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In this paper, the self-diffusion coefficients (SDC) of CH4/CO2/H2O in coal molecules under different ensemble conditions and the effects of temperature and pressure are calculated by grand canonical Monte Carlo and molecular dynamics (MD) methods. Results indicated that the self-diffusion coefficients of CH4 and CO2 gradually increase with the increasing temperature under the NPT ensemble. The SDC of H2O increases undulatory and it is higher than that of CH4 and CO2 at each temperature, where the diffusion activation energy followed the order of \begin{document}$E({{\mathrm{H}}_2{\mathrm{O}}})

Featured Application: The materials studied may be used in the radioactive waste vitrification process. Additionally, the proposed simulation method may be useful in optimizing glass composition and predicting the glass elements migration. Among the many possible applications of iron phosphate glasses, one of them is that they are promising materials in waste vitrification, particularly for radioactive waste. In vitrified form, waste elements should be permanently immobilized in a glass network as they are susceptible to harsh environmental conditions. The self-diffusion of the vitrified material species may limit the potential usefulness of the glasses. This paper presents the possibility of using molecular dynamics simulations to study this process and the substitution of SrO into an iron phosphate glass network. It was evidenced that the self-diffusion mechanism differed significantly depending on whether the glass was in a solid or liquid state. The proposed method also offered a relatively easy prediction of glass characteristic temperatures, such as transformation and flow. We also observed, and here describe, an aggregation process of the glass elements that may drive their crystallization. The obtained results are discussed in light of the experimental and theoretical structural feature literature data. [ABSTRACT FROM AUTHOR]

This study aims to investigate a diffusive predator–prey system incorporating additional food for predators, prey refuge, fear effect, and its carry-over effects. For the temporal model, the well-posedness and persistence of the system have been discussed. We investigated the existence and the stability behavior of the various equilibria. Furthermore, we explored the bifurcations of codimension-1 including transcritical, saddle-node, and Hopf, concerning the crucial parameters. The system also presents codimension-2 bifurcations such as Bogdanov–Takens and cusp bifurcation along with the global homoclinic bifurcation. We observed the bubbling phenomena, which illustrate the fluctuations in the amplitudes of the periodic oscillations. For the spatiotemporal system, we established the non-negativity and boundedness of the solutions. We derived the conditions for the diffusion-driven instabilities in a confined region with Neumann boundary conditions. Extensive numerical simulations have been conducted to depict the various stationary patterns in Turing space. It is observed that incorporating cross-diffusion divides the bi-parametric plane into various sub-regions and dynamic patterns are analyzed in these different regions. The intricate spatiotemporal dynamics exhibited by prey–predator interactions are crucial for unraveling the intricacies within ecological systems. [ABSTRACT FROM AUTHOR]

In this work, a two-dimensional Coupled Map Lattice (CML) method is applied to a continuous Reaction–Diffusion (RD) SIRS epidemic model. The model involves a complex, nonlinear incidence rate and the spatially heterogeneous self- and cross-diffusion coefficients. The CML method applied here results in a space–time discrete counterpart of the model. The basic reproduction number is derived, and the local stability of the fixed points of the discrete model is established. The two typical bifurcations of codimension 1, such as the flip and the Neimark–Sacker (NS) bifurcations, are investigated by utilizing the center manifold theory and the normal form theorem. Furthermore, the codimension-2 fold–flip bifurcation about the endemic fixed point is discussed in detail. Turing instability criterion of the cross-diffusion epidemic model is established by utilizing the CML method under periodic boundary conditions. Finally, the numerical simulations clarify both the effectiveness of all theoretical findings and the effects of time step size and cross-diffusion on the spatiotemporal patterns. The main contribution of this work is the discovery of some interesting Turing and non-Turing patterns which may be induced by flip–Turing instability, NS–Turing instability or fold–flip–Turing instability. Concretely speaking, spiral patterns are observed near the flip bifurcation threshold value and ultimately evolve into an irregular defect pattern. Stripe pattern, labyrinth pattern and circular pattern are observed near the NS bifurcation threshold value. The disorderly interwoven ribbon pattern, mosaic pattern and some irregular oscillatory patterns are observed near the fold–flip bifurcation point. Our results greatly help in predicting the long-term behavior of the space–time discrete SIRS epidemic model under study. [ABSTRACT FROM AUTHOR]

The secondary consequences of predator species on prey species have substantial implications for population dynamics. A deeper comprehension of the dynamics between prey and predator can be achieved through the examination of indirect consequences. This work examines the dynamic behavior of a modified Holling-Tanner model. The interactions between the species are characterized by a functional response of the Beddington–DeAngelis type. Factors such as prey refuge, fear factor, disturbance intensity, and cross diffusion have been taken into account. The boundedness, feasibility of equilibrium points, their stability and restrictions for Hopf bifurcation of non-spatial model system are derived. The study explores the combined effects of prey refuge presence and fear factors on population dynamics. Furthermore, the investigation focuses on the stability of spatial self-diffusion and cross-diffusion model systems, as well as the specific conditions that lead to Turing instability. Ultimately, it has been shown that in the context of self-diffusion, a moderate level of fear promotes the survival of prey, whereas an excessive level of dread hinders the survival of prey. Concurrently, the mean density of prey exhibited a gradual decline as the refuge parameters increased. The spatial patterns of the population have also been investigated. As the mutual interference between prey populations intensifies, the spatial distribution of the prey population transitions from a clustered pattern to a combination of striped and clustered patterns, ultimately settling into a striped pattern. With the gradual growth of the half saturation constant, the prey population reached a state of uniform distribution. In the scenario of cross diffusion, when the prey is heavily impacted by the pursuit of predators, the fear effect, when appropriately used, did not have a significant impact on the survival of the prey. This work adds to the existing body of knowledge by revealing novel insights into the influence of indirect factors on the behavior of predator and prey populations.

This study investigated the effect of polyols erythritol, d-mannitol, and maltitol on the volatility of aroma compounds γ-butyrolactone, 3-methyl-1-butanol, and 2-phenylethanol in aqueous solution. Headspace solid-phase microextraction/gas chromatography and diffusion-ordered nuclear magnetic resonance techniques were used to obtain information on aroma–food matrix interaction. Results demonstrated that adding polyols at final low concentrations of 5% or 10% (w/w) to an aqueous solution of 2-phenylethanol reduced the release of vapor-phase aromas, except in the case of 3-methyl-1-butanol, which was not affected by the presence of polyols in the liquid matrix. Polyols also reduced the diffusion coefficients of all three aroma compounds, probably due to friction between the molecules. At low polyol concentrations, aroma compound volatility and diffusion coefficient values were altered compared to those of aromas released from pure water. This observation is related to the physicochemical properties of the aroma compounds. These insights may help guide the use of the combination of aroma compounds and polyols in the formulation of sugar-free and reduced-sugar beverages. Chemical compounds: γ-butyrolactone (PubChem CID: 7302), 3-methyl-1-butanol (PubChem CID: 31260), 2-phenylethanol (PubChem CID: 6054), erythritol (PubChem CID: 222285), d-mannitol (PubChem CID: 6251), maltitol (PubChem CID: 493591).

Abstract Background Characterizing cancer molecular subtypes is crucial for improving prognosis and individualized treatment. Integrative analysis of multi-omics data has become an important approach for disease subtyping, yielding better understanding of the complex biology. Current multi-omics integration tools and methods for cancer subtyping often suffer challenges of high computational efficiency as well as the problem of weight assignment on data types. Results Here, we present an efficient multi-omics integration via weighted affinity and self-diffusion (MOSD) to dissect cancer heterogeneity. MOSD first construct local scaling affinity on each data type and then integrate all affinities by weighted linear combination, followed by the self-diffusion to further improve the patients’ similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness for cancer subtyping, we apply MOSD across ten cancer types with three measurements (Gene expression, DNA methylation, miRNA). Conclusions Our approach exhibits more significant differences in patient survival and computationally efficient benchmarking against several state-of-art integration methods and the identified molecular subtypes reveal strongly biological interpretability. The code as well as its implementation are available in GitHub: https://github.com/DXCODEE/MOSD .

Background: Characterizing cancer molecular subtypes is crucial for improving prognosis and individualized treatment. Integrative analysis of multi-omics data has become an important approach for disease subtyping, yielding better understanding of the complex biology. Current multi-omics integration tools and methods for cancer subtyping often suffer challenges of high computational efficiency as well as the problem of weight assignment on data types. Results: Here, we present an efficient multi-omics integration via weighted affinity and self-diffusion (MOSD) to dissect cancer heterogeneity. MOSD first construct local scaling affinity on each data type and then integrate all affinities by weighted linear combination, followed by the self-diffusion to further improve the patients' similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness for cancer subtyping, we apply MOSD across ten cancer types with three measurements (Gene expression, DNA methylation, miRNA). Conclusions: Our approach exhibits more significant differences in patient survival and computationally efficient benchmarking against several state-of-art integration methods and the identified molecular subtypes reveal strongly biological interpretability. The code as well as its implementation are available in GitHub: https://github.com/DXCODEE/MOSD. [ABSTRACT FROM AUTHOR]

Among the many possible applications of iron phosphate glasses, one of them is that they are promising materials in waste vitrification, particularly for radioactive waste. In vitrified form, waste elements should be permanently immobilized in a glass network as they are susceptible to harsh environmental conditions. The self-diffusion of the vitrified material species may limit the potential usefulness of the glasses. This paper presents the possibility of using molecular dynamics simulations to study this process and the substitution of SrO into an iron phosphate glass network. It was evidenced that the self-diffusion mechanism differed significantly depending on whether the glass was in a solid or liquid state. The proposed method also offered a relatively easy prediction of glass characteristic temperatures, such as transformation and flow. We also observed, and here describe, an aggregation process of the glass elements that may drive their crystallization. The obtained results are discussed in light of the experimental and theoretical structural feature literature data.

Self-diffusion of components of a binary chloroform – methanol system has been investigated by experimental (pulsed gradient spin-echo nuclear magnetic resonance (PGSE NMR)), molecular dynamics (MD) simulation and theoretical (athermal associated solution model) methods at 298 K. The concentration dependences of the effective hydrodynamic radii of the mixture components calculated on the basis of the Stokes-Einstein equation are shown to be extremal, which indicates molecular association in the system. The analysis of the radial distribution functions and the fraction of molecules of chloroform and methanol with i-hydrogen bonds obtained from MD simulations made it possible to determine the degree of self- and heteroassociation of the mixture components. The contributions of self-association and heteroassociation to the self-diffusion of the mixture components are estimated within the framework of the athermal associated solution model, taking into account the Stokes-Einstein equation.

In the present work, we carried out a molecular dynamics study of the kinetic properties of the FLiNaK molten salt, as well as a detailed study of the structure of this salt melt. The high value of the self-diffusion coefficient of fluorine ions is due to the large number of Coulomb repulsions between the most numerous negative ions. The calculated values of shear viscosity are in good agreement with the experimental data, as well as with the reference data obtained on the basis of finding the most reliable data. The total and partial functions of the radial distribution are calculated. According to the statistical analysis, fluorine ions have the greatest numerical diversity in the environment of similar ions, and sodium ions with the lowest representation in FLiNaK, have the least such diversity. For the subsystem of fluorine ions, the rotational symmetry of the fifth order is the most pronounced. Some of the fluorine ions form linear chains consisting of three atoms, which are not formed for positive ions. The results of the work give an understanding of the behavior molten FLiNaK under operating conditions in a molten salt reactor and will find application in future studies of this molten salt.

The dynamical properties of four protic ionic liquids, based on the ethyltriazolium ([C 2 HTr 124 ]) and the ethylimidazolium ([C 2 HIm]) cation, were investigated. The associated anions were the triflate ([TfO]) and the bistriflimide ([TFSI]). Ionic conductivity values and self-diffusion coefficients were measured and discussed, extending the discussion to the concept of fragility. Furthermore, in order to allow the measurement of the ionic conductivity of very small volumes (<0.5 mL) of ionic liquid under an inert and dry atmosphere, a new setup was developed. It was found that the cation nature strongly affected the transport properties, the [C 2 HTr 124 ] cation resulting in slower dynamics than the [C 2 HIm] one. This was concluded from both conductivity and diffusivity measurements while for both properties, the anion had a lesser effect. By fitting the conductivity data with the Vogel–Fulcher–Tammann (VFT) equation, we could also estimate the fragility of these ionic liquids, which all fell in the range of very fragile glass-forming materials. Finally, the slower dynamics observed in the triazolium-based ionic liquids can be rationalized by the stronger interactions that this cation establishes with both anions, as deduced from the frequency analysis of relevant Raman signatures and density functional theory (DFT) calculations. [ABSTRACT FROM AUTHOR]

Oxidative polymerization of plant oils and lipids is poorly understood yet widely encountered. Oils and fats are renewable resources providing biofuels and polymers. Oil oxidation is accelerated at high temperatures, typically above 110°C, where triacylglycerides are converted into toxic compounds and viscous deleterious polymers. Polymerization of mono‐unsaturated oil (210°C, 3 h, open to air) was investigated by comparing four similar sized molecules with different functional groups: oleic acid, methyl oleate, trans‐7‐tetradecene, and stearic acid. Non‐volatile products identified by NMR spectroscopy are minor ketones for saturated fatty acid (stearic acid), epoxides for acyl chains without acid groups (methyl oleate, tetradecane) and copious oligomerization, through ester cross‐links, for acyl chains with acid, and olefinic groups (oleic acid). Long range CH coupling clearly shows ester (not ether) cross‐links, contradicting long‐held beliefs. Chain fragmentation also occurs for heated oleic acid as revealed by formation of a species with a methylene group bonded to oxygen of an ester, CH2OC(O). Large size (slow diffusion) of the first oligomer (trimer) formed from oleic acid, used to represent hydrolyzed vegetable oil, was evidenced by DOSY (diffusion‐ordered spectroscopy). Combined NMR results show oligomers found in heated oleic acid are fatty acid estolides. Model oil reactions demonstrate why olefin and carboxylic acid groups are required for polymerization. [ABSTRACT FROM AUTHOR]

The equivalent characteristics of the materials' interfaces are known to impact the overall mechanical properties of ceramic–metal composites significantly. One technological method that has been suggested is raising the temperature of the liquid metal to improve the weak wettability of ceramic particles with liquid metals. Therefore, as the first step, it is necessary to produce the diffusion zone at the interface by heating the system and maintaining it at a preset temperature to develop the cohesive zone model of the interface using mode I and mode II fracture tests. This study uses the molecular dynamics method to study the interdiffusion at the interface of α-Al2O3/AlSi12. The hexagonal crystal structure of aluminum oxide with the Al- and O-terminated interfaces with AlSi12 are considered. A single diffusion couple is used for each system to determine the average main and cross ternary interdiffusion coefficients. In addition, the effect of temperature and the termination type on the interdiffusion coefficients is examined. The results demonstrate that the thickness of the interdiffusion zone is proportional to the annealing temperature and time, and Al- and O-terminated interfaces exhibit similar interdiffusion properties. [ABSTRACT FROM AUTHOR]

This study is seeking a better understanding of polyethylene glycol (PEG) as a solvent to promote its use in chemical synthesis. The effect of adding two solutes of interest, 2,2,6,6-tetramethylpiperidinyloxyl (TEMPO) and 5-tert-butylisophthalic acid (5-TBIPA) to PEG200 (average molar weight of 200 g·mol−1) on the solution density, viscosity, and self-diffusion coefficients is monitored in a temperature range of 298.15–358.15 K to deduce how these solutes interact with the PEG200 solvent. The effect of water, the most common impurity in PEGs, is also monitored and found to be nearly negligibly small. Addition of (5-TBIPA) increases solution density and viscosity. Combined with the observation that 5-TBIPA consistently self-diffuses at about half the rate as PEG200 at all investigated experimental conditions, this suggests strong attractive solute–solvent interactions likely through hydrogen bonding interactions. In contrast, addition of TEMPO causes lower solution densities and viscosities suggesting that the solute–solvent interactions of TEMPO lead to an overall weakening of the intermolecular interactions present compared to neat PEG200. Inspection of the viscosity and self-diffusion temperature dependence reveals slight deviations from the Arrhenius equation. Interestingly, the activation energies obtained from the viscosity and the self-diffusion data are essentially identical in values suggesting that the same dynamic processes and thus the same activation barriers govern translational motion and momentum transfer in these PEG200 solutions. [ABSTRACT FROM AUTHOR]

The review considers a number of typical mechanisms of degradation of thermoelectric materials in the process of their functioning as a part of thermoelectric generator modules. Among them are diffusion and self-diffusion in thermoelectric materials and contact structures, loss of dopants by thermoelectric materials due to sublimation, formation of intermetallic compounds in contact structures, accumulation of structural defects under the influence of thermal and mechanical loads, mechanical destruction of thermoelectric legs, contact structures and other structural members of modules, chemical interaction of thermoelectric materials with other structural members. The main design and technological ways and methods of reducing the negative impact of these mechanisms on the reliability of thermoelectric materials and modules are also considered.

The evaluation of an oil's oxidation status during industrial production is highly important for monitoring the oil's purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil's material characterization. Towards this goal, a 1H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a 1H LF-NMR relaxation curve, and its reconstruction into T1 and T2 spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T2 relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T2 signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications. [ABSTRACT FROM AUTHOR]

Interactions between nanoparticles (NPs) determine their self-organization and dynamic processes. In these systems, a quantitative description of the interparticle forces is complicated by the presence of the hydrophobic effect (HE), treatable only qualitatively, and due to the competition between the hydrophobic and hydrophilic forces. Recently, instead, a sort of crossover of HE from hydrophilic to hydrophobic has been experimentally observed on a local scale, by increasing the temperature, in pure confined water and studying the occurrence of this crossover in different water–methanol solutions. Starting from these results, we then considered the idea of studying this process in different nanoparticle solutions. By using photon correlation spectroscopy (PCS) experiments on dendrimer with OH terminal groups (dissolved in water and methanol, respectively), we show the existence of this hydrophobic–hydrophilic crossover with a well defined temperature and nanoparticle volume fraction dependence. In this frame, we have used the mode coupling theory extended model to evaluate the measured time-dependent density correlation functions (ISFs). In this context we will, therefore, show how the measured spectra are strongly dependent on the specificity of the interactions between the particles in solution. The observed transition demonstrates that just the HE, depending sensitively on the system thermodynamics, determines the hydrophobic and hydrophilic interaction properties of the studied nanostructures surface. [ABSTRACT FROM AUTHOR]

The diffusion coefficient of a metallic alloy system is critical to understanding phase transformations, mechanical properties, and failure mechanisms such as creep. Rather than performing costly and time consuming experiments, first-principles calculations based on density functional theory have emerged as a robust alternative to experimentally determined diffusion coefficients. In this work, first-principles calculations based on density functional theory are used to investigate the effects of varying the exchange-correlation functional and supercell size had on the accuracy of calculated self-diffusion coefficients. Self-diffusion and thermodynamic properties of Cu, Ni, and Ag are calculated using the two exchange-correlation functionals: the local density approximation and the generalized gradient approximation of Perdew, Burke, and Ernzerhof for solids. Supercells of 32, 64, and 108 atoms are used to investigate the minimum supercell size necessary to isolate the vacancy. Finally, two methods for accounting for vibrational entropy contributions due to temperature are employed: a simple estimation of the diffusion prefactor used previously in the literature and harmonic phonon calculations. The estimation of the diffusion prefactor produces acceptable results for Cu, but not for the other metals investigated. The harmonic phonon calculations improves both self-diffusion and thermodynamic properties when compared to experimental values for Cu, Ni, and Ag. For Cu and Ni, self-diffusion coefficients and related thermodynamic properties are consistent and stable regardless of the supercell size or exchange-correlation functional employed. [ABSTRACT FROM AUTHOR]

One primary concern in colloid science is understanding the relationship of its macroscopic rheology and diffusion behavior with the observed microscopic arrangements of the nanoparticles in the fluid. This manuscript addresses the study of these dynamical properties through a first-principle stochastic method. Both properties directly relate to the observed fluid structure factor, which depends on a few known material parameters. However, in the literature, this static quantity is reported up to the first prominent peak of its small momentum transfer of the scattered radiation, leading to inaccurate determination of the transport properties. Here, it is proposed to use the rescaled mean spherical approximation under the requirement of fitting the experimental data of the structure beyond the dependence of more significant wave numbers. The predicted viscosity agrees with the observed ones at a low volume fraction of particles for ferrofluids dispersed in polymer solvents. This rheological quantity is inversely related to the self-diffusion coefficient of a tracer particle. [ABSTRACT FROM AUTHOR]

Fullerene derivatives are of great interest in various fields of science and technology. Fullerene derivatives are known to have pronounced anticancer and antiviral activity. They have antibacterial properties. Their properties are largely determined by association processes. Understanding the nature and properties of associates in solvents of various types will make it possible to make significant progress in understanding the mechanisms of aggregation of molecules of fullerene derivatives in solutions. Thus, this work, aimed at studying the size and stability of associates, is relevant and promising for further research. The NMR method in a pulsed field gradient was used, which makes it possible to directly study the translational mobility of molecules. The sizes of individual molecules and associates were calculated based on the Stokes–Einstein model. The lifetime of associates was also estimated. The interaction of water-soluble C60 fullerene derivatives with erythrocytes was also evaluated. The values of self-diffusion coefficients and the lifetime of molecules of their compounds in cell membranes are obtained. It is concluded that the molecules of fullerene derivatives are fixed on the cell surface, and their forward movement is controlled by lateral diffusion. [ABSTRACT FROM AUTHOR]

The food quality of edible oils is dependent on basic chemical and structural changes that can occur by oxidation during preparation and storage. A rapid and efficient analytical method of the different steps of oil oxidation is described using a time-domain nuclear magnetic resonance (TD-NMR) sensor for measuring signals related to the chemical and physical properties of the oil. The degree of thermal oxidation of edible oils at 80 °C was measured by the conventional methodologies of peroxide and aldehyde analysis. Intact non-modified samples of the same oils were more rapidly analyzed for oxidation using a TD-NMR sensor for 2D T1-T2 and self-diffusion (D) measurements. A good linear correlation between the D values and the conventional chemical analysis was achieved, with the highest correlation of R2 = 0.8536 for the D vs. the aldehyde concentrations during the thermal oxidation of poly-unsaturated linseed oils, the oil most susceptible to oxidation. A good correlation between the D and aldehyde levels was also achieved for all the other oils. The possibility to simplify and minimize the time of oxidative analysis using the TD NMR sensors D values is discussed as an indicator of the oil's oxidation quality, as a rapid and accurate methodology for the oil industry. [ABSTRACT FROM AUTHOR]

We discuss a phenomenon regarding water that was until recently a subject of scientific interest: i.e., the dynamical crossover, from the fragile to strong glass forming material, for both bulk and protein hydration water. Such crossover is characterized by a temperature TL in which significant dynamical changes like the decoupling (or the violation of the Stokes-Einstein relation) of homologous transport parameters, e.g., the density relaxation time τ and the viscosity η, occur in the system. On this respect we considered the dynamic properties of water-protein systems. More precisely, we focused our study on proteins and their hydration water, as far as bulk and confined water. In order to clarify the effects of the water dynamical crossover on the protein properties we considered and discussed in a comparative way previous and new experimental data, obtained from different techniques and molecular dynamic simulation (MD). We pointed out the reasons for different dynamical findings from the use of different experimental techniques.

The dynamical properties of four protic ionic liquids, based on the ethyltriazolium ([C2HTr124]) and the ethylimidazolium ([C2HIm]) cation, were investigated. The associated anions were the triflate ([TfO]) and the bistriflimide ([TFSI]). Ionic conductivity values and self-diffusion coefficients were measured and discussed, extending the discussion to the concept of fragility. Furthermore, in order to allow the measurement of the ionic conductivity of very small volumes (2HTr124] cation resulting in slower dynamics than the [C2HIm] one. This was concluded from both conductivity and diffusivity measurements while for both properties, the anion had a lesser effect. By fitting the conductivity data with the Vogel–Fulcher–Tammann (VFT) equation, we could also estimate the fragility of these ionic liquids, which all fell in the range of very fragile glass-forming materials. Finally, the slower dynamics observed in the triazolium-based ionic liquids can be rationalized by the stronger interactions that this cation establishes with both anions, as deduced from the frequency analysis of relevant Raman signatures and density functional theory (DFT) calculations.

The equivalent characteristics of the materials’ interfaces are known to impact the overall mechanical properties of ceramic–metal composites significantly. One technological method that has been suggested is raising the temperature of the liquid metal to improve the weak wettability of ceramic particles with liquid metals. Therefore, as the first step, it is necessary to produce the diffusion zone at the interface by heating the system and maintaining it at a preset temperature to develop the cohesive zone model of the interface using mode I and mode II fracture tests. This study uses the molecular dynamics method to study the interdiffusion at the interface of α-Al2O3/AlSi12. The hexagonal crystal structure of aluminum oxide with the Al- and O-terminated interfaces with AlSi12 are considered. A single diffusion couple is used for each system to determine the average main and cross ternary interdiffusion coefficients. In addition, the effect of temperature and the termination type on the interdiffusion coefficients is examined. The results demonstrate that the thickness of the interdiffusion zone is proportional to the annealing temperature and time, and Al- and O-terminated interfaces exhibit similar interdiffusion properties.

One of the commonly accepted approaches to estimate protein–protein interactions (PPI) in aqueous solutions is the analysis of their translational diffusion. The present review article observes a phenomenological approach to analyze PPI effects via concentration dependencies of self- and collective translational diffusion coefficient for several spheroidal proteins derived from the pulsed field gradient NMR (PFG NMR) and dynamic light scattering (DLS), respectively. These proteins are rigid globular α-chymotrypsin (ChTr) and human serum albumin (HSA), and partly disordered α-casein (α-CN) and β-lactoglobulin (β-Lg). The PPI analysis enabled us to reveal the dominance of intermolecular repulsion at low ionic strength of solution (0.003–0.01 M) for all studied proteins. The increase in the ionic strength to 0.1–1.0 M leads to the screening of protein charges, resulting in the decrease of the protein electrostatic potential. The increase of the van der Waals potential for ChTr and α-CN characterizes their propensity towards unstable weak attractive interactions. The decrease of van der Waals interactions for β-Lg is probably associated with the formation of stable oligomers by this protein. The PPI, estimated with the help of interaction potential and idealized spherical molecular geometry, are in good agreement with experimental data. [ABSTRACT FROM AUTHOR]

NMR spectroscopic literature data are used, in a wide temperature-pressure range (180–350 K and 0.1–400 MPa), to study the water polymorphism and the validity of the liquid–liquid transition (LLT) hypothesis. We have considered the self-diffusion coefficient DS and the reorientational correlation time τθ (obtained from spin-lattice T1 relaxation times), measured, respectively, in bulk and emulsion liquid water from the stable to well inside the metastable supercooled region. As an effect of the hydrogen bond (HB) networking, the isobars of both these transport functions evolve with T by changing by several orders of magnitude, whereas their pressure dependence become more and more pronounced at lower temperatures. Both these transport functions were then studied according to the Adam–Gibbs model, typical of glass forming liquids, obtaining the water configurational entropy and the corresponding specific heat contribution. The comparison of the evaluated CP,conf isobars with the experimentally measured water specific heat reveals the full consistency of this analysis. In particular, the observed CP,conf maxima and its diverging behaviors clearly reveals the presence of the LLT and with a reasonable approximation the liquid–liquid critical point (LLCP) locus in the phase diagram.

Transport diffusion of the quaternary mixture cyclohexane + toluene + acetone + methanol and most of its binary and ternary subsystems at ambient conditions of temperature and pressure is studied by molecular dynamics simulation. Liquid-liquid phase separation of cyclohexane + methanol extends into the according ternary and quaternary mixtures, rendering them highly non-ideal. Maxwell-Stefan diffusion and intradiffusion coefficients are predicted with the Green-Kubo formalism, while the thermodynamic factor is sampled with Kirkwood-Buff integration. From these data, the Fick diffusion coefficient is determined and thoroughly compared with experimental literature values. Given that the simulation results for all binaries, one of the ternaries and the quaternary mixture are successfully validated, it can be assumed that the results for the three remaining ternary mixtures, for which no experimental data exist, are sound, too. These three ternaries exhibit non-idealities that entail Maxwell-Stefan diffusion coefficient maxima that are accompanied by Fick diffusion coefficient minima due to the thermodynamic factor. Particularly the very different nature of cyclohexane and methanol molecules, where the latter strongly self-associate, leads to anomalies. The predictive models by Li et al. (2001) and Allie-Ebrahim et al. (2018) for binary and ternary mixtures, respectively, are found to yield good results for predicting the Fick diffusion coefficient of these challenging systems.   • Diffusion of cyclohexane + toluene + acetone + methanol is studied by molecular dynamics. • Fick diffusion coefficient data are validated with experimental literature values. • Strong nonidealities entail Maxwell-Stefan and Fick diffusion coefficient extrema. • Anomalies are primarily driven by the different nature of cyclohexane and methanol. [ABSTRACT FROM AUTHOR]

The distribution of individual water molecules' self-diffusivities in adsorbed layers at TiO2 surfaces anatase (101) and rutile (110) have been determined at 300 K for inner and outer adsorbed layers, via classical molecular-dynamics methods. The layered-water structure has been identified and classified in layers making use of local order parameters, which proved to be an equally valid method of "self-ordering" molecules in layers. Significant distinctness was observed between anatase and rutile in disturbing these molecular distributions, more specifically in the adsorbed outer layer. Anatase (101) presented significantly higher values of self-diffusivity, presumably due to its "corrugated" structure that allows more hydrogen bonding interaction with adsorbed molecules beyond the first hydration layer. On the contrary, rutile (110) has adsorbed water molecules more securely "trapped" in the region between Ob atoms, resulting in less mobile adsorbed layers. [ABSTRACT FROM AUTHOR]

We focus on a system of two competing species with cross-diffusion and self-diffusion. By constructing an appropriate auxiliary function, we derive the sufficient conditions such that there are no coexisting steady-state solutions to the model. It is worth noting that the auxiliary function constructed above is applicable to Dirichlet, Neumann and Robin boundary conditions.

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities' metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability. [ABSTRACT FROM AUTHOR]

An in-depth understanding of the diffusion process in liquid metals is a key to design and engineering new high-performance materials. In this study, using molecular dynamics simulations supplemented with the embedded atom potential, we investigate/compare the self-diffusion process in liquid Aluminium. To understand the self-diffusion process, we analyse the radial distribution functions, velocity distributions, mean square displacements, and self-diffusion coefficients at various temperatures well above the melting temperature of Aluminium in the temperature range of 1000 K to 1800 K. As a key result, in both the α and β phases, the self-diffusion coefficients show a non-linear variation with rise in temperatures in the range of 1000 K to 1200 K. From 1300 K to 1800 K, the self-diffusion coefficients increase more or less monotonically with rise in temperature. We found that a higher temperature in the range of 1300 K to 1800 K leads to a greater self-diffusion coefficient, suggesting the more violent movement of the atoms around their equilibrium positions. The results presented in this work can help to understand the differences in the self-diffusion process in the technologically relevant Al phases. [ABSTRACT FROM AUTHOR]

The self-diffusion of components of such binary mixtures as dimethyl sulfoxide (DMSO) – chloroform and acetone – chloroform has been investigated by pulsed gradient spin-echo nuclear magnetic resonance (PGSE NMR) method and molecular dynamics (MD) simulation at ambient conditions. The self-diffusion coefficients obtained by the experimental and theoretical methods are well agreed with each other. Detailed analysis of structural characteristics (fraction of molecules with i hydrogen bonds, the average number of hydrogen bonds), as well as interaction energy in hydrogen-bonded complexes of DMSO – DMSO, DMSO – chloroform, acetone – acetone, acetone – chloroform made it possible to estimate the influence of the self- and heteroassociation on the self-diffusion of the mixture components. A set of methods showed that it is necessary to take into account not only heteroassociates with a composition of 1:1 just like acetone – chloroform mixture for sufficient approximation of the experimental data for DMSO – chloroform mixture. It has been established that for cosolvents (DMSO and acetone) with a similar molecular structure, a different behavior of concentration dependences of the self-diffusion coefficients in binary systems is connected with the features of self-association and heteroassociation.

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

The present work focused on the experimental study of the specific features of self-diffusion of tridecane molecules in macroporous kaolinite, which is used as a raw material for the production of chemically inert membranes. The measurements of self-diffusion coefficients by pulsed magnetic field gradient nuclear magnetic resonance (PMFG NMR) revealed an increased translational mobility of tridecane molecules in kaolinite with incomplete filling of the pore space. This effect was accompanied by a sharp change in the slope of the Arrhenius plot of the self-diffusion coefficients of tridecane molecules in kaolinite. An analysis of the diffusion spin echo decay in the tridecane–kaolinite system revealed a discrepancy between the experimental data and the theoretical predictions, considering the effect of the geometry of porous space on molecular mobility. It was shown that the experimental results could be interpreted in terms of a model of two phases of tridecane molecules in the pores of kaolinite, in the gaseous and adsorbed state, coexisting under the fast-exchange conditions. Within the framework of the model, the activation energies of self-diffusion were calculated, which agreed satisfactorily with the experimental data. Additionally, the effects of the internal magnetic field gradients arising in a porous medium loaded with a gas or liquid on the data of the PFG NMR measurements were calculated. It was shown that the effect of magnetic field inhomogeneities on the measured self-diffusion coefficients of tridecane in kaolinite is small and could be neglected.

The evaluation of an oil’s oxidation status during industrial production is highly important for monitoring the oil’s purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil’s material characterization. Towards this goal, a 1H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a 1H LF-NMR relaxation curve, and its reconstruction into T1 and T2 spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T2 relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T2 signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.

Mixed conducting cobaltites PrBaCo2−xFexO6−δ (x = 0–0.6) with a double perovskite structure are promising materials for ceramic semi-permeable membranes for oxygen separation and purification due to their fast oxygen exchange and diffusion capability. Here, we report the results of the detailed study of an interplay between the defect chemistry, oxygen nonstoichiometry and oxygen transport in these materials as a function of iron doping. We show that doping leads to a systematic variation of both the thermodynamics of defect formation reactions and oxygen transport properties. Thus, iron doping can be used to optimize the performance of mixed conducting oxygen-permeable double perovskite membrane materials.

One primary concern in colloid science is understanding the relationship of its macroscopic rheology and diffusion behavior with the observed microscopic arrangements of the nanoparticles in the fluid. This manuscript addresses the study of these dynamical properties through a first-principle stochastic method. Both properties directly relate to the observed fluid structure factor, which depends on a few known material parameters. However, in the literature, this static quantity is reported up to the first prominent peak of its small momentum transfer of the scattered radiation, leading to inaccurate determination of the transport properties. Here, it is proposed to use the rescaled mean spherical approximation under the requirement of fitting the experimental data of the structure beyond the dependence of more significant wave numbers. The predicted viscosity agrees with the observed ones at a low volume fraction of particles for ferrofluids dispersed in polymer solvents. This rheological quantity is inversely related to the self-diffusion coefficient of a tracer particle.

The food quality of edible oils is dependent on basic chemical and structural changes that can occur by oxidation during preparation and storage. A rapid and efficient analytical method of the different steps of oil oxidation is described using a time-domain nuclear magnetic resonance (TD-NMR) sensor for measuring signals related to the chemical and physical properties of the oil. The degree of thermal oxidation of edible oils at 80 °C was measured by the conventional methodologies of peroxide and aldehyde analysis. Intact non-modified samples of the same oils were more rapidly analyzed for oxidation using a TD-NMR sensor for 2D T1-T2 and self-diffusion (D) measurements. A good linear correlation between the D values and the conventional chemical analysis was achieved, with the highest correlation of R2 = 0.8536 for the D vs. the aldehyde concentrations during the thermal oxidation of poly-unsaturated linseed oils, the oil most susceptible to oxidation. A good correlation between the D and aldehyde levels was also achieved for all the other oils. The possibility to simplify and minimize the time of oxidative analysis using the TD NMR sensors D values is discussed as an indicator of the oil’s oxidation quality, as a rapid and accurate methodology for the oil industry.

2H solid-state NMR represents a reliable method for probing the dynamics of the molecules in confined geometries, including microporous and mesoporous materials such as zeolites or metal–organic frameworks (MOFs). Dynamic characteristics derived from analyses of 2H NMR line shape and spin relaxation for adsorbed deuterated molecules can be related with translational motions along the porous framework. This opens up the possibility to use 2H NMR for probing self-diffusion within porous materials. Herein we discuss the basic principles and approaches that can be used to derive the information on diffusion coefficients based on analysis of the dynamics of confined molecules by 2H NMR. We discuss the corresponding possibilities and limitations of 2H NMR compared to other experimental methods, such QENS and PFG NMR. Several examples for estimation of diffusion coefficients by 2H NMR for alkanes, olefins, alcohol, benzene, pyridine in zeolites and MOFs are provided. [ABSTRACT FROM AUTHOR]

A model of self-diffusion in confined water is proposed. It is based on the idea that confined water forms structures similar to hexagonal ice and its transport characteristics can be described in the framework of a quasiparticle approach. It is assumed that the quasiparticles responsible for self-diffusion are D- and L-bond defects, which in the case of confined water are interstitial H2O molecules and vacancies in an ice-like lattice. The process of D-defect migration is described as the diffusion of an ideal gas through a porous medium. The analytical expression for the self-diffusion coefficient of confined water depending on the water content quantitatively well describes the experimental data for Nafion-type of proton-exchange membranes. [ABSTRACT FROM AUTHOR]

Kirkendall effect has played an important role in developing the understanding of atomic diffusion and mass transport in solids. In thin films and multilayers, in which the microstructure may be far from thermal equilibrium, behavior of a system can be very different from that of the bulk. Therefore, there is a great need to understand the peculiarities of Kirkendall shift occurring at nanometer or sub-nanometer length scale in such systems. In the present work, Kirkendall shift in thin films has been measured with sub-nanometer accuracy, by making use of x-ray planar waveguide structures. Using this technique, we report an apparently counterintuitive result of Kirkendall shift associated with self-diffusion in nanocrystalline iron. The cavity of the waveguide consists of two layers of iron having different microstructures, separated by a thin marker layer of Ag. Thermal annealing results in shift of the Ag marker towards the Fe layer having a coarser grain structure. The effect is established unambiguously by studying two samples with the order of the two Fe layers reversed, and a third sample with the two Fe layers having the same microstructure. The result can be understood in terms of the difference in the microstructure of the two Fe layers; difference in the grain size and hence different concentration of the grain boundaries in the two layers results in a net mass transfer from one region to the other. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract The aim of this project is studying the effect of thermal operation parameters on the graphitization of self-diffused ethane-based catalytic coke. The novelty of this study refers to self-diffused metals that had given unique properties to the catalytic coke and had improved the graphitization degree at low temperatures. The main feature of this research is presenting a remarkable energy saving approach that uses low-cost installations for production of graphitized carbon. The experiments were performed in two steps including preparation of self-diffused ethane-based catalytic coke and then low-temperature graphitization of coke samples below 1500 °C. Characteristic tests were performed by determination of electrical resistivity and XRD pattern of graphitized samples including graphitization degree, aromaticity, coke rank, number of carbon rings, graphene thickness and length. The results revealed that the blanket atmosphere, final temperature and exposure time had the greatest impact on the aforementioned criteria, while the role of thermal ramp and sulfur content of catalytic coke was negligible. The electrical resistivity tests on the graphitized sample showed how the electrical resistivity of graphitized samples is a function of graphitization degree.

The problem of friction, wear of machine parts and cutting tools, the need to increase the life of machines makes the task of creating new high-performance, energy-saving technologies of hardening one of the main in engineering. In mechanical engineering, the problem of improving the physical, mechanical and operational properties in thin surface layers (~10 microns) of such parts as shafts, gears, measuring tools, drills, cylinders of internal combustion engines, etc. is important. Currently, these tasks are often solved by applying reinforcing coatings. Cementation is economical. The technology of cemented steels smelting, which is currently used by metallurgical plants, does not ensure stable production of fine-grained structure in products. At the same time, the capacity of machines and units are growing, and to obtain parts that can withstand higher loads while maintaining their overall dimensions, new steel grades are needed. The complexity of the configuration of gears with a thin tooth and a massive sleeve, and the need for minimal warping make heat treatment as difficult and responsible as a complex tool, such as shaped cutters. Therefore, for the manufacture of gears you want to assign steel with small hereditary grain size. In this article we will focus on the development and production of cemented steel in the conditions of OJSC «BSW – Management Company of Holding «BMC», in particular, the steel brand 16MnCrS5 commissioned by the European manufacturer of gearboxes and motors. To determine the grain size of the metal must be subjected to special types of processing for the manifestation of certain characteristics. These results allow us to draw conclusions about the need to adjust the chemical composition of steel grade 16MnCrS5 by introducing a system of modifying elements, which will reduce the tendency of steel to overheating, therefore, reduce the size of austenitic grains. On the basis of the revealed regularities on the influence of carbide-forming elements, a further system of steel modification is determined, which includes a complex of elements V, Nb, Ti.