Hypothesis

The electrical double layer formed at the mineral/electrolyte interface is often modeled using mean-field approaches based on a continuum description of the solvent whose dielectric constant is assumed to decrease monotonically with decreasing distance to the surface. In contrast, molecular simulations show that the solvent polarizability oscillates near the surface similar to the water density profile - as shown previously, for example, by Bonthuis et al. (D.J. Bonthuis, S. Gekle, R.R. Netz, Dielectric Profile of Interfacial Water and its Effect on Double-Layer Capacitance, Phys Rev Lett 107(16) (2011) 166102). We showed that molecular and mesoscale pictures agree by spatially averaging the dielectric constant obtained from molecular dynamics simulations over the distances relevant to the mean-field representation. In addition, the values of capacitances used to describe the electrical double layer in Surface Complexation Models (SCMs) of the mineral/electrolyte interface can be estimated using molecularly informed spatially averaged dielectric constants and positions of hydration layers.

Experiments

First, we used molecular dynamics simulations to model the calcite 101¯4/electrolyte interface. Next, by using atomistic trajectories, we calculated the distance-dependent static dielectric constant and water density in the direction normal to the. Finally, we applied spatial compartmentalization consistent with the model of parallel-plate capacitors connected in series to estimate SCM capacitances.

Findings

Computationally expensive simulations are required to determine the dielectric constant profile of interfacial water near the mineral surface. On the other hand, water density profiles are readily assessable from much shorter simulation trajectories. Our simulations confirmed that dielectric and water density oscillations at the interface are correlated. Here, we parametrized linear regression models to estimate the dielectric constant directly from the local water density. This is a significant computational shortcut compared to slowly converging calculations relying on total dipole moment fluctuations. The amplitude of the interfacial dielectric constant oscillation can exceed the dielectric constant of the bulk water, suggesting an ice-like frozen state, but only if there are no electrolyte ions. The interfacial accumulation of electrolyte ions causes a decrease in the dielectric constant due to the reduction of water density and re-orientation of water dipoles in ion hydration shells. Finally, we show how to use the computed dielectric properties to estimate SCM's capacitances.

Water diffusion within smectite clay interlayers is reduced by confinement and hence is highly determined by the interlayer spacings that are adopted during swelling. However, a molecular understanding of the short- and long-range forces governing interlayer water structure and dynamics is lacking. Using molecular dynamics simulations of water intercalated between pyrophyllite (smectite prototype) layers we provide a detailed picture of the variation of interlayered water mobility accompanying smectite expansion. Subtle changes in hydrogen bond network structure cause significant changes in water mobility that is greater for stable hydration states and reduced for intermediate separations. By studying pyrophyllite with and without external water we reveal that long-range electrostatic forces apply a restraining effect upon interlayer water mobility. Our findings are relevant for broad range of confining nanostructures with walls thin enough to permit long-range interactions that could affect the mobility of confined solvent molecules and solute species.

Iron(III) oxides and oxyhydroxides are among the most reactive minerals in the environment, with surfaces that become charged when immersed in water. The governing role of surface charge over interfacial processes such as metal sorption is well understood. However, its role in interfacial redox reactions, such as when metal sorption is coupled to interfacial electron transfer (ET), is not. This is mainly because surface charge affects not only the types and densities of surface complexes formed but also their respective driving forces for ET. An important case is Fe(II)-catalyzed recrystallization of Fe(III)-oxyhydroxides, in which Fe(II) sorption and interfacial ET are closely linked. In this study, we used replica-exchange constant-pH molecular dynamics simulations (Zarzycki, P.; Smith, D. M.; Rosso, K. M. J. Chem. Theory Comput. 2015, 11, 1715-1724) to calculate the distance-dependent electrostatic potential at charged (110) surfaces of goethite particles, assessing its effect on previously computed Fe(II) sorption and interfacial ET free energies (Zarzycki, P.; Kerisit, S.; Rosso, K. M. J. Phys. Chem. C 2015, 119, 3111-3123). We show that Fe(II) adsorbs preferentially as an inner-sphere complex on the negatively charged surface, and as an outer-sphere complex on the positively charged surface because of both electrostatic repulsion and high energy barriers that arise from ordered water layers at the interface. The separation distance between adsorbed Fe(II) and the surface largely dictates adiabatic versus nonadiabatic ET regimes for this interface. The findings help unravel the pH dependence of Fe(II)-catalyzed recrystallization of goethite.

Goethite is one of the most stable and common iron(III) minerals at the Earth's near surface. However, recent isotope tracer studies have suggested that goethite continuously recrystallizes in the presence of aqueous Fe(II) ions. Some of these studies indicate the presence of two regimes of atom-exchange kinetics, a rapid stage assigned to reactive defect sites initially available at particle surfaces, followed by slower continuous exchange. An autocatalytic solid-state electron conduction model coupling Fe(II) oxidative adsorption to its reductive release at spatially distinct sites has been proposed, but the thermodynamic driving force has yet to be pinpointed. Here, using a novel hybrid/reactive molecular simulation method, for goethite (110) surfaces at circumneutral pH, we rigorously tested whether surface free energy minimization, including examining the role of structural defects, is sufficient to overcome the activation energy for interfacial electron transfer and conduction. The simulations quantitatively show that (i) on smooth stable surfaces the available thermal energy at dynamic equilibrium is sufficient to sustain the slow continuous regime of atom-exchange kinetics via short intrasurface electron conduction pathways of 1-2 nm (three to five Fe site hops), (ii) in this slower regime, the model converges to atom-exchange kinetics of 10 -5 Fe s -1 cm -2 , a rate recently deduced from stochastic modeling of experimental data and linked to the reductive dissolution rate of goethite, and (iii) the driving force for smoothing of initially rough defective goethite surfaces can accelerate atom exchange to an extent quantitatively consistent with that observed in the initial rapid stage, in this case accessing conduction pathways of up to 8 nm. The findings suggest that the interaction of Fe(II) with initially defective goethite surfaces can drive, by the conduction model, atom exchange that is capable of recrystallizing the interiors of nanoscale particles and that, closer to equilibrium on smooth surfaces, slower atom exchange continues in perpetuity but likely involving only the outermost atomic layers.

The safety assessments of the geological storage of spent nuclear fuel require understanding the underground radionuclide mobility in case of a leakage from multi-barrier canisters. Uranium, the most common radionuclide in non-reprocessed spent nuclear fuels, is immobile in reduced form (U(IV) and highly mobile in an oxidized state (U(VI)). The latter form is considered one of the most dangerous environmental threats in the safety assessments of spent nuclear fuel repositories. The sorption of uranium to mineral surfaces surrounding the repository limits their mobility. We quantify uranium sorption using surface complexation models (SCMs). Unfortunately, numerical SCM solvers often encounter convergence problems due to the complex nature of convoluted equations and correlations between model parameters. This study explored two machine learning surrogates for the 2-pK Triple Layer Model of uranium retention by oxide surfaces if released as U(IV) in the oxidizing conditions: random forest regressor and deep neural networks. Our surrogate models, particularly DNN, accurately reproduce SCM model predictions at a fraction of the computational cost without any convergence issues. The safety assessment of spent fuel repositories, specifically the migration of leaked radioactive waste, will benefit from having ultrafast AI/ML surrogates for the computationally expensive sorption models that can be easily incorporated into larger-scale contaminant migration models. One such model is presented here.

Aqueous films on mineral surfaces control the physical, chemical, and biological transport processes in the atmosphere, soil, and rocks. Despite the importance of thin films for various research and engineering fields, there are still unanswered questions regarding the roles of the different forces affecting the nature of water films. One of these, the focus of this study, is the development of abnormally thick water films on quartz surfaces. In this study, we developed a density-functional-theory-based model to describe the time-dependent evolution of water films and identify the governing forces responsible for thickening films. We simulated the diffusion of water vapor from ambient air toward mineral surfaces and the formation and thickening of water films at various relative humidity values. Our model predicts an abnormal water film thickness on a hydroxylated quartz surface compared to a surface free of hydroxylation, which explains experimental observations. We further used the model to understand the key interaction forces at different stages of water film formation and thickening. Our model suggests that the attractive hydrogen bonding and van der Waals forces initiate a seed layer of water, and the electrostatic forces, generated by the hydroxylated and thus charged surface, lead to the thickening of water films. This generalizable model can provide insights into the peculiarities of water film development on various mineral surfaces.

Chemotherapy is a primary treatment for the metastatic lung cancer patients. To select the most effective combination of drugs, we need an efficient way of assessing tumor response. Here, we showed that differential scanning calorimetry (DSC) analysis of blood serum proteins could reveal the patient response to the treatment. If chemotherapy is effective, serum proteins DSC curve of non-small cellular lung cancer (NSCLC) case is similar to the those of the healthy individuals. If treatment fails, notable changes occur in the DSC profile of NSCLC patients blood serum. Our preliminary work illustrates how thermal analysis of changes in the heat capacity of blood serum proteins can provide an insight into patient response to chemotherapy - the essential information for any successive lung cancer treatment.

When a metal oxide surface is immersed in aqueous solution, it has the ability to bind, orient, and order interfacial water, affecting both chemical and physical interactions with the surface. Structured interfacial water thus possesses time-averaged, spatially varying polarization charge and potential that are comparable to those arising due to ion accumulation. It is well established that interfacial water structure propagates from the surface into bulk solution. Here, we show that interfacial water structure also propagates laterally, with important consequences. The constant pH molecular dynamics was used to impose a pH difference between opposite faces of a model goethite (α-FeOOH) nanoparticle and quantify water polarization charge on intervening faces. We find that the structure of water on one face is strongly affected by the structure on nearby surfaces, revealing the importance of long-range lateral hydrogen bonding networks with implications for particle aggregation, oriented attachment, and processes such as dissolution and growth.

Serpentine minerals exert important controls on the physical properties of ultramafic rocks and have the potential to influence deformation phenomena in fault zones and to control the release of water in subducted slabs. Sheet serpentine generally, and lizardite and amesite specifically, can adopt alternative crystallographic stacking arrangements called polytypes. Polytypism has been extensively studied in fully ordered crystals, but it remains largely enigmatic in the more common semi-disordered crystals that in long-range analyses such as Xray diffraction only exhibit random combinations of 0b and ±1/3b interlayer shifts. To date, atomic-resolution imaging to identify locally ordered polytypes has been precluded by the beam-sensitive nature of this hydrous magnesium silicate mineral. Here, we employed low-dose high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to study the polytypic structure of semi-disordered lizardite and amesite. Because the electron dose was as low as ~6000 e-/Å2, it was possible to directly resolve oxygen atomic columns and all the cations with a resolution of ~1 Å and reveal the short-range order. For lizardite, we identified long-period non-standard polytypes, including examples with 3, 4, 8, and 9 layers stemming from the ordering of the octahedral tilt along the a-axis. For amesite, we found short-range ordered polytypes with periodicities of up to 42 Å stemming from the ordering of interlayer shifts along the b-axis. The resolution was sufficient to determine the relative abundance of 6R2, 6R1, 2H1, and 2H2 polytypes in amesite to be 46.1, 29.6, 7.7, and 1.9%, respectively. This is contrary to the expectation that the most common form of amesite is the 2H2 polytype, which may be more likely to form macroscopic crystals suitable for conventional Xray diffraction-based studies. We conclude that HAADF-STEM methods open the way for the characterization of beam-sensitive minerals and to resolve the structural details of less well-ordered (but possibly more abundant) minerals at a unit-cell scale.

Molecular dynamics simulation is an indispensable tool for understanding the collective behavior of atoms and molecules and the phases they form. Statistical mechanics provides accurate routes for predicting macroscopic properties as time-averages over visited molecular configurations - microstates. However, to obtain convergence, we need a sufficiently long record of visited microstates, which translates to the high-computational cost of the molecular simulations. In this work, we show how to use a point cloud-based deep learning strategy to rapidly predict the structural properties of liquids from a single molecular configuration. We tested our approach using three homogeneous liquids with progressively more complex entities and interactions: Ar, NO, and H₂O under varying pressure and temperature conditions within the liquid state domain. Our deep neural network architecture allows rapid insight into the liquid structure, here probed by the radial distribution function, and can be used with molecular/atomistic configurations generated by either simulation, first-principle, or experimental methods.