Molecular dynamics of interfacial crystallization of dodecane on hydroxylated silica surface impacted by H2O and CO2

The morphology of dodecane in a nanopore at temperatures typical in exploited or depleted oil reservoirs is investigated using molecular dynamics simulation. The dodecane morphology is found to be determined by interactions between interfacial crystallization and surface wetting of the simplified oil, while "evaporation" only plays a minor role. The morphology changes from an isolated, solidified dodecane droplet to a film with orderly lamellae structures remaining within, and finally to a film containing randomly distributed dodecane molecules, as the system temperature increases. In a nanoslit under the impact of water, since water wins against oil in surface wetting on the silica surface due to electrostatic interaction induced hydrogen bonding between water and the silanol group of silica, the spreading of dodecane molecules over the silica surface is impeded by this water confinement mechanism. Meanwhile, interfacial crystallization is enhanced, leading to always an isolated dodecane "droplet," with crystallization weakening as the temperature increases. Since dodecane is immiscible to water, there is no mechanism for dodecane to escape the silica surface, and the competition of surface wetting between water and oil determines the morphology of the crystallized dodecane droplet. For the CO2-dodecane system in a nanoslit, CO2 is an efficient solvent for dodecane at all temperatures. Therefore, interfacial crystallization rapidly disappears. The competition of surface adsorption between CO2 and dodecane is secondary for all cases. The dissolution mechanism is a clear clue for the fact that CO2 is more effective than water flooding in oil recovery for a depleted oil reservoir.

Sequence-to-Sequence Change-Point Detection in Single-Particle Trajectories via Recurrent Neural Network for Measuring Self-Diffusion

A recurrent neural network is developed for segmenting between anomalous and normal diffusion in single-particle trajectories. Accurate segmentation infers a distinct change point that is used to approximate an Einstein linear regime in the mean-squared displacement curve via the transition density function, a unique physical descriptor for short-lived and delayed transiency. Through several artificial and simulated scenarios, we demonstrate the compelling accuracy of our model for dissecting linear and nonlinear behaviour. The inherent practicality of our model lies in its ability to substantiate the self-diffusion coefficient through offline trajectory segmentation, which is opposed to the common ‘best-guess’ linear fitting standard. Additionally, we show that the transition density function has fundamental implications and correspondence to underlying mechanisms that influence transition. In particular, we show that the known proportionality between salt concentration and diffusion of water also influences delayed anomalous behaviour.