Corrigendum to: "Entropy Scaling-Based Correlation for Estimating the Self-Diffusion Coefficients of Pure Fluids".
Ontology highlight
ABSTRACT:
SUBMITTER: Dehlouz A
PROVIDER: S-EPMC11363014 | biostudies-literature | 2024 Aug
REPOSITORIES: biostudies-literature
Publications
Corrigendum to: "Entropy Scaling-Based Correlation for Estimating the Self-Diffusion Coefficients of Pure Fluids".
Dehlouz Aghilas A Jaubert Jean-Noël JN Galliero Guillaume G Bonnissel Marc M Privat Romain R
Industrial & engineering chemistry research 20240816 34
[This corrects the article DOI: 10.1021/acs.iecr.2c01086.]. ...[more]
Similar Datasets
Project description:Entropy scaling is a powerful technique that has been used for predicting transport properties of pure components over a wide range of states. However, modeling mixture diffusion coefficients by entropy scaling is an unresolved task. We tackle this issue and present an entropy scaling framework for predicting mixture self-diffusion coefficients as well as mutual diffusion coefficients in a thermodynamically consistent way. The predictions of the mixture diffusion coefficients are made based on information on the self-diffusion coefficients of the pure components and the infinite-dilution diffusion coefficients. This is accomplished using information on the entropy of the mixture, which is taken here from molecular-based equations of state. Examples for the application of the entropy scaling framework for the prediction of diffusion coefficients in mixtures illustrate its performance. It enables predictions over a wide range of temperatures and pressures including gaseous, liquid, supercritical, and metastable states-also for strongly non-ideal mixtures.
| S-EPMC11914492 | biostudies-literature
Project description:Molecular self-diffusion coefficients underlie various kinetic properties of the liquids involved in chemistry, physics, and pharmaceutics. In this study, 547 self-diffusion coefficients are calculated based on all-atom molecular dynamics (MD) simulations of 152 diverse pure liquids at various temperatures employing the OPLS4 force field. The calculated coefficients are compared with experimental data (424 extracted from the literature and 123 newly measured by pulsed-field gradient nuclear magnetic resonance). The calculations well agree with the experimental values. The determination coefficient and root mean square error between the observed and calculated logarithmic self-diffusion coefficients of the 547 entries are 0.931 and 0.213, respectively, demonstrating that the MD calculation can be an excellent industrial tool for predicting, for example, molecular transportation in liquids such as the diffusion of active ingredients in biological and pharmaceutical liquids. The self-diffusion coefficients collected in this study are compiled into a database for broad researches including artificial intelligence calculations.
| S-EPMC9804551 | biostudies-literature
Project description:In our previous work [Yang X.J. Chem. Eng. Data2021, 66, 1385-1398], a residual entropy scaling (RES) approach was developed to link viscosity to residual entropy using a 4-term power function for 39 refrigerants. In further research [Yang X.Int. J. Thermophys.2022, 43, 183], this RES approach was extended to 124 pure fluids containing fluids from light gases (hydrogen and helium) to dense fluids (e.g., heavy hydrocarbons) and fluids with strong association force (e.g., water). In these previous research studies, the model was developed by manual optimization of the power function. The average absolute relative deviation (AARD) of experimental data from the RES model is approximately 3.36%, which is higher than the 2.74% obtained with the various models in REFPROP 10.0. In the present work, the power function was optimized by iteratively fitting the global (fluid-independent power terms) and local parameters (fluid-specific and group-specific parameters) and screening the experimental data. The resulting equation has only three terms instead of four. Most notably, the AARD of the new RES model is reduced down to 2.76%; this is very close to the various multiparameter models in REFPROP 10.0, while the average relative deviation (ARD) amounts to 0.03%, which is smaller than REFPROP 10.0's 0.7%. A Python package is provided for the use of the developped model.
| S-EPMC11831593 | biostudies-literature
Project description:Coefficients for translational and rotational diffusion characterize the Brownian motion of particles. Emerging X-ray photon correlation spectroscopy (XPCS) experiments probe a broad range of length scales and time scales and are well-suited for investigation of Brownian motion. While methods for estimating the translational diffusion coefficients from XPCS are well-developed, there are no algorithms for measuring the rotational diffusion coefficients based on XPCS, even though the required raw data are accessible from such experiments. In this paper, we propose angular-temporal cross-correlation analysis of XPCS data and show that this information can be used to design a numerical algorithm (Multi-Tiered Estimation for Correlation Spectroscopy [MTECS]) for predicting the rotational diffusion coefficient utilizing the cross-correlation: This approach is applicable to other wavelengths beyond this regime. We verify the accuracy of this algorithmic approach across a range of simulated data.
| S-EPMC8403868 | biostudies-literature
Project description:Fluorescence correlation spectroscopy (FCS) is frequently used to study diffusion in cell membranes, primarily the plasma membrane. The diffusion coefficients reported in the plasma membrane of the same cell type and even within single cells typically display a large spread. We have investigated whether this spread can be explained by variations in membrane topography throughout the cell surface, that changes the amount of membrane in the FCS focal volume at different locations. Using FCS, we found that diffusion of the membrane dye DiI in the apical plasma membrane was consistently faster above the nucleus than above the cytoplasm. Using live cell scanning ion conductance microscopy (SICM) to obtain a topography map of the cell surface, we demonstrate that cell surface roughness is unevenly distributed with the plasma membrane above the nucleus being the smoothest, suggesting that the difference in diffusion observed in FCS is related to membrane topography. FCS modeled on simulated diffusion in cell surfaces obtained by SICM was consistent with the FCS data from live cells and demonstrated that topography variations can cause the appearance of anomalous diffusion in FCS measurements. Furthermore, we found that variations in the amount of the membrane marker DiD, a proxy for the membrane, but not the transmembrane protein TCRζ or the lipid-anchored protein Lck, in the FCS focal volume were related to variations in diffusion times at different positions in the plasma membrane. This relationship was seen at different positions both at the apical cell and basal cell sides. We conclude that it is crucial to consider variations in topography in the interpretation of FCS results from membranes.
| S-EPMC7443568 | biostudies-literature
Project description:A residual entropy scaling (RES) approach combined with the cubic equation of state (EoS) was developed to calculate the viscosity and thermal conductivity of 151 common fluids. These pure fluids are all the pure fluids available in the NIST's REFPROP 10.0 database. Seven cubic EoS were studied, while only four yielded good and similar results; these are Peng-Robinson (PR), Soave-Redlich-Kwong (SRK), Patel-Teja-Valderrama (PTV), and Yang-Frotscher-Richter (YFR) EoS. The parameters of a pure fluid in this cubic EoS + RES approach were fitted using experimental data if they are available in the NIST ThermoData Engine database 10.1, otherwise, using the calculations of REFPROP 10.0. This approach is applicable in the entire temperature and pressure ranges for thermal conductivity and at pressures lower than 60 MPa for viscosity. Using this approach, the average absolute value of the relative deviation (AARD) of all of the analyzable experimental values from model calculations was approximately 3.1% and 3.6% for viscosity and thermal conductivity, respectively. This result is not too bad compared to 2.7% and 2.5% obtained by the state-of-the-art viscosity and thermal conductivity models in REFPROP 10.0. The key advantage of this approach is that it has a much simpler equation form and can be easily extended to more fluids. The developed approach has been implemented in the OilMixProp 1.0 software package, and this work will be a basis for the future development of more than 600 pure fluids.
| S-EPMC11840589 | biostudies-literature
Project description: Not available
| S-EPMC11317982 | biostudies-literature
Project description:Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.
| S-EPMC5863042 | biostudies-literature
Project description:Diffusion coefficients for proteins in water are predicted. The numerical method developed is general enough to be applied to a wide range of protein surface shapes, from rodlike to globular. Results are presented for lysozyme and tobacco mosaic virus, and they are compared with actual data and with predictions made by less general methods.
| S-EPMC46400 | biostudies-other
Project description:In molecular dynamics simulations in the NPT ensemble at constant pressure, the size and shape of the periodic simulation box fluctuate with time. For particle images far from the origin, the rescaling of the box by the barostat results in unbounded position displacements. Special care is thus required when a particle trajectory is unwrapped from a projection into the central box under periodic boundary conditions to a trajectory in full three-dimensional space, e.g., for the calculation of translational diffusion coefficients. Here, we review and compare different schemes in use for trajectory unwrapping. We also specify the corresponding rewrapping schemes to put an unwrapped trajectory back into the central box. On this basis, we then identify a scheme for the calculation of diffusion coefficients from NPT simulations, which is a primary application of trajectory unwrapping. In this scheme, the wrapped and unwrapped trajectory are mutually consistent and their statistical properties are preserved. We conclude with advice on best practice for the consistent unwrapping of constant-pressure simulation trajectories and the calculation of accurate translational diffusion coefficients.
| S-EPMC10269326 | biostudies-literature