Formation, chemical evolution and solidification of the dense liquid phase of calcium (bi)carbonate.

Metal carbonates, which are ubiquitous in the near-surface mineral record, are a major product of biomineralizing organisms and serve as important targets for capturing anthropogenic CO2 emissions. However, pathways of carbonate mineralization typically diverge from classical predictions due to the involvement of disordered precursors, such as the dense liquid phase (DLP), yet little is known about DLP formation or solidification processes. Using in situ methods we report that a highly hydrated bicarbonate DLP forms via liquid-liquid phase separation and transforms into hollow hydrated amorphous CaCO3 particles. Acidic proteins and polymers extend DLP lifetimes while leaving the pathway and chemistry unchanged. Molecular simulations suggest that the DLP forms via direct condensation of solvated Ca²+⋅(HCO3-)2 complexes that react due to proximity effects in the confined DLP droplets. Our findings provide insight into CaCO3 nucleation that is mediated by liquid-liquid phase separation, advancing the ability to direct carbonate mineralization and elucidating an often-proposed complex pathway of biomineralization.

Towards predictive control of reversible nanoparticle assembly with solid-binding proteins.

Although a broad range of ligand-functionalized nanoparticles and physico-chemical triggers have been exploited to create stimuli-responsive colloidal systems, little attention has been paid to the reversible assembly of unmodified nanoparticles with non-covalently bound proteins. Previously, we reported that a derivative of green fluorescent protein engineered with oppositely located silica-binding peptides mediates the repeated assembly and disassembly of 10-nm silica nanoparticles when pH is toggled between 7.5 and 8.5. We captured the subtle interplay between interparticle electrostatic repulsion and their protein-mediated short-range attraction with a multiscale model energetically benchmarked to collective system behavior captured by scattering experiments. Here, we show that both solution conditions (pH and ionic strength) and protein engineering (sequence and position of engineered silica-binding peptides) provide pathways for reversible control over growth and fragmentation, leading to clusters ranging in size from 25 nm protein-coated particles to micrometer-size aggregate. We further find that the higher electrolyte environment associated with successive cycles of base addition eventually eliminates reversibility. Our model accurately predicts these multiple length scales phenomena. The underpinning concepts provide design principles for the dynamic control of other protein- and particle-based nanocomposites.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction.

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Detecting underscreening and generalized Kirkwood transitions in aqueous electrolytes.

We establish the connection between the measured small angle x-ray scattering signal and the charge-charge correlations underlying Kirkwood transitions (KTs) in 1:1, 2:1, and 3:1 aqueous electrolytes. These measurements allow us to obtain underscreening lengths for bulk electrolytes independently verified by theory and simulations. Furthermore, we generalize the concept of KTs beyond those theoretically predicted for 1:1 electrolytes, which involves the inverse screening length, a0, and the inverse periodicity length, Q0. Above the KTs, we find a universal scaling of a0∝c-ζ/3 and Q0 ∝ c1/3 for the studied electrolyte solutions, where ζ is the ionic strength factor.

Ion-dependent protein-surface interactions from intrinsic solvent response.

The phyllosilicate mineral muscovite mica is widely used as a surface template for the patterning of macromolecules, yet a molecular understanding of its surface chemistry under varying solution conditions, required to predict and control the self-assembly of adsorbed species, is lacking. We utilize all-atom molecular dynamics simulations in conjunction with an electrostatic analysis based in local molecular field theory that affords a clean separation of long-range and short-range electrostatics. Using water polarization response as a measure of the electric fields that arise from patterned, surface-bound ions that direct the adsorption of charged macromolecules, we apply a Landau theory of forces induced by asymmetrically polarized surfaces to compute protein-surface interactions for two muscovite-binding proteins (DHR10-mica6 and C98RhuA). Comparison of the pressure between surface and protein in high-concentration KCl and NaCl aqueous solutions reveals ion-specific differences in far-field protein-surface interactions, neatly capturing the ability of ions to modulate the surface charge of muscovite that in turn selectively attracts one binding face of each protein over all others.

Computational and Experimental Determination of the Properties, Structure, and Stability of Peptoid Nanosheets and Nanotubes.

Peptoids (N-substituted glycines) are a group of highly controllable peptidomimetic polymers. Amphiphilic diblock peptoids have been engineered to assemble crystalline nanospheres, nanofibrils, nanosheets, and nanotubes with biochemical, biomedical, and bioengineering applications. The mechanical properties of peptoid nanoaggregates and their relationship to the emergent self-assembled morphologies have been relatively unexplored and are critical for the rational design of peptoid nanomaterials. In this work, we consider a family of amphiphilic diblock peptoids consisting of a prototypical tube-former (Nbrpm6Nc6, a NH2-capped hydrophobic block of six N-((4-bromophenyl)methyl)glycine residues conjugated to a polar NH3(CH2)5CO tail), a prototypical sheet-former (Nbrpe6Nc6, where the hydrophobic block comprises six N-((4-bromophenyl)ethyl)glycine residues), and an intermediate sequence that forms mixed structures ((NbrpeNbrpm)3Nc6). We combine all-atom molecular dynamics simulations and atomic force microscopy to determine the mechanical properties of the self-assembled 2D crystalline nanosheets and relate these properties to the observed self-assembled morphologies. We find good agreement between our computational predictions and experimental measurements of Young's modulus of crystalline nanosheets. A computational analysis of the bending modulus along the two axes of the planar crystalline nanosheets reveals bending to be more favorable along the axis in which the peptoids stack by interdigitation of the side chains compared to that in which they form columnar crystals with π-stacked side chains. We construct molecular models of nanotubes of the Nbrpm6Nc6 tube-forming peptoid and predict a stability optimum in good agreement with experimental measurements. A theoretical model of nanotube stability suggests that this optimum is a free energy minimum corresponding to a "Goldilocks" tube radius at which capillary wave fluctuations in the tube wall are minimized.

The evolution of solvation symmetry and composition in Zn halide aqueous solutions from dilute to extreme concentrations.

The emergence of cation-anion species, or contact ion pairs, is fundamental to understanding the physical properties of aqueous solutions when moving from the ideal, low-concentration limit to the manifestly non-ideal limits of very high solute concentration or constituent ion activity. We focus here on Zn halide solutions both as a model system and also as an exemplar of the applications spanning from (i) electrical energy storage via the paradigm of water in salt electrolyte (WiSE) to (ii) the physical chemistry of brines in geochemistry to (iii) the long-standing problem of nucleation. Using a combination of experimental and theoretical approaches we quantify the halide coordination number and changing coordination geometry without embedded use of theoretical equilibrium constants. These results and the associated methods, notably including the use of valence-to-core X-ray emission spectroscopy, provide new insights into the Zn halide system and new research directions in the physical chemistry of concentrated electrolytes.

Toward a First-Principles Framework for Predicting Collective Properties of Electrolytes.

Given the universal importance of electrolyte solutions, it is natural to expect that we have a nearly complete understanding of the fundamental properties of these solutions (e.g., the chemical potential) and that we can therefore explain, predict, and control the phenomena occurring in them. In fact, reality falls short of these expectations. But, recent advances in the simulation and modeling of electrolyte solutions indicate that it should soon be possible to make progress toward these goals. In this Account, we will discuss the use of first-principles interaction potentials based in quantum mechanics (QM) to enhance our understanding of electrolyte solutions. Specifically, we will focus on the use of quantum density functional theory (DFT) combined with molecular dynamics simulation (DFT-MD) as the foundation for our approach. The overarching concept is to understand and accurately reproduce the balance between local or short-ranged (SR) structural details and long-range (LR) correlations, allowing the prediction of the thermodynamics of both single ions in solution as well as the collective interactions characterized by activity/osmotic coefficients. In doing so, relevant collective motions and driving forces characterized by chemical potentials can be determined.In this Account, we will make the case that understanding electrolyte solutions requires a faithful QM representation of the SR nature of the ion-ion, ion-water, and water-water interactions. However, the number of molecules that is required for collective behavior makes the direct application of high-level QM methods that contain the best SR physics untenable, making methods that balance accuracy and efficiency a practical goal. Alternatives such as continuum solvent models (CSMs) and empirically based classical molecular dynamics have been extensively employed to resolve this problem but without yet overcoming the fundamental issue of SR accuracy. We will demonstrate that accurately describing the SR interaction is imperative for predicting both intrinsic properties, namely, at infinite dilution, and collective properties of electrolyte solutions.DFT has played an important role in our understanding of condensed phase systems, e.g., bulk liquid water, the air-water interface, ions in bulk, and at the air-water interface. This approach holds huge promise to provide benchmark calculations of electrolyte solution properties that will allow for the development and improvement of more efficient methods, as well as an enhanced understanding of fundamental phenomena. However, the standard protocol using the generalized gradient approximation with van der Waals (vdW) correction requires improvement in order to achieve a high level of quantitative accuracy. Simply simulating with higher level DFT functionals may not be the best route considering the significant computational cost. Alternative methods of incorporating information from higher levels of QM should be explored; e.g., using force matching techniques on small clusters, where high level benchmark calculations are possible, to develop ideal correction terms to the DFT functional is a promising possibility. We argue that DFT with statistical mechanics is becoming an increasingly useful framework enabling the prediction of collective electrolyte properties.

Hierarchical Self-Assembly Pathways of Peptoid Helices and Sheets.

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers. Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with applications in catalysis and molecular separations. Assembly is induced in an organic solvent/water mixture by evaporating the organic phase, but the assembly pathways remain uncharacterized. We conduct all-atom molecular dynamics simulations of Nbrpe6Nc6 as a prototypical amphiphilic diblock peptoid comprising an NH2-capped block of six hydrophobic N-((4-bromophenyl)ethyl)glycine residues conjugated to a polar NH3(CH2)5CO tail. We identify a thermodynamically controlled assembly mechanism by which monomers assemble into disordered aggregates that self-order into 1D chiral helical rods then 2D achiral crystalline sheets. We support our computational predictions with experimental observations of 1D rods using small-angle X-ray scattering, circular dichroism, and atomic force microscopy and 2D crystalline sheets using X-ray diffraction and atomic force microscopy. This work establishes a new understanding of hierarchical peptoid assembly and principles for the design of peptoid-based nanomaterials.

Spiers Memorial Lecture: Assembly-based pathways of crystallization.

Solution crystallization of materials ranging from simple salts to complex supramolecular assemblies has long been viewed through the lens of classical nucleation and growth theories in which monomeric building blocks assemble into ordered structures through inherent thermal fluctuations that overcome a free energy barrier and continue to grow by the addition of such units to atomic steps. However, recent observations have revealed a rich set of hierarchical pathways during both nucleation and growth involving species of a higher order than monomers. While many studies have investigated and deduced the mechanisms underlying hierarchical nucleation pathways, much less research has been directed towards the development of a mechanistic picture of growth by the assembly of more complex units. Here, we review recent investigations into crystal growth by particle attachment, with an emphasis on oriented attachment. We discuss the relationship between interfacial structure, interparticle forces, and attachment dynamics, discuss the consequences of size dependent phase stability, and examine the impact of the ligand-functionalization of primary particles.

Molecular-level origin of the carboxylate head group response to divalent metal ion complexation at the air-water interface.

We exploit gas-phase cluster ion techniques to provide insight into the local interactions underlying divalent metal ion-driven changes in the spectra of carboxylic acids at the air-water interface. This information clarifies the experimental findings that the CO stretching bands of long-chain acids appear at very similar energies when the head group is deprotonated by high subphase pH or exposed to relatively high concentrations of Ca2+ metal ions. To this end, we report the evolution of the vibrational spectra of size-selected [Ca2+·RCO2-]+·(H2O) n=0to12 and RCO2-·(H2O) n=0to14 cluster ions toward the features observed at the air-water interface. Surprisingly, not only does stepwise hydration of the RCO2- anion and the [Ca2+·RCO2-]+ contact ion pair yield solvatochromic responses in opposite directions, but in both cases, the responses of the 2 (symmetric and asymmetric stretching) CO bands to hydration are opposite to each other. The result is that both CO bands evolve toward their interfacial asymptotes from opposite directions. Simulations of the [Ca2+·RCO2-]+·(H2O) n clusters indicate that the metal ion remains directly bound to the head group in a contact ion pair motif as the asymmetric CO stretch converges at the interfacial value by n = 12. This establishes that direct metal complexation or deprotonation can account for the interfacial behavior. We discuss these effects in the context of a model that invokes the water network-dependent local electric field along the C-C bond that connects the head group to the hydrocarbon tail as the key microscopic parameter that is correlated with the observed trends.

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data.

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

Probing the thermodynamics and kinetics of ethylene carbonate reduction at the electrode-electrolyte interface with molecular simulations.

Understanding the formation of the solid-electrolyte interphase (SEI) in lithium-ion batteries is an ongoing area of research due to its high degree of complexity and the difficulties encountered by experimental studies. Herein, we investigate the initial stage of SEI growth, the reduction reaction of ethylene carbonate (EC), from both a thermodynamic and a kinetic approach with theory and molecular simulations. We employed both the potential distribution theorem and the Solvation Method based on Density (SMD) to EC solvation for the estimation of reduction potentials of Li+, EC, and Li+-solvating EC (s-EC) as well as reduction rate constants of EC and s-EC. We find that solvation effects greatly influence these quantities of interest, particularly the Li+/Li reference electrode potential in EC solvent. Furthermore, we also compute the inner- and outer-sphere reorganization energies for both EC and s-EC at the interface of liquid EC and a hydroxyl-terminated graphite surface, where total reorganization energies are predicted to be 76.6 and 88.9 kcal/mol, respectively. With the computed reorganization energies, we estimate reduction rate constants across a range of overpotentials and show that EC has a larger electron transfer rate constant than s-EC at equilibrium, despite s-EC being more thermodynamically favorable. Overall, this manuscript demonstrates how ion solvation effects largely govern the prediction of reduction potentials and electron transfer rate constants at the electrode-electrolyte interface.

Visualization of Aluminum Ions at the Mica Water Interface Links Hydrolysis State-to-Surface Potential and Particle Adhesion.

When hydrolyzable cations such as aluminum interact with solid-water interfaces, macroscopic interfacial properties (e.g., surface charge and potential) and interfacial phenomena (e.g., particle adhesion) become tightly linked with the microscopic details of ion adsorption and speciation. We use in situ atomic force microscopy to directly image individual aluminum ions at a mica-water interface and show how adsorbate populations change with pH and aluminum activity. Complementary streaming potential measurements then allow us to build a triple layer model (TLM) that links surface potentials to adsorbate populations, via equilibrium binding constants. Our model predicts that hydrolyzed species dominate the mica-water interface, even when unhydrolyzed species dominate the solution. Ab initio molecular dynamics (AIMD) simulations confirm that aluminum hydrolysis is strongly promoted at the interface. The TLM indicates that hydrolyzed adsorbates are responsible for surface-potential inversions, and we find strong correlations between hydrolyzed adsorbates and particle-adhesion forces, suggesting that these species mediate adhesion by chemical bridging.

Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob's Ladder.

The ability to reproduce the experimental structure of water around the sodium and potassium ions is a key test of the quality of interaction potentials due to the central importance of these ions in a wide range of important phenomena. Here, we simulate the Na+ and K+ ions in bulk water using three density functional theory functionals: (1) the generalized gradient approximation (GGA) based dispersion corrected revised Perdew, Burke, and Ernzerhof functional (revPBE-D3) (2) the recently developed strongly constrained and appropriately normed (SCAN) functional (3) the random phase approximation (RPA) functional for potassium. We compare with experimental X-ray diffraction (XRD) and X-ray absorption fine structure (EXAFS) measurements to demonstrate that SCAN accurately reproduces key structural details of the hydration structure around the sodium and potassium cations, whereas revPBE-D3 fails to do so. However, we show that SCAN provides a worse description of pure water in comparison with revPBE-D3. RPA also shows an improvement for K+, but slow convergence prevents rigorous comparison. Finally, we analyse cluster energetics to show SCAN and RPA have smaller fluctuations of the mean error of ion-water cluster binding energies compared with revPBE-D3.

Solvent reaction coordinate for an SN2 reaction.

We study the prototypical SN2 reaction Cl- + CH3Cl → CH3Cl + Cl- in water using quantum mechanics/molecular mechanics (QM/MM) computer simulations with transition path sampling and inertial likelihood maximization. We have identified a new solvent coordinate to complement the original atom-exchange coordinate used in the classic analysis by Chandrasekhar, Smith, and Jorgensen [J. Am. Chem. Soc. 107, 154 (1985)]. The new solvent coordinate quantifies instantaneous solvent-induced polarization relative to the equilibrium average charge density at each point along the reaction pathway. On the basis of likelihood scores and committor distributions, the new solvent coordinate improves upon the description of solvent dynamical effects relative to previously proposed solvent coordinates. However, it does not increase the transmission coefficient or the accuracy of a transition state theory rate calculation.

CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations.

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-the-art ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

Trends in mica-mica adhesion reflect the influence of molecular details on long-range dispersion forces underlying aggregation and coalignment.

Oriented attachment of nanocrystalline subunits is recognized as a common crystallization pathway that is closely related to formation of nanoparticle superlattices, mesocrystals, and other kinetically stabilized structures. Approaching particles have been observed to rotate to achieve coalignment while separated by nanometer-scale solvent layers. Little is known about the forces that drive coalignment, particularly in this "solvent-separated" regime. To obtain a mechanistic understanding of this process, we used atomic-force-microscopy-based dynamic force spectroscopy with tips fabricated from oriented mica to measure the adhesion forces between mica (001) surfaces in electrolyte solutions as a function of orientation, temperature, electrolyte type, and electrolyte concentration. The results reveal an ∼60° periodicity as well as a complex dependence on electrolyte concentration and temperature. A continuum model that considers the competition between electrostatic repulsion and van der Waals attraction, augmented by microscopic details that include surface separation, water structure, ion hydration, and charge regulation at the interface, qualitatively reproduces the observed trends and implies that dispersion forces are responsible for establishing coalignment in the solvent-separated state.

Direct Observation of the Orientational Anisotropy of Buried Hydroxyl Groups inside Muscovite Mica.

Muscovite mica (001) is a widely used model surface for controlling molecular assembly and a common substrate for environmental adsorption processes. The mica (001) surface displays near-trigonal symmetry, but many molecular adsorbates-including water-exhibit unequal probabilities of alignment along its three nominally equivalent lattice directions. Buried hydroxyl groups within the muscovite structure are speculated to be responsible, but direct evidence is lacking. Here, we utilize vibrational sum frequency generation spectroscopy (vSFG) to characterize the orientation and hydrogen-bonding environment of near-surface hydroxyls inside mica. Multiple distinct peaks are detected in the O-H stretch region, which we attribute to Si/Al substitution in the SiO4 tetrahedron and K+ ion adsorption above the hydroxyls based on density functional theory simulations. Our findings demonstrate that vSFG can identify the absolute orientation of -OH groups and, hence, the surface termination at a mica surface, providing a means to investigate how -OH groups influence molecular adsorption and better understand mica stacking-sequences and physical behavior.

Tuning crystallization pathways through sequence engineering of biomimetic polymers.

Two-step nucleation pathways in which disordered, amorphous, or dense liquid states precede the appearance of crystalline phases have been reported for a wide range of materials, but the dynamics of such pathways are poorly understood. Moreover, whether these pathways are general features of crystallizing systems or a consequence of system-specific structural details that select for direct versus two-step processes is unknown. Using atomic force microscopy to directly observe crystallization of sequence-defined polymers, we show that crystallization pathways are indeed sequence dependent. When a short hydrophobic region is added to a sequence that directly forms crystalline particles, crystallization instead follows a two-step pathway that begins with the creation of disordered clusters of 10-20 molecules and is characterized by highly non-linear crystallization kinetics in which clusters transform into ordered structures that then enter the growth phase. The results shed new light on non-classical crystallization mechanisms and have implications for the design of self-assembling polymer systems.