Two Mesoporous Domains Are Better Than One for Catalytic Deconstruction of Polyolefins.

Catalytic hydrogenolysis of polyolefins into valuable liquid, oil, or wax-like hydrocarbon chains for second-life applications is typically accompanied by the hydrogen-wasting co-formation of low value volatiles, notably methane, that increase greenhouse gas emissions. Catalytic sites confined at the bottom of mesoporous wells, under conditions in which the pore exerts the greatest influence over the mechanism, are capable of producing less gases than unconfined sites. A new architecture was designed to emphasize this pore effect, with the active platinum nanoparticles embedded between linear, hexagonal mesoporous silica and gyroidal cubic MCM-48 silica (mSiO2/Pt/MCM-48). This catalyst deconstructs polyolefins selectively into ∼C20-C40 paraffins and cleaves C-C bonds at a rate (TOF = 4.2 ± 0.3 s-1) exceeding that of materials lacking these combined features while generating negligible volatile side products including methane. The time-independent product distribution is consistent with a processive mechanism for polymer deconstruction. In contrast to time- and polymer length-dependent products obtained from non-porous catalysts, mSiO2/Pt/MCM-48 yields a C28-centered Gaussian distribution of waxy hydrocarbons from polyolefins of varying molecular weight, composition, and physical properties, including low-density polyethylene, isotactic polypropylene, ultrahigh-molecular-weight polyethylene, and mixtures of multiple, post-industrial polyolefins. Coarse-grained simulation reveals that the porous-core architecture enables the paraffins to diffuse away from the active platinum site, preventing secondary reactions that produce gases.

Phase separation and ripening in a viscoelastic gel.

The process of phase separation in elastic solids and viscous fluids is of fundamental importance to the stability and function of soft materials. We explore the dynamics of phase separation and domain growth in a viscoelastic material such as a polymer gel. Using analytical theory and Monte Carlo simulations, we report a domain growth regime in which the domain size increases algebraically with a ripening exponent [Formula: see text] that depends on the viscoelastic properties of the material. For a prototypical Maxwell material, we obtain [Formula: see text], which is markedly different from the well-known Ostwald ripening process with [Formula: see text]. We generalize our theory to systems with arbitrary power-law relaxation behavior and discuss our findings in the context of the long-term stability of materials as well as recent experimental results on phase separation in cross-linked networks and cytoskeleton.

Unravelling crystal growth of nanoparticles.

Crystal growth from nanoscale constituents is a ubiquitous phenomenon in biology, geology and materials science. Numerous studies have focused on understanding the onset of nucleation and on producing high-quality crystals by empirically sampling constituents with different attributes and varying the growth conditions. However, the kinetics of post-nucleation growth processes, an important determinant of crystal morphology and properties, have remained underexplored due to experimental challenges associated with real-space imaging at the nanoscale. Here we report the imaging of the crystal growth of nanoparticles of different shapes using liquid-phase transmission electron microscopy, resolving both lateral and perpendicular growth of crystal layers by tracking individual nanoparticles. We observe that these nanoscale systems exhibit layer-by-layer growth, typical of atomic crystallization, as well as rough growth prevalent in colloidal systems. Surprisingly, the lateral and perpendicular growth modes can be independently controlled, resulting in two mixed crystallization modes that, until now, have received only scant attention. Combining analytical considerations with molecular dynamics and kinetic Monte Carlo simulations, we develop a comprehensive framework for our observations, which are fundamentally determined by the size and shape of the building blocks. These insights unify the understanding of crystal growth across four orders of magnitude in particle size and suggest novel pathways to crystal engineering.

Coarse-Grained Modeling of Polymer Cleavage within a Porous Catalytic Support.

The chemical upcycling of plastic waste to valuable liquid products requires catalytic cleavage architectures that afford control over the resulting product distributions. Recently, a catalyst was synthesized in which polymer chains are cleaved at the bottoms of pores to yield a narrow distribution of alkane products. An attractive feature of this architecture is the ability to modulate the product distribution by tuning physical parameters like the diameter of the pore. Understanding how such parameters affect product distributions is an important requirement of further synthetic improvements. We demonstrate that the pore diameter controls the products of the cleavage reaction via two distinct mechanisms. Our coarse-grained, particle-based simulations yield insight into the interplay of chain cleavage and pore residence times and show that the pore size can bias which bonds along a chain are cleaved.

Seeking regularity from irregularity: unveiling the synthesis-nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning.

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis-nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

Hybrid Nanocrystals of Small Molecules and Chemically Disordered Polymers.

Organic crystals formed by small molecules can be highly functional but are often brittle or insoluble structures with limited possibilities for use or processing from a liquid phase. A possible solution is the nanoscale integration of polymers into organic crystals without sacrificing long-range order and therefore function. This enables the organic crystals to benefit from the advantageous mechanical and chemical properties of the polymeric component. We report here on a strategy in which small molecules cocrystallize with side chains of chemically disordered polymers to create hybrid nanostructures containing a highly ordered lattice. Synchrotron X-ray scattering, absorption spectroscopy, and coarse-grained molecular dynamics simulations reveal that the polymer backbones form an "exo-crystalline" layer of disordered chains that wrap around the nanostructures, becoming a handle for interesting properties. The morphology of this "hybrid bonding polymer" nanostructure is dictated by the competition between the polymers' entropy and the enthalpy of the lattice allowing for control over the aspect ratio of the nanocrystal by changing the degree of polymer integration. We observed that nanostructures with an exo-crystalline layer of polymer exhibit enhanced fracture strength, self-healing capacity, and dispersion in water, which benefits their use as light-harvesting assemblies in photocatalysis. Guided by computation, future work could further explore these hybrid nanostructures as components for functional materials.

Hydrodynamically Controlled Self-Organization in Mixtures of Active and Passive Colloids.

Active particles are known to exhibit collective behavior and induce structure in a variety of soft-matter systems. However, many naturally occurring complex fluids are mixtures of active and passive components. The authors examine how activity induces organization in such multi-component systems. Mixtures of passive colloids and colloidal micromotors are investigated and it is observed that even a small fraction of active particles induces reorganization of the passive components in an intriguing series of phenomena. Experimental observations are combined with large-scale simulations that explicitly resolve the near- and far-field effects of the hydrodynamic flow and simultaneously accurately treat the fluid-colloid interfaces. It is demonstrated that neither conventional molecular dynamics simulations nor the reduction of hydrodynamic effects to phoretic attractions can explain the observed phenomena, which originate from the flow field that is generated by the active colloids and subsequently modified by the aggregating passive units. These findings not only offer insight into the organization of biological or synthetic active-passive mixtures, but also open avenues to controlling the behavior of passive building blocks by means of small amounts of active particles.

Accelerated simulation method for charge regulation effects.

The net charge of solvated entities, ranging from polyelectrolytes and biomolecules to charged nanoparticles and membranes, depends on the local dissociation equilibrium of individual ionizable groups. Incorporation of this phenomenon, charge regulation (CR), in theoretical and computational models requires dynamic, configuration-dependent recalculation of surface charges and is therefore typically approximated by assuming constant net charge on particles. Various computational methods exist that address this. We present an alternative, particularly efficient CR Monte Carlo method (CR-MC), which explicitly models the redistribution of individual charges and accurately samples the correct grand-canonical charge distribution. In addition, we provide an open-source implementation in the large-scale Atomic/Molecular Massively Parallel Simulator molecular dynamics (MD) simulation package, resulting in a hybrid MD/CR-MC simulation method. This implementation is designed to handle a wide range of implicit-solvent systems that model discreet ionizable groups or surface sites. The computational cost of the method scales linearly with the number of ionizable groups, thereby allowing accurate simulations of systems containing thousands of individual ionizable sites. By matter of illustration, we use the CR-MC method to quantify the effects of CR on the nature of the polyelectrolyte coil-globule transition and on the effective interaction between oppositely charged nanoparticles.

From predictive modelling to machine learning and reverse engineering of colloidal self-assembly.

An overwhelming diversity of colloidal building blocks with distinct sizes, materials and tunable interaction potentials are now available for colloidal self-assembly. The application space for materials composed of these building blocks is vast. To make progress in the rational design of new self-assembled materials, it is desirable to guide the experimental synthesis efforts by computational modelling. Here, we discuss computer simulation methods and strategies used for the design of soft materials created through bottom-up self-assembly of colloids and nanoparticles. We describe simulation techniques for investigating the self-assembly behaviour of colloidal suspensions, including crystal structure prediction methods, phase diagram calculations and enhanced sampling techniques, as well as their limitations. We also discuss the recent surge of interest in machine learning and reverse-engineering methods. Although their implementation in the colloidal realm is still in its infancy, we anticipate that these data-science tools offer new paradigms in understanding, predicting and (inverse) design of novel colloidal materials.

Charge Regulation Effects in Nanoparticle Self-Assembly.

Nanoparticles in solution acquire charge through the dissociation or association of surface groups. Thus, a proper description of their electrostatic interactions requires the use of charge-regulating boundary conditions rather than the commonly employed constant-charge approximation. We implement a hybrid Monte Carlo/molecular dynamics scheme that dynamically adjusts the charges of individual surface groups of objects while evolving their trajectories. Charge regulation effects are shown to qualitatively change self-assembled structures due to global charge redistribution, stabilizing asymmetric constructs. We delineate under which conditions the conventional constant-charge approximation may be employed and clarify the interplay between charge regulation and dielectric polarization.

Particle-particle particle-mesh algorithm for electrolytes between charged dielectric interfaces.

Ionic distributions near charged interfaces control processes from colloidal aggregation to solvent flow in nanodevices. Such interfaces are often characterized by a jump in the permittivity, which gives rise to the surface polarization charge. This induced charge may significantly affect the ionic distributions so that efficient methods for modeling spatially varying dielectrics are needed. We formulate a method with O(N⁡log⁡N) scaling for electrolytes between charged planar interfaces with asymmetric dielectric contrasts. Our approach, which builds on earlier work, is based on combining image charges with the particle-particle particle-mesh algorithm and representing uniform surface charges via an electric field. This enables simulations of complex dielectric interactions that outperform most alternative methods in speed and accuracy. To make the method practically useful, we provide guidelines-based upon careful tests-for choosing optimal simulation parameters. Explicit expressions for the electrostatic forces are given to facilitate the implementation of our algorithm in standard molecular dynamics packages.

"Metaphilic" Cell-Penetrating Polypeptide-Vancomycin Conjugate Efficiently Eradicates Intracellular Bacteria via a Dual Mechanism.

Infections by intracellular pathogens are difficult to treat because of the poor accessibility of antibiotics to the pathogens encased by host cell membranes. As such, a strategy that can improve the membrane permeability of antibiotics would significantly increase their efficiency against the intracellular pathogens. Here, we report the design of an adaptive, metaphilic cell-penetrating polypeptide (CPP)-antibiotic conjugate (VPP-G) that can effectively eradicate the intracellular bacteria both in vitro and in vivo. VPP-G was synthesized by attaching vancomycin to a highly membrane-penetrative guanidinium-functionalized metaphilic CPP. VPP-G effectively kills not only extracellular but also far more challenging intracellular pathogens, such as S. aureus, methicillin-resistant S. aureus, and vancomycin-resistant Enterococci. VPP-G enters the host cell via a unique metaphilic membrane penetration mechanism and kills intracellular bacteria through disruption of both cell wall biosynthesis and membrane integrity. This dual antimicrobial mechanism of VPP-G prevents bacteria from developing drug resistance and could also potentially kill dormant intracellular bacteria. VPP-G effectively eradicates MRSA in vivo, significantly outperforming vancomycin, which represents one of the most effective intracellular antibacterial agents reported so far. This strategy can be easily adapted to develop other conjugates against different intracellular pathogens by attaching different antibiotics to these highly membrane-penetrative metaphilic CPPs.

Harmonic surface mapping algorithm for molecular dynamics simulations of particle systems with planar dielectric interfaces.

We have developed an accurate and efficient method for molecular dynamics simulations of charged particles confined by planar dielectric interfaces. The algorithm combines the image-charge method for near field with the harmonic surface mapping, which converts the contribution of infinite far-field charges into a finite number of charges on an auxiliary spherical surface. We approximate the electrostatic potential of far-field charges via spherical harmonic expansion and determine the coefficients by fitting the Dirichlet-to-Neumann boundary condition, which only requires the potential within the simulation cell. Instead of performing the direct evaluation of spherical harmonic series expansion, we use Green's second identity to transform the series expansion into a spherical integral, which can be accurately represented by discrete charges on the sphere. Therefore, the fast multipole method can be readily employed to sum over all charges within and on the sphere, achieving truly linear O(N) complexity. Our algorithm can be applied to a broad range of charged complex fluids under dielectric confinement.

Kinetic pathways of crystallization at the nanoscale.

Nucleation and growth are universally important in systems from the atomic to the micrometre scale as they dictate structural and functional attributes of crystals. However, at the nanoscale, the pathways towards crystallization have been largely unexplored owing to the challenge of resolving the motion of individual building blocks in a liquid medium. Here we address this gap by directly imaging the full transition of dispersed gold nanoprisms to a superlattice at the single-particle level. We utilize liquid-phase transmission electron microscopy at low dose rates to control nanoparticle interactions without affecting their motions. Combining particle tracking with Monte Carlo simulations, we reveal that positional ordering of the superlattice emerges from orientational disorder. This method allows us to measure parameters such as line tension and phase coordinates, charting the nonclassical nucleation pathway involving a dense, amorphous intermediate. We demonstrate the versatility of our approach via crystallization of different nanoparticles, pointing the way to more general applications.

Dielectric Modulation of Two-Dimensional Dipolar Materials.

Spontaneous pattern formation plays an important role in a wide variety of natural phenomena and materials systems. A key ingredient for the occurrence of modulated phases is the presence of competing interactions, generally of different physical origins. We demonstrate that in dipolar films, a prototypical system for pattern formation, patterns can be induced by dielectric effects alone. A rich phase diagram arises, where striped and circular morphologies emerge with geometric properties that can be controlled through variation of particle shape and substrate permittivity or permeability. These effects are particularly enhanced by metamaterial substrates.

Efficient dynamic simulations of charged dielectric colloids through a novel hybrid method.

Modern particle-based simulations increasingly incorporate polarization charges arising from spatially nonuniform permittivity. For complex dielectric geometries, calculation of these induced many-body effects typically requires numerical solvers based upon boundary-element methods, which very significantly increase the required computational effort. For the special case of dielectric spheres, such as colloids or nanoparticles, we recently proposed a semianalytical spectrally accurate hybrid method that combines the method of moments, the image-charge method, and the fast multipole method. The hybrid method is efficient and accurate even when dielectric spheres are closely packed. Here, we extend the method to the evaluation of direct and induced electrostatic forces and demonstrate how this can be incorporated in molecular dynamics simulations. The choice of the relevant numerical parameters for molecular dynamics simulations is discussed in detail, as well as comparisons to the boundary-element method. As a concrete example, we examine the challenging case of binary crystal structures composed of close-packed dielectric colloidal spheres.

Subtle changes in surface-tethered groups on PEGylated DNA nanoparticles significantly influence gene transfection and cellular uptake.

PEGylation strategy has been widely used to enhance colloidal stability of polycation/DNA nanoparticles (NPs) for gene delivery. To investigate the effect of polyethylene glycol (PEG) terminal groups on the transfection properties of these NPs, we synthesized DNA NPs using PEG-g-linear polyethyleneimine (lPEI) with PEG terminal groups containing alkyl chains of various lengths with or without a hydroxyl terminal group. For both alkyl- and hydroxyalkyl-decorated NPs with PEG grafting densities of 1.5, 3, or 5% on lPEI, the highest levels of transfection and uptake were consistently achieved at intermediate alkyl chain lengths of 3 to 6 carbons, where the transfection efficiency is significantly higher than that of nonfunctionalized lPEI/DNA NPs. Molecular dynamics simulations revealed that both alkyl- and hydroxyalkyl-decorated NPs with intermediate alkyl chain length exhibited more rapid engulfment than NPs with shorter or longer alkyl chains. This study identifies a new parameter for the engineering design of PEGylated DNA NPs.

Dynamics and structure of colloidal aggregates under microchannel flow.

The kinetics of colloidal gels under narrow confinement are of widespread practical relevance, with applications ranging from flow in biological systems to 3D printing. Although the properties of such gels under uniform shear have received considerable attention, the effects of strongly nonuniform shear are far less understood. Motivated by the possibilities offered by recent advances in nano- and microfluidics, we explore the generic phase behavior and dynamics of attractive colloids subject to microchannel flow, using mesoscale particle-based hydrodynamic simulations. Whereas moderate shear strengths result in shear-assisted crystallization, high shear strengths overwhelm the attractions and lead to melting of the clusters. Within the transition region between these two regimes, we discover remarkable dynamics of the colloidal aggregates. Shear-induced surface melting of the aggregates, in conjunction with the Plateau-Rayleigh instability and size-dependent cluster velocities, leads to a cyclic process in which elongated threads of colloidal aggregates break up and reform, resulting in large crystallites. These insights offer new possibilities for the control of colloidal dynamics and aggregation under confinement.

Dielectric Effects on Ion Transport in Polyelectrolyte Brushes.

Surface-grafted polyelectrolytes provide a versatile way to create functionalized interfaces and nanochannels with externally controllable properties. Understanding the behavior of ions within the brush-like assemblies is crucial for the further development of these devices. We demonstrate that the ion transport through the brushes is governed by the interplay of electrostatic ion-polymer binding and steric effects, leading to a mobility that depends nonmonotonically on grafting density. However, the ion-polymer binding can be modulated by the dielectric properties of the substrate. As a result, surface polarization suppresses ion mobility near insulating interfaces and enhances it near conducting interfaces, even causing a shift from nonmonotonic to monotonic variation with grafting density.

Asymmetric electrolytes near structured dielectric interfaces.

The ion distribution of electrolytes near interfaces with dielectric contrast has important consequences for electrochemical processes and many other applications. To date, most studies of such systems have focused on geometrically simple interfaces, for which dielectric effects are analytically solvable or computationally tractable. However, all real surfaces display nontrivial structure at the nanoscale and have, in particular, a nonuniform local curvature. Using a recently developed, highly efficient computational method, we investigate the effect of surface geometry on ion distribution and interface polarization. We consider an asymmetric 2:1 electrolyte bounded by a sinusoidally deformed solid surface. We demonstrate that even when the surface is neutral, the electrolyte acquires a nonuniform ion density profile near the surface. This profile is asymmetric and leads to an effective charging of the surface. We furthermore show that the induced charge is modulated by the local curvature. The effective charge is opposite in sign to the multivalent ions and is larger in concave regions of the surface.