Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials.

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer-particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer-particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Investigating structure and dynamics of unentangled poly(dimethyl-co-diphenyl)siloxane via molecular dynamics simulation.

Polysiloxane is one of the most important polymeric materials in technological use. Polydimethylsiloxane displays glass-like mechanical properties at low temperatures. Incorporation of phenyl siloxane, via copolymerization for example, improves not only the low-temperature elasticity but also enhances its performance over a wide range of temperatures. Copolymerization with the phenyl component can significantly change the microscopic properties of polysiloxanes, such as chain dynamics and relaxation. However, despite much work in the literature, the influence of such changes is still not clearly understood. In this work, we systematically study the structure and dynamics of random poly(dimethyl-co-diphenyl)siloxane via atomistic molecular dynamics simulations. As the molar ratio ϕ of the diphenyl component increases, we find that the size of the linear copolymer chain expands. At the same time, the chain-diffusivity slows down by over an order of magnitudes. The reduced diffusivity appears to be a result of a complex interplay between the structural and dynamic changes induced by phenyl substitution.

United atom and coarse grained models for crosslinked polydimethylsiloxane with applications to the rheology of silicone fluids.

Siloxane systems consisting primarily of polydimethylsiloxane (PDMS) are versatile, multifaceted materials that play a key role in diverse applications. However, open questions exist regarding the correlation between their varied atomic-level properties and observed macroscale features. To this effect, we have created a systematic workflow to determine coarse-grained simulation models for crosslinked PDMS in order to further elucidate the effects of network changes on the system's rheological properties below the gel point. Our approach leverages a fine-grained united atom model for linear PDMS, which we extend to include crosslinking terms, and applies iterative Boltzmann inversion to obtain a coarse-grain "bead-spring-type" model. We then perform extensive molecular dynamics simulations to explore the effect of crosslinking on the rheology of silicone fluids, where we compute systematic increases in both density and shear viscosity that compare favorably to experiments that we conduct here. The kinematic viscosity of partially crosslinked fluids follows an empirical linear relationship that is surprisingly consistent with Rouse theory, which was originally derived for systems comprised of a uniform distribution of linear chains. The models developed here serve to enable quantitative bottom-up predictions for curing- and age-induced effects on macroscale rheological properties, allowing for accurate prediction of material properties based on fundamental chemical data.

Mechanistic Insights on Permeation of Water over Iron Cations in Nanoporous Silicon Oxide Films for Selective H2 and O2 Evolution.

Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO2 films. It is shown that even for SiO2 pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2 pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2 coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.

Probing the Kinetic Origin of Varying Oxidative Stability of Ethyl- vs. Propyl-spaced Amines for Direct Air Capture.

Amine-based adsorbents are promising for direct air capture of CO2 , yet oxidative degradation remains a key unmitigated risk hindering wide-scale deployment. Borrowing wisdom from the basic auto-oxidation scheme, insights are gained into the underlying degradation mechanisms of polyamines by quantum chemical, advanced sampling simulations, adsorbent synthesis, and accelerated degradation experiments. The reaction kinetics of polyamines are contrasted with that of typical aliphatic polymers and they elucidate for the first time the critical role of aminoalkyl hydroperoxide decomposition in the oxidative degradation of amino-oligomers. The experimentally observed variation in oxidative stability of polyamines with different backbone structures is explained by the relationship between the local chemical structure and the free energy barrier of aminoalkyl hydroperoxide decomposition, suggesting that its energetics can be used as a descriptor to screen and design new polyamines with improved stability. The developed computational capability sheds light on radical-induced degradation chemistry of other organic functional materials.

Bead-Spring Simulation of Ionomer Melts-Studying the Effects of Chain-Length and Associating Group Fraction on Equilibrium Structure and Extensional Flow Behavior.

Ionomers are associative polymers with diverse applications ranging from selective membranes and high-performance adhesives to abrasion- and chemical-resistant coatings, insulation layers, vacuum packaging, and foamed sheets. Within equilibrium melt, the ionic or associating groups are known to form thermally reversible, associative clusters whose presence can significantly affect the system's mechanical, viscoelastic, and transport properties. It is, thus, of great interest to understand how to control such clusters' size distribution, shape, and stability through the designed choice of polymer architecture and the ionic groups' fraction, arrangement, and interaction strength. In this work, we represent linear associating polymers using a Kremer-Grest type bead-spring model and perform large-scale MD simulations to explore the effect of polymer chain-length (l) and fraction (fs) of randomly placed associating groups on the size distribution and stability of formed clusters. We consider different chain-lengths (below and above entanglement), varying fractions of associating groups (represented by 'sticky' beads) between 5 and 20%, and a fixed sticky-sticky nonbond interaction strength of four times that between regular non-associating beads. For all melts containing associating groups the equilibrium structure factor S(q) displays a signature ionomer peak at low wave vector q whose intensity increases with increasing fs and l. The average cluster size Nc increases with fs. However, the effect of chain-length on Nc appears to be pronounced only at higher values of fs. Under extensional flows, the computed stress (and viscosity) is higher at higher fs and l regardless of strain rate. Beyond a critical strain rate, we observe fragmentation of the associative clusters, which has interesting effects on the stress/viscous response.

Multiscale Strategy for Predicting Radiation Chemistry in Polymers.

A primary mode for radiation damage in polymers arises from ballistic electrons that induce electronic excitations, yet subsequent chemical mechanisms are poorly understood. We develop a multiscale strategy to predict this chemistry starting from subatomic scattering calculations. Nonadiabatic molecular dynamics simulations sample initial bond-breaking events following the most likely excitations, which feed into semiempirical simulations that approach chemical equilibrium. Application to polyethylene reveals a mechanism explaining the low propensity to cross-link in crystalline samples.

Polymer degradation through chemical change: a quantum-based test of inferred reactions in irradiated polydimethylsiloxane.

Chemical reaction schemes are key conceptual tools for interpreting the results of experiments and simulations, but often carry implicit assumptions that remain largely unverified for complicated systems. Established schemes for chemical damage through crosslinking in irradiated silicone polymers comprised of polydimethylsiloxane (PDMS) date to the 1950's and correlate small-molecule off-gassing with specific crosslink features. In this regard, we use a somewhat reductionist model to develop a general conditional probability and correlation analysis approach that tests these types of causal connections between proposed experimental observables to reexamine this chemistry through quantum-based molecular dynamics (QMD) simulations. Analysis of the QMD simulations suggests that the established reaction schemes are qualitatively reasonable, but lack strong causal connections under a broad set of conditions that would enable making direct quantitative connections between off-gassing and crosslinking. Further assessment of the QMD data uncovers a strong (but nonideal) quantitative connection between exceptionally hard-to-measure chain scission events and the formation of silanol (Si-OH) groups. Our analysis indicates that conventional notions of radiation damage to PDMS should be further qualified and not necessarily used ad hoc. In addition, our efforts enable independent quantum-based tests that can inform confidence in assumed connections between experimental observables without the burden of fully elucidating entire reaction networks.

Towards replacing physical testing of granular materials with a Topology-based Model.

In the study of packed granular materials, the performance of a sample (e.g., the detonation of a high-energy explosive) often correlates to measurements of a fluid flowing through it. The "effective surface area," the surface area accessible to the airflow, is typically measured using a permeametry apparatus that relates the flow conductance to the permeable surface area via the Carman-Kozeny equation. This equation allows calculating the flow rate of a fluid flowing through the granules packed in the sample for a given pressure drop. However, Carman-Kozeny makes inherent assumptions about tunnel shapes and flow paths that may not accurately hold in situations where the particles possess a wide distribution in shapes, sizes, and aspect ratios, as is true with many powdered systems of technological and commercial interest. To address this challenge, we replicate these measurements virtually on micro-CT images of the powdered material, introducing a new Pore Network Model based on the skeleton of the Morse-Smale complex. Pores are identified as basins of the complex, their incidence encodes adjacency, and the conductivity of the capillary between them is computed from the cross-section at their interface. We build and solve a resistive network to compute an approximate laminar fluid flow through the pore structure. We provide two means of estimating flow-permeable surface area: (i) by direct computation of conductivity, and (ii) by identifying dead-ends in the flow coupled with isosurface extraction and the application of the Carman-Kozeny equation, with the aim of establishing consistency over a range of particle shapes, sizes, porosity levels, and void distribution patterns.

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks.

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. These backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si-O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

Structural Anomalies and Electronic Properties of an Ionic Liquid under Nanoscale Confinement.

Ionic liquids (ILs) promise far greater electrochemical performance compared to aqueous systems, yet key physicochemical properties governing their assembly at interfaces within commonly used graphitic nanopores remain poorly understood. In this work, we combine synchrotron X-ray scattering with first-principles molecular dynamics simulations to unravel key structural characteristics of 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([TFSI]-) ionic liquids confined in carbon slit pores. X-ray scattering reveals selective pore filling due to size exclusion, while filled pores exhibit disruption in the IL intermolecular structure, the extent of which increases for narrower slit pores. First-principles simulations corroborate this finding and quantitatively describe how perturbations in the local IL structure, particularly the hydrogen-bond network, depend strongly on the degree of confinement. Despite significant deviations in structure under confinement, electrochemical stability remains intact, which is important for energy storage based on nanoporous carbon electrodes (e.g., supercapacitors).

Solvation Properties of Silver and Copper Ions in a Room Temperature Ionic Liquid: A First-Principles Study.

Understanding the behavior of metal ions in room temperature ionic liquids (ILs) is essential for predicting and optimizing performance for technologies like metal electrodeposition; however, many mechanistic details remain enigmatic, including the solvation properties of the ions in ILs and how they are governed by the intrinsic interaction between the ions and the liquid species. Here, we utilize first-principles molecular dynamics simulations to unravel and compare the key structural properties of Ag+ and Cu+ ions in a common room temperature IL, 1-ethyl-3-methylimidazolium trifluoromethanesulfonate. We find that, when compared to Cu+, the larger Ag+ shows a more disordered and flexible solvation structure with a more frequent exchange of the IL species between its solvation shells. In addition, our simulations reveal an interesting analog in the solvation behavior of the ions in the IL and aqueous environments, particularly in the effect of the ion electronic structures on their solvation properties. This work provides fundamental understanding of the intrinsic properties of the metal ions in the IL, while offering mechanistic understanding and strategy for future selection of ILs for metal electrodeposition processes.

Printable enzyme-embedded materials for methane to methanol conversion.

An industrial process for the selective activation of methane under mild conditions would be highly valuable for controlling emissions to the environment and for utilizing vast new sources of natural gas. The only selective catalysts for methane activation and conversion to methanol under mild conditions are methane monooxygenases (MMOs) found in methanotrophic bacteria; however, these enzymes are not amenable to standard enzyme immobilization approaches. Using particulate methane monooxygenase (pMMO), we create a biocatalytic polymer material that converts methane to methanol. We demonstrate embedding the material within a silicone lattice to create mechanically robust, gas-permeable membranes, and direct printing of micron-scale structures with controlled geometry. Remarkably, the enzymes retain up to 100% activity in the polymer construct. The printed enzyme-embedded polymer motif is highly flexible for future development and should be useful in a wide range of applications, especially those involving gas-liquid reactions.

SnO2 nanoslab as NO2 sensor: identification of the NO2 sensing mechanism on a SnO2 surface.

Among the various metal oxides, SnO2 has been most widely exploited as a semiconductor gas sensor for its excellent functionalities. Models illustrating the sensing mechanism of SnO2 have been proposed and tested to explain experimentally derived "power laws". The models, however, are often based on somewhat simplistic assumptions; for instance, the net charge transfer from an adsorbate to a sensor surface site is assumed to occur only by integer values independent of the crystallographic planes. In this work, we use layer-shaped SnO2 crystallites with one nanodimension (1ND-crystallites) as NO2 gas sensing elements under flat band conditions, and derive appropriate "power laws" by combining the dynamics of gas molecules on the sensor surface with a depletion theory of semiconductor. Our experimentally measured sensor response as a function of NO2 concentration when compared with the theoretically derived power law indicates that sensing occurs primarily through the chemisorption of single NO2 molecules at oxygen vacancy sites on the sensor surface.

New materials for methane capture from dilute and medium-concentration sources.

Methane (CH4) is an important greenhouse gas, second only to CO2, and is emitted into the atmosphere at different concentrations from a variety of sources. However, unlike CO2, which has a quadrupole moment and can be captured both physically and chemically in a variety of solvents and porous solids, methane is completely non-polar and interacts very weakly with most materials. Thus, methane capture poses a challenge that can only be addressed through extensive material screening and ingenious molecular-level designs. Here we report systematic in silico studies on the methane capture effectiveness of two different materials systems, that is, liquid solvents (including ionic liquids) and nanoporous zeolites. Although none of the liquid solvents appears effective as methane sorbents, systematic screening of over 87,000 zeolite structures led to the discovery of a handful of candidates that have sufficient methane sorption capacity as well as appropriate CH4/CO2 and/or CH4/N2 selectivity to be technologically promising.

Metal-organic frameworks (MOFs) as safer, structurally reinforced energetics.

Second-generation cobalt and zinc coordination architectures were obtained through efforts to stabilize extremely sensitive and energetic transition-metal hydrazine perchlorate ionic polymers. Partial ligand substitution by the tridentate hydrazinecarboxylate anion afforded polymeric 2D-sheet structures never before observed for energetic materials. Carefully balanced reaction conditions allowed the retention of the noncoordinating perchlorate anion in the presence of a strongly chelating hydrazinecarboxylate ligand. High-quality X-ray single-crystal structure determination revealed that the metal coordination preferences lead to different structural motifs and energetic properties, despite the nearly isoformulaic nature of the two compounds. Energetic tests indicate highly decreased sensitivity and DFT calculations suggest a high explosive performance for these remarkable structures.

Water-clustering in hygroscopic ionic liquids-an implicit solvent analysis.

Most ionic liquids are known to be hygroscopic to varying degrees, and that can be detrimental or useful depending upon the application in question. Water can accumulate slowly over hours or days to saturation levels corresponding to the humidity level. When designing or deploying a new ionic liquid it is important to be able to estimate its maximum moisture absorbing ability at the temperature and pressure of its operation. With this goal in mind we have carried out computational studies on three ionic liquid systems based on [BF(4)](-), [PF(6)](-), and [Tf(2)N](-) anions and 1-alkyl-3-methyl-imidazolium ([C(n)mim](+)) cations within an implicit solvent formalism. For highly hygroscopic systems like [C(n)mim][BF(4)] we find that non-iterative calculations with single water molecules can lead to significant underestimation of the maximum moisture content, while iterative calculations can result in miscibility behavior qualitatively different from experimental observations. On the other hand, the inclusion of small hydrogen-bonded water-clusters up to an appropriately chosen size is shown to yield better quantitative agreements with experimentally observed water uptake. Additionally, such calculations appear consistent with a number of thermodynamically interesting phase behaviors, including limited-solubility to full-miscibility transitions as a function of temperature and as a function of the alkyl chain length of the imidazolium cation. For hydrophobic systems like [C(n)mim][PF(6)] and [C(n)mim][Tf(2)N] the computed solubility (for each n) is found to have a smooth convergence behavior as a function of the largest cluster-size considered with the results for the larger clusters being close to that obtained by iterative calculations with single water molecules.

Ionic polymers as a new structural motif for high-energy-density materials.

Energetic materials have been used for nearly two centuries in military affairs and to cut labor costs and expedite laborious processes in mining, tunneling, construction, demolition, and agriculture, making a tremendous contribution to the world economy. Yet there has been little advancement in the development of altogether new energetic motifs despite long-standing research efforts to develop superior materials. We report the discovery of new energetic compounds of exceptionally high energy content and novel polymeric structure which avoid the use of lead and mercury salts common in conventional primary explosives. Laboratory tests indicate the remarkable performance of these Ni- and Co-based energetic materials, while DFT calculations indicate that these are possibly the most powerful metal-based energetic materials known to date, with heats of detonation comparable with those of the most powerful organic-based high explosives currently in use.

Synthesis of glycine-containing complexes in impacts of comets on early Earth.

Delivery of prebiotic compounds to early Earth from an impacting comet is thought to be an unlikely mechanism for the origins of life because of unfavourable chemical conditions on the planet and the high heat from impact. In contrast, we find that impact-induced shock compression of cometary ices followed by expansion to ambient conditions can produce complexes that resemble the amino acid glycine. Our ab initio molecular dynamics simulations show that shock waves drive the synthesis of transient C-N bonded oligomers at extreme pressures and temperatures. On post impact quenching to lower pressures, the oligomers break apart to form a metastable glycine-containing complex. We show that impact from cometary ice could possibly yield amino acids by a synthetic route independent of the pre-existing atmospheric conditions and materials on the planet.

Anomalous sound propagation and slow kinetics in dynamically compressed amorphous carbon.

We have performed molecular-dynamics simulations of dynamic compression waves propagating through amorphous carbon using the Tersoff potential and find that a variety of dynamic compression features appear for two different initial densities. These features include steady elastic shocks, steady chemically reactive shocks, unsteady elastic waves, and unsteady chemically reactive waves. We show how these features can be distinguished by analyzing time-dependent propagation speeds, time-dependent sound speeds, and comparison to multiscale shock technique (MSST) simulations. Understanding such features is a key challenge in quasi-isentropic experiments involving phase transformations. In addition to direct simulations of dynamic compression, we employ the MSST and find agreement with the direct method for this system for the shocks observed. We show how the MSST can be extended to include explicit material viscosity and demonstrate on an amorphous Lennard-Jones system.