Viscosity model of deep eutectic solvents from group contribution method

In this work, based on mathematical model inspired by transition state theory, the group contribution (GC) method is used to predict the viscosity of DESs. The model is constrained by Eyring rate theory and hard sphere free volume theory. A dataset of 2229 experimental measurements of the viscosity of 183 DESs from literature is used for determining the model parameters and subsequent verification of the model. The rules introduced by this model are simple and easy to understand. The results show that the proposed model is able to predict the DESs viscosity with very high accuracy, i.e., with an average absolute relative deviation of 8.12% over the training set and 8.64% over the test set, using only temperature and composition as inputs. The maximum absolute relative deviation is 34.63%. Therefore, the as-proposed model can be considered a highly reliable tool for predicting DESs viscosity when experimental data are absent.

Study on Driver Gaze Characteristics in Sight Distance Limited Section of Mountain Highway Based on Visual Information

In order to study the gaze behavior characteristics of drivers in mountainous road sections with limited sight distance, the real vehicle test is carried out by using Smart Eye Pro 5.7 noninvasive eye tracker. Combined with the sight distance change rate theory, 6 typical test representative mountainous sections are selected to study the gaze distribution law and gaze duration of drivers in different mountainous sections. The research shows that when the driver drives on the test section with the most unfavorable sight distance of 44 m, 50 m, and 56 m, the fixation characteristics of “from far to near” are significant, and the long fixation duration accounts for a large proportion of the driver. When the driver drives on the section with the most unfavorable sight distance of more than 70 m, i.e., the sight distance change rate of less than 1.33, the fixation characteristics of “from far to near” disappear. The driver’s fixation stability increases, the fixation freedom increases, and the proportion of medium and long fixation duration decreases. The data analysis provides a theoretical basis for drivers to pass safely in mountainous sections.

Dynamical Models of Chemical Exchange in Nuclear Magnetic Resonance Spectroscopy

ABSTRACT Chemical exchange line broadening is an important phenomenon in nuclear magnetic resonance (NMR) spectroscopy, in which a nuclear spin experiences more than one magnetic environment as a result of chemical or conformational changes of a molecule. The dynamic process of chemical exchange strongly affects the sensitivity and resolution of NMR experiments and increasingly provides a powerful probe of the interconversion between chemical and conformational states of proteins, nucleic acids, and other biologic macromolecules. A simple and often used theoretic description of chemical exchange in NMR spectroscopy is based on an idealized 2-state jump model (the random phase or telegraph signal). However, chemical exchange can also be represented as a barrier crossing event that can be modeled by using chemical reaction rate theory. The timescale of crossing is determined by the barrier height, the temperature, and the dissipation modeled as collisional or frictional damping. This tutorial explores the connection between the NMR theory of chemical exchange line broadening and strong collision models for chemical kinetics in statistical mechanics. Theoretic modeling and numeric simulation are used to map the rate of barrier crossing dynamics of a particle on a potential energy surface to the chemical exchange relaxation rate constant. By developing explicit models for the exchange dynamics, the tutorial aims to elucidate the underlying dynamical processes that give rise to the rich phenomenology of chemical exchange observed in NMR spectroscopy. Software for generating and analyzing the numeric simulations is provided in the form of Python and Fortran source codes.

Microstructure-dependent rate theory model of defect segregation and phase stability in irradiated polycrystalline LiAlO2

Gamma lithium aluminate (LiAlO2) is a breeder material for tritium and is one of key components in a tritium-producing burnable absorber rod (TPBAR). Dissolution and precipitation of second phases such as LiAl5O8 and voids are observed in irradiated LiAlO2. Such microstructure changes cause the degradation of thermomechanical properties of LiAlO2 and affect tritium retention and release kinetics, and hence, the TPBAR performance. In this work, a microstructure-dependent model of radiation-induced segregation (RIS) has been developed for investigating the accumulation of species and phase stability in polycrystalline LiAlO2 structures under irradiation. Three sublattices (i.e., [Li, Al, V]I [O, Vo]II [Lii, Ali, Oi, Vi]III), and concentrations of six diffusive species (i.e., Li; vacancy of Li or Al at [Li, Al, V]I sublattice, O vacancy at [O, Vo]II sublattice, and Li, Al and O interstitials at [Lii, Ali, Oi, Vi]III interstitial sublattices; are used to describe spatial and temporal distributions of defects and chemistry. Microstructure-dependent thermodynamic and kinetic properties including the generation, reaction, and chemical potentials of defects and defect mobility are taken into account in the model. The parametric studies demonstrated the capability of the developed RIS model to assess the effect of thermodynamic and kinetic properties of defects on the segregation and depletion of species in polycrystalline structures and to explain the phase stability observed in irradiated LiAlO2 samples. The developed RIS model will be extended to study the precipitation of LiAl5O8 and voids and tritium retention by integrating the phase-field method.

Adiabatic versus non-adiabatic electron transfer at 2D electrode materials

Abstract2D electrode materials are often deployed on conductive supports for electrochemistry and there is a great need to understand fundamental electrochemical processes in this electrode configuration. Here, an integrated experimental-theoretical approach is used to resolve the key electronic interactions in outer-sphere electron transfer (OS-ET), a cornerstone elementary electrochemical reaction, at graphene as-grown on a copper electrode. Using scanning electrochemical cell microscopy, and co-located structural microscopy, the classical hexaamineruthenium (III/II) couple shows the ET kinetics trend: monolayer > bilayer > multilayer graphene. This trend is rationalized quantitatively through the development of rate theory, using the Schmickler-Newns-Anderson model Hamiltonian for ET, with the explicit incorporation of electrostatic interactions in the double layer, and parameterized using constant potential density functional theory calculations. The ET mechanism is predominantly adiabatic; the addition of subsequent graphene layers increases the contact potential, producing an increase in the effective barrier to ET at the electrode/electrolyte interface.

Kinetic Properties of Microbial Exoenzymes Vary With Soil Depth but Have Similar Temperature Sensitivities Through the Soil Profile

Current knowledge of the mechanisms driving soil organic matter (SOM) turnover and responses to warming is mainly limited to surface soils, although over 50% of global soil carbon is contained in subsoils. Deep soils have different physicochemical properties, nutrient inputs, and microbiomes, which may harbor distinct functional traits and lead to different SOM dynamics and temperature responses. We hypothesized that kinetic and thermal properties of soil exoenzymes, which mediate SOM depolymerization, vary with soil depth, reflecting microbial adaptation to distinct substrate and temperature regimes. We determined the Michaelis-Menten (MM) kinetics of three ubiquitous enzymes involved in carbon (C), nitrogen (N) and phosphorus (P) acquisition at six soil depths down to 90 cm at a temperate forest, and their temperature sensitivity based on Arrhenius/Q10 and Macromolecular Rate Theory (MMRT) models over six temperatures between 4–50°C. Maximal enzyme velocity (Vmax) decreased strongly with depth for all enzymes, both on a dry soil mass and a microbial biomass C basis, whereas their affinities increased, indicating adaptation to lower substrate availability. Surprisingly, microbial biomass-specific catalytic efficiencies also decreased with depth, except for the P-acquiring enzyme, indicating distinct nutrient demands at depth relative to microbial abundance. These results suggested that deep soil microbiomes encode enzymes with intrinsically lower turnover and/or produce less enzymes per cell, reflecting distinct life strategies. The relative kinetics between different enzymes also varied with depth, suggesting an increase in relative P demand with depth, or that phosphatases may be involved in C acquisition. Vmax and catalytic efficiency increased consistently with temperature for all enzymes, leading to overall higher SOM-decomposition potential, but enzyme temperature sensitivity was similar at all depths and between enzymes, based on both Arrhenius/Q10 and MMRT models. In a few cases, however, temperature affected differently the kinetic properties of distinct enzymes at discrete depths, suggesting that it may alter the relative depolymerization of different compounds. We show that soil exoenzyme kinetics may reflect intrinsic traits of microbiomes adapted to distinct soil depths, although their temperature sensitivity is remarkably uniform. These results improve our understanding of critical mechanisms underlying SOM dynamics and responses to changing temperatures through the soil profile.

A Rate Theory Model of Radiation-Induced Swelling in an Austenitic Stainless Steel

Many rate theory models of cavity (void) swelling have been published over the past 50 years, all having the same, or similar, structures. A rigorous validation of the models has not been possible because of the dearth of information concerning the microstructures that correspond with the swelling data. Whereas the lack of microstructure information is still an issue for historical swelling data, in the past 10–20 years data have been published on the evolution of the microstructure (point defect yields from collision cascades, cavity number densities, and dislocation densities/yield strengths) allowing certain gaps in information to be filled when considering historic swelling data. With reasonable estimates of key microstructure parameters, a standard rate theory model can be applied, and the model parameter space explored, in connection with historical swelling data. By using published data on: (i) yield strength as a function of dose and temperature (to establish an empirical expression for dislocation density evolution); (ii) cavity number densities as a function of temperature; and (iii) freely migrating defect (FMD) production as a function of primary knock-on atom (PKA) spectrum, the necessary parameter and microstructure inputs that were previously unknown can be used in model development. This paper describes a rate-theory model for void swelling of 316 stainless steel irradiated in the EBR-2 reactor as a function of irradiation temperature and neutron dose.

Potassium carbonate-based ternary transition temperature mixture (deep eutectic analogues) for CO2 absorption: Characterizations and DFT analysis

Is it possible to improve CO2 solubility in potassium carbonate (K2CO3)-based transition temperature mixtures (TTMs)? To assess this possibility, a ternary transition-temperature mixture (TTTM) was prepared by using a hindered amine, 2-amino-2-methyl-1,3-propanediol (AMPD). Fourier transform infrared spectroscopy (FT-IR) was employed to detect the functional groups including hydroxyl, amine, carbonate ion, and aliphatic functional groups in the prepared solvents. From thermogravimetric analysis (TGA), it was found that the addition of AMPD to the binary mixture can increase the thermal stability of TTTM. The viscosity findings showed that TTTM has a higher viscosity than TTM while their difference was decreased by increasing temperature. In addition, Eyring’s absolute rate theory was used to compute the activation parameters (∆G*, ∆H*, and ∆S*). The CO2 solubility in liquids was measured at a temperature of 303.15 K and pressures up to 1.8 MPa. The results disclosed that the CO2 solubility of TTTM was improved by the addition of AMPD. At the pressure of about 1.8 MPa, the CO2 mole fractions of TTM and TTTM were 0.1697 and 0.2022, respectively. To confirm the experimental data, density functional theory (DFT) was employed. From the DFT analysis, it was found that the TTTM + CO2 system has higher interaction energy (|∆E|) than the TTM + CO2 system indicating the higher CO2 affinity of the former system. This study might help scientists to better understand and to improve CO2 solubility in these types of solvents by choosing a suitable amine as HBD and finding the best combination of HBA and HBD.