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Molecular dynamics (MD) simulations are used to investigate $^1$H nuclear magnetic resonance (NMR) relaxation and diffusion of bulk $n$-C$_5$H$_{12}$ to $n$-C$_{17}$H$_{36}$ hydrocarbons and bulk water. The MD simulations of the $^1$H NMR relaxation times $T_{1,2}$ in the fast motion regime where $T_1 = T_2$ agree with measured (de-oxygenated) $T_2$ data at ambient conditions, without any adjustable parameters in the interpretation of the simulation data. Likewise, the translational diffusion $D_T$ coefficients calculated using simulation configurations are well-correlated with measured diffusion data at ambient conditions. The agreement between the predicted and experimentally measured NMR relaxation times and diffusion coefficient also validate the forcefields used in the simulation. The molecular simulations naturally separate intramolecular from intermolecular dipole-dipole interactions helping bring new insight into the two NMR relaxation mechanisms as a function of molecular chain-length (i.e. carbon number). Comparison of the MD simulation results of the two relaxation mechanisms with traditional hard-sphere models used in interpreting NMR data reveals important limitations in the latter. With increasing chain length, there is substantial deviation in the molecular size inferred on the basis of the radius of gyration from simulation and the fitted hard-sphere radii required to rationalize the relaxation times. This deviation is characteristic of the local nature of the NMR measurement, one that is well-captured by molecular simulations.
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Atomistic molecular dynamics simulations are used to investigate $^1$H NMR $T_1$ relaxation of water from paramagnetic Gd$^{3+}$ ions in solution at 25$^{circ}$C. Simulations of the $T_1$ relaxivity dispersion function $r_1$ computed from the Gd$^{3+}$--$^1$H dipole--dipole autocorrelation function agree within $simeq 8$% of measurements in the range $f_0 simeq $ 5 $leftrightarrow$ 500 MHz, without any adjustable parameters in the interpretation of the simulations, and without any relaxation models. The simulation results are discussed in the context of the Solomon-Bloembergen-Morgan inner-sphere relaxation model, and the Hwang-Freed outer-sphere relaxation model. Below $f_0 lesssim $ 5 MHz, the simulation overestimates $r_1$ compared to measurements, which is used to estimate the zero-field electron-spin relaxation time. The simulations show potential for predicting $r_1$ at high frequencies in chelated Gd$^{3+}$ contrast-agents used for clinical MRI.
It is well known that water inside hydrophobic nano-channels diffuses faster than bulk water. Recent theoretical studies have shown that this enhancement depends on the size of the hydrophobic nanochannels. However, experimental evidence of this dependence is lacking. Here, by combining two-dimensional Nuclear Magnetic Resonance (NMR) diffusion-relaxation D-T2eff spectroscopy in the stray field of a superconducting magnet, and Molecular Dynamics (MD) simulations, we analyze the size dependence of water dynamics inside carbon nanotubes (CNTs) of different diameters (1.1 nm to 6.0 nm), in the temperature range of 265K to 305K. Depending on the CNTs diameter, the nanotube water is shown to resolve in two or more tubular components acquiring different self-diffusion coefficients. Most notable, a favourable CNTs diameter range 3.0-4.5 nm is experimentally verified for the first time, in which water molecule dynamics at the centre of the CNTs exhibit distinctly non-Arrhenius behaviour, characterized by ultrafast diffusion and extraordinary fragility, a result of significant importance in the efforts to understand water behaviour in hydrophobic nanochannels.
The mechanism behind the $^1$H NMR frequency dependence of $T_1$ and the viscosity dependence of $T_2$ for polydisperse polymers and bitumen remains elusive. We elucidate the matter through NMR relaxation measurements of polydisperse polymers over an extended range of frequencies ($f_0 = 0.01 leftrightarrow$ 400 MHz) and viscosities ($eta = 385 leftrightarrow 102,000$ cP) using $T_{1}$ and $T_2$ in static fields, $T_{1}$ field-cycling relaxometry, and $T_{1rho}$ in the rotating frame. We account for the anomalous behavior of the log-mean relaxation times $T_{1LM} propto f_0$ and $T_{2LM} propto (eta/T)^{-1/2}$ with a phenomenological model of $^1$H-$^1$H dipole-dipole relaxation which includes a distribution in molecular correlation times and internal motions of the non-rigid polymer branches. We show that the model also accounts for the anomalous $T_{1LM}$ and $T_{2LM}$ in previously reported bitumen measurements. We find that molecular dynamics (MD) simulations of the $T_{1} propto f_0$ dispersion and $T_2$ of similar polymers simulated over a range of viscosities ($eta = 1 leftrightarrow 1,000$ cP) are in good agreement with measurements and the model. The $T_{1} propto f_0$ dispersion at high viscosities agrees with previously reported MD simulations of heptane confined in a polymer matrix, which suggests a common NMR relaxation mechanism between viscous polydisperse fluids and fluids under confinement, without the need to invoke paramagnetism.
This paper proposes an effective diffusion equation method to analyze nuclear magnetic resonance (NMR) relaxation. NMR relaxation is a spin system recovery process, where the evolution of the spin system is affected by the random field due to Hamiltonians, such as dipolar couplings. The evolution of magnetization can be treated as a random walk in phase space described either by a normal or fractional phase diffusion equation. Based on these phase diffusion equations, the NMR relaxation rates and equations can be obtained, exemplified in the analysis of relaxations affected by an arbitrary random field, and by dipolar coupling for both like and unlike spins. The obtained theoretical results are consistent with the reported results in the literature. Additionally, the anomalous relaxation expression obtained from the Mittag-Leffler function based time correlation function can successfully fit the previously reported 13C T1 NMR experimental data of polyisobutylene (PIB) in the blend of PIB and head-to-head poly(propylene) (hhPP). Furthermore, the proposed phase diffusion approach provides an intuitive way to interpret NMR relaxation, particularly for the fractional NMR relaxation, which is still a challenge to explain by the available theoretical methods. The paper provides additional insights into NMR and magnetic resonance imaging (MRI) relaxation experiments.
The ultraviolet (UV) photodissociation of amorphous water ice at different ice temperatures is investigated using molecular dynamics (MD) simulations and analytical potentials. Previous MD calculations of UV photodissociation of amorphous and crystalline water ice at 10 K [S. Andersson et al., J. Chem. Phys. 124, 064715 (2006)] revealed -for both types of ice- that H atom, OH, and H2O desorption are the most important processes after photoexcitation in the uppermost layers of the ice. Water desorption takes place either by direct desorption of recombined water, or when, after dissociation, an H atom transfers part of its kinetic energy to one of the surrounding water molecules which is thereby kicked out from the ice. We present results of MD simulations of UV photodissociation of amorphous ice at 10, 20, 30, and 90 K in order to analyze the effect of ice temperature on UV photodissociation processes. Desorption and trapping probabilities are calculated for photoexcitation of H2O in the top four monolayers and the main conclusions are in agreement with the 10 K results: desorption dominates in the top layers, while trapping occurs deeper in the ice. The hydrogen atom photodesorption probability does not depend on ice temperature, but OH and H2O photodesorption probabilities tend to increase slightly (~30%) with ice temperature. We have compared the total photodesorption probability (OH+H2O) with the experimental total photodesorption yield, and in both cases the probabilities rise smoothly with ice temperature. The experimental yield is on average 3.8 times larger than our theoretical results, which can be explained by the different time scales studied and the approximations in our model.
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