestri, Enrico Tronconi
Summary: Cellular materials are potential alternatives to honeycomb monoliths for improving automotive DeNOx-SCR abatement efficiency. Computational Fluid Dynamics simulations are used to assess the performance of these innovative substrates in terms of pressure drop and abatement efficiency. The simulations reveal that a radial flow configuration of the reactor loaded with cellular materials can reduce the pressure drop and improve the abatement efficiency.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Energy & Fuels
Matteo Savarese, Alberto Cuoci, Ward De Paepe, Alessandro Parente
Summary: In this study, a novel methodology for the design of CRN models was proposed, which involves the post-processing of CFD data using unsupervised clustering and graph scanning algorithms. The methodology was tested on a semi-industrial furnace and showed promising predictive performances.
Article
Chemistry, Physical
Martin Vondrak, Karsten Reuter, Johannes T. T. Margraf
Summary: This paper presents the q-pac Python package, which implements several algorithmic and methodological advances to the kQEq method and provides an extendable framework for the development of ML charge equilibration models.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
King Chun Lai, Sebastian Matera, Christoph Scheurer, Karsten Reuter
Summary: The nature of an atom in a bonded structure depends on its local atomic environment. Identifying groups of atoms with equivalent environments is a frequent task in atomic-scale modeling and simulation, and we present a machine-learning framework to automate this task.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Sina Stocker, Hyunwook Jung, Gabor Csanyi, C. Franklin Goldsmith, Karsten Reuter, Johannes T. Margraf
Summary: This study demonstrates that machine-learning methods can be used to predict the rate constants of elementary reaction steps in catalytic processes. The results show that thermal effects have a significant impact on the free energy barriers, which can be different from the predictions based on the harmonic transition state theory approximation. This finding calls into question the previously reported mechanisms established by microkinetic models.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Chemistry, Multidisciplinary
Riccardo Uglietti, Daniele Micale, Damiano La Zara, Aristeidis Goulas, Luca Nardi, Mauro Bracconi, J. Ruud van Ommen, Matteo Maestri
Summary: This study demonstrates the potential of combining numerical and experimental approaches in understanding catalytic reactors, particularly fluidized beds. Through experiments and data analysis, the effectiveness of the in-house first-principles multiscale Computational Fluid Dynamic-Discrete Element Method (CFD-DEM) model is validated, and the fundamental insights that can be achieved through detailed numerical methods are reported. The integration of experimental information and numerical simulations allows for optimal design and scale-up procedures for reactor configurations with promising prospects in the energy transition.
REACTION CHEMISTRY & ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Ke Chen, Christian Kunkel, Bingqing Cheng, Karsten Reuter, Johannes T. Margraf
Summary: Machine learning is widely used in predicting chemical properties, especially energies and forces in molecules and materials. However, some electronic properties do not scale linearly with system size, leading to large errors when using size-extensive models. This study explores different strategies to learn intensive and localized properties, focusing on HOMO energies in organic molecules.