Diffusion in dense supercritical methane from quasi-elastic neutron scattering measurements

Crossover from gas-like to liquid-like molecular diffusion in a simple supercritical fluid

According to textbooks, no physical observable can be discerned allowing to distinguish a liquid from a gas beyond the critical point. Yet, several proposals have been put forward challenging this view and various transition boundaries between a gas-like and a liquid-like behaviour, including the so-called Widom and Frenkel lines, and percolation line, have been suggested to delineate the supercritical state space. Here we report observation of a crossover from gas-like (Gaussian) to liquid-like (Lorentzian) self-dynamic structure factor by incoherent quasi-elastic neutron scattering measurements on supercritical fluid methane as a function of pressure, along the 200 K isotherm. The molecular self-diffusion coefficient was derived from the best Gaussian (at low pressures) or Lorentzian (at high pressures) fits to the neutron spectra. The Gaussian-to-Lorentzian crossover is progressive and takes place at about the Widom line intercept (59 bar). At considerably higher pressures, a liquid-like jump diffusion mechanism properly describes the supercritical fluid on both sides of the Frenkel line. The present observation of a gas-like to liquid-like crossover in the self dynamics of a simple supercritical fluid confirms emerging views on the unexpectedly complex physics of the supercritical state, and could have planet-wide implications and possible industrial applications in green chemistry.

Freezing density scaling of transport coefficients in the Weeks-Chandler-Andersen fluid

It is shown that the transport coefficients (self-diffusion, shear viscosity, and thermal conductivity) of the Weeks-Chandler-Andersen (WCA) fluid along isotherms exhibit a freezing density scaling (FDS). The functional form of this FDS is essentially the same or closely related to those in the Lennard-Jones fluid, hard-sphere fluid, and some liquefied noble gases. This proves that this FDS represents a quasi-universal corresponding state principle for simple classical fluids with steep interactions. Some related aspects, such as a Stokes-Einstein relation without a hydrodynamic diameter and gas-to-liquid dynamical crossover, are briefly discussed. Simple fitting formulas for the transport coefficients of the dense WCA fluid are suggested.

System Size Dependence of the Diffusion Coefficients in MD Simulations: A Simple Correction Formula for Pure Dense Fluids

A practical correction formula relating the self-diffusion coefficient of dense liquids from molecular dynamics (MD) simulations with periodic boundary conditions to the self-diffusion coefficient in the thermodynamic limit is discussed. This formula applies to pure dense fluids and has a very simple form D = D0(1 - γN-1/3), where D0 is the self-diffusion coefficient in the thermodynamic limit and N is the number of particles in the simulation. The numerical factor γ is dependent on the geometry of the simulation cell. Remarkably, γ ≃ 1.0 is the most popular cubic geometry. The success of this formula is supported by results from MD simulations, including very recent simulations with a "magic" simulation geometry.

A versatile pressure-cell design for studying ultrafast molecular-dynamics in supercritical fluids using coherent multi-pulse x-ray scattering

Supercritical fluids (SCFs) can be found in a variety of environmental and industrial processes. They exhibit an anomalous thermodynamic behavior, which originates from their fluctuating heterogeneous micro-structure. Characterizing the dynamics of these fluids at high temperature and high pressure with nanometer spatial and picosecond temporal resolution has been very challenging. The advent of hard x-ray free electron lasers has enabled the development of novel multi-pulse ultrafast x-ray scattering techniques, such as x-ray photon correlation spectroscopy (XPCS) and x-ray pump x-ray probe (XPXP). These techniques offer new opportunities for resolving the ultrafast microscopic behavior in SCFs at unprecedented spatiotemporal resolution, unraveling the dynamics of their micro-structure. However, harnessing these capabilities requires a bespoke high-pressure and high-temperature sample system that is optimized to maximize signal intensity and address instrument-specific challenges, such as drift in beamline components, x-ray scattering background, and multi-x-ray-beam overlap. We present a pressure cell compatible with a wide range of SCFs with built-in optical access for XPCS and XPXP and discuss critical aspects of the pressure cell design, with a particular focus on the design optimization for XPCS.

Noncanonical Relationship between Heterogeneity and the Stokes-Einstein Breakdown in Deep Eutectic Solvents

The relationship between Stokes-Einstein breakdown (SEB) and dynamical heterogeneity (DH) is of paramount importance in the physical chemistry of complex fluids. In this work, we employ neutron scattering to probe the DH and SEB in a series of deep eutectic solvents (DESs) composed of acetamide and lithium salts. Quasielastic neutron scattering experiments reveal SEB in the jump diffusion of acetamide, represented by a fractional Stokes-Einstein relationship. Among these DESs, lithium perchlorate exhibits the most pronounced SEB while lithium bromide displays the weakest. Concurrently, elastic incoherent neutron scans identify that bromide DES is the most heterogeneous and perchlorate is the least. For the first time, our study unveils a counterintuitive incommensurate relationship between DH and SEB. Further, it reveals the intricate contrasting nature of the SEB-DH relationship when investigated in proximity to the glass-transition temperature and further away from it.

Combining Molecular Dynamics and Machine Learning to Analyze Shear Thinning for Alkane and Globular Lubricants in the Low Shear Regime

Lubricants with desirable frictional properties are important in achieving an energy-saving society. Lubricants at the interfaces of mechanical components are confined under high shear rates and pressures and behave quite differently from the bulk material. Computational approaches such as nonequilibrium molecular dynamics (NEMD) simulations have been performed to probe the molecular behavior of lubricants. However, the low-shear-velocity regions of the materials have rarely been simulated owing to the expensive calculations necessary to do so, and the molecular dynamics under shear velocities comparable with that in the experiments are not clearly understood. In this study, we performed NEMD simulations of extremely confined lubricants, i.e., two molecular layers for four types of lubricants confined in mica walls, under shear velocities from 0.001 to 1 m/s. While we confirmed shear thinning, the velocity profiles could not show the flow behavior when the shear velocity was much slower than thermal fluctuations. Therefore, we used an unsupervised machine learning approach to detect molecular movements that contribute to shear thinning. First, we extracted the simple features of molecular movements from large amounts of MD data, which were found to correlate with the effective viscosity. Subsequently, the extracted features were interpreted by examining the trajectories contributing to these features. The magnitude of diffusion corresponded to the viscosity, and the location of slips that varied depending on the spherical and chain lubricants was irrelevant. Finally, we attempted to apply a modified Stokes-Einstein relation at equilibrium to the nonequilibrium and confined systems. While systems with low shear rates obeyed the relation sufficiently, large deviations were observed under large shear rates.

Transfer-Free CVD Growth of High-Quality Wafer-Scale Graphene at 300 °C for Device Mass Fabrication

Direct chemical vapor deposition of graphene on semiconductors and insulators provides high feasibility for integration of graphene devices and semiconductor electronics. However, the current methods typically rely on high temperatures (>1000 °C), which can damage the substrates. Here, a growth method for high-quality large-area graphene at 300 °C is introduced. A multizone furnace with gradient temperature control was designed according to a computational fluid dynamics model. The crucial roles of the chamber pressure in the film continuity and hydrogen composition in the graphene defect density at low temperature were revealed. As a result, a uniform graphene film with the Raman ratio I_D/I_G = 0.08 was obtained. Furthermore, a technique of laminating single-crystal Cu foil as a sacrificial layer on the substrate was proposed to realize transfer-free growth, and a wafer-scale graphene transistor array was demonstrated with good performance consistency, which paves the way for mass fabrication of graphene devices.

Freezing density scaling of fluid transport properties: Application to liquified noble gases

A freezing density scaling of transport properties of the Lennard-Jones fluid is rationalized in terms of the Rosenfeld's excess entropy scaling and isomorph theory of Roskilde-simple systems. Then, it is demonstrated that the freezing density scaling operates reasonably well for viscosity and thermal conductivity coefficients of liquid argon, krypton, and xenon. Quasi-universality of the reduced transport coefficients at their minima and at freezing conditions is discussed. The magnitude of the thermal conductivity coefficient at the freezing point is shown to agree remarkably well with the prediction of the vibrational model of thermal transport in dense fluids.

Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China

Ambient concentrations of volatile organic compounds (VOCs) vary with emission rates, meteorology, and chemistry. Conventional positive matrix factorization (PMF) loses information because of dilution variations and chemical losses. Multiply improved PMF incorporates the ventilation coefficient, and total solar radiation or oxidants to reduce the effects of dispersion and chemical loss. These methods were applied to hourly speciated VOC data from November 2019 to March 2020 including during the COVID-19 shutdown. Various comparisons were made to assess the influences of these fluctuation drivers by time of day. Dispersion normalized PMF (DN-PMF) reduced the dispersion variations. Dispersion-radiation normalized PMF (DRN-PMF) reduced the impact of chemical loss, especially at night, which was better than Dispersion-O_x normalized PMF (DON-PMF). The conditional bivariate probability function (CBPF) plots of DRN-PMF results were consist with actual source locations. The DN-PMF, DRN-PMF, and DON-PMF results were consistent between 10:00 and 15:00, suggesting dispersion was significantly more influential than photochemical reactions during these times. The DRN-PMF results indicated that the highest VOC contributors during the COVID-19 shutdown were liquefied petroleum gas (LPG) (28.8%), natural gas (25.2%), and pulverized coal boilers emissions (19.6%). Except for petrochemical-related enterprises and LPG, the contribution concentrations of all other sources decreased substantially during the COVID-19 shutdown, by 94.7%, 90.6%, and 86.8% for vehicle emissions, gasoline evaporation, and the mixed source of diesel evaporation and solvent use, respectively. Controlling the use of motor vehicles and related volatilization of diesel fuel and gasoline can be effective in controlling VOCs in the future.