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.

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.

3D Printing of High Viscosity Reinforced Silicone Elastomers.

Recent advances in additive manufacturing, specifically direct ink writing (DIW) and ink-jetting, have enabled the production of elastomeric silicone parts with deterministic control over the structure, shape, and mechanical properties. These new technologies offer rapid prototyping advantages and find applications in various fields, including biomedical devices, prosthetics, metamaterials, and soft robotics. Stereolithography (SLA) is a complementary approach with the ability to print with finer features and potentially higher throughput. However, all high-performance silicone elastomers are composites of polysiloxane networks reinforced with particulate filler, and consequently, silicone resins tend to have high viscosities (gel- or paste-like), which complicates or completely inhibits the layer-by-layer recoating process central to most SLA technologies. Herein, the design and build of a digital light projection SLA printer suitable for handling high-viscosity resins is demonstrated. Further, a series of UV-curable silicone resins with thiol-ene crosslinking and reinforced by a combination of fumed silica and MQ resins are also described. The resulting silicone elastomers are shown to have tunable mechanical properties, with 100-350% elongation and ultimate tensile strength from 1 to 2.5 MPa. Three-dimensional printed features of 0.4 mm were achieved, and complexity is demonstrated by octet-truss lattices that display negative stiffness.

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.

Anisotropic Hydrolysis Susceptibility in Deformed Polydimethylsiloxanes.

Chemical reactions involving the polydimethylsiloxane (PDMS) backbone can induce significant network rearrangements and ultimately degrade macro-scale mechanical properties of silicone components. Using two levels of quantum chemical theory, we identify a possible electronic driver for chemical susceptibility in strained PDMS chains and explore the complicated interplay between hydrolytic chain scissioning reactions, mechanical deformations of the backbone, water attack vector, and chain mobility. Density functional theory (DFT) calculations reveal that susceptibility to hydrolysis varies significantly with the vector for water attacks on silicon backbone atoms, which matches strain-induced anisotropic changes in the backbone electronic structure. Efficient semiempirical density functional tight binding (DFTB) calculations are shown to reproduce DFT predictions for select reaction pathways and facilitate more exhaustive explorations of configuration space. We show that concerted strains of the backbone must occur over at least a few monomer units to significantly increase hydrolysis susceptibility. In addition, we observe that sustaining tension across multiple monomer lengths by constraining molecular degrees of freedom further enhances hydrolysis susceptibility, leading to barrierless scission reactions for less substantial backbone deformations than otherwise. We then compute chain scission probabilities as functions of the backbone degrees of freedom, revealing complicated configurational interdependencies that impact the likelihood for hydrolytic degradation. The trends identified in our study suggest simple physical descriptions for the synergistic coupling between local mechanical deformation and environmental moisture in hydrolytic degradation of silicones.

Chemical Degradation Pathways in Siloxane Polymers Following Phenyl Excitations.

We use ensembles of quantum-based molecular dynamics simulations to predict the chemical reactions that follow radiation-induced excitations of phenyl groups in a model copolymer of polydimethylsiloxane and polydiphenylsiloxane. Our simulations span a wide range of highly porous and condensed phase densities and include both wet and dry conditions. We observe that in the absence of water, excited phenyl groups tend to abstract hydrogen from other methyl or phenyl side groups to produce benzene, with the under-hydrogenated group initiating subsequent intrachain cyclization reactions. These systems also yield minor products of diphenyl moieties formed by the complete abstraction of both phenyl groups from a single polydiphenylsiloxane subunit. In contrast, we find that the presence of water promotes the formation of free benzene and silanol side groups, reduces the likelihood for intrachain cyclization reactions, and completely suppresses the formation of diphenyl species. In addition, we predict that water plays a critical role in chain scission reactions, which indicates a possible synergistic effect between environmental moisture and radiation that could promote alterations of a larger polymer network. These results could have impact in interpreting accelerated aging experiments, where polymer decomposition reactions and network rearrangements are thought to have a significant effect on the ensuing mechanical properties.

Custom 3D Printable Silicones with Tunable Stiffness.

Silicone elastomers have broad versatility within a variety of potential advanced materials applications, such as soft robotics, biomedical devices, and metamaterials. A series of custom 3D printable silicone inks with tunable stiffness is developed, formulated, and characterized. The silicone inks exhibit excellent rheological behavior for 3D printing, as observed from the printing of porous structures with controlled architectures. Herein, the capability to tune the stiffness of printable silicone materials via careful control over the chemistry, network formation, and crosslink density of the ink formulations in order to overcome the challenging interplay between ink development, post-processing, material properties, and performance is demonstrated.

3D-Printing of Meso-structurally Ordered Carbon Fiber/Polymer Composites with Unprecedented Orthotropic Physical Properties.

Here we report the first example of a class of additively manufactured carbon fiber reinforced composite (AMCFRC) materials which have been achieved through the use of a latent thermal cured aromatic thermoset resin system, through an adaptation of direct ink writing (DIW) 3D-printing technology. We have developed a means of printing high performance thermoset carbon fiber composites, which allow the fiber component of a resin and carbon fiber fluid to be aligned in three dimensions via controlled micro-extrusion and subsequently cured into complex geometries. Characterization of our composite systems clearly show that we achieved a high order of fiber alignment within the composite microstructure, which in turn allows these materials to outperform equivalently filled randomly oriented carbon fiber and polymer composites. Furthermore, our AM carbon fiber composite systems exhibit highly orthotropic mechanical and electrical responses as a direct result of the alignment of carbon fiber bundles in the microscale which we predict will ultimately lead to the design of truly tailorable carbon fiber/polymer hybrid materials having locally programmable complex electrical, thermal and mechanical response.

Shape-morphing composites with designed micro-architectures.

Shape memory polymers (SMPs) are attractive materials due to their unique mechanical properties, including high deformation capacity and shape recovery. SMPs are easier to process, lightweight, and inexpensive compared to their metallic counterparts, shape memory alloys. However, SMPs are limited to relatively small form factors due to their low recovery stresses. Lightweight, micro-architected composite SMPs may overcome these size limitations and offer the ability to combine functional properties (e.g., electrical conductivity) with shape memory behavior. Fabrication of 3D SMP thermoset structures via traditional manufacturing methods is challenging, especially for designs that are composed of multiple materials within porous microarchitectures designed for specific shape change strategies, e.g. sequential shape recovery. We report thermoset SMP composite inks containing some materials from renewable resources that can be 3D printed into complex, multi-material architectures that exhibit programmable shape changes with temperature and time. Through addition of fiber-based fillers, we demonstrate printing of electrically conductive SMPs where multiple shape states may induce functional changes in a device and that shape changes can be actuated via heating of printed composites. The ability of SMPs to recover their original shapes will be advantageous for a broad range of applications, including medical, aerospace, and robotic devices.

Soft Materials with Recoverable Shape Factors from Extreme Distortion States.

Elastomeric polysiloxane nanocomposites with elongations of >5000% (more than 3× greater than any previously reported material) with excellent shape recovery are presented. Highly deformable materials are desirable for the fabrication of stretchable implants and microfluidic devices. No crosslinking or domain formation is observed by a variety of analytical techniques, suggesting that their elastomeric behavior is caused by polymer chain entanglements.

Correction: Nanoscale structure and superhydrophobicity of sp(2)-bonded boron nitride aerogels.

Correction for 'Nanoscale structure and superhydrophobicity of sp(2)-bonded boron nitride aerogels' by Thang Pham et al., Nanoscale, 2015, 7, 10449-10458.

Nanoscale structure and superhydrophobicity of sp(2)-bonded boron nitride aerogels.

Aerogels have much potential in both research and industrial applications due to their high surface area, low density, and fine pore size distribution. Here we report a thorough structural study of three-dimensional aerogels composed of highly crystalline sp(2)-bonded boron nitride (BN) layers synthesized by a carbothermic reduction process. The structure, crystallinity and bonding of the as-prepared BN aerogels are elucidated by X-ray diffraction, (11)B nuclear magnetic resonance, transmission electron microscopy, and resonant soft X-ray scattering. The macroscopic roughness of the aerogel's surface causes it to be superhydrophobic with a contact angle of ∼155° and exhibit high oil uptake capacity (up to 1500 wt%). The oil can be removed from the BN aerogel by oxidizing in air without damaging the crystalline porous structure of the aerogel or diminishing its oil absorption capacity.

Advances in modeling sorption and diffusion of moisture in porous reactive materials.

Water-vapor-uptake experiments were performed on a silica-filled poly(dimethylsiloxane) (PDMS) network and modeled by using two different approaches. The data was modeled by using established methods and the model parameters were used to predict moisture uptake in a sample. The predictions are reasonably good, but not outstanding; many of the shortcomings of the modeling are discussed. A high-fidelity modeling approach is derived and used to improve the modeling of moisture uptake and diffusion. Our modeling approach captures the physics and kinetics of diffusion and adsorption/desorption, simultaneously. It predicts uptake better than the established method; more importantly, it is also able to predict outgassing. The material used for these studies is a filled-PDMS network; physical interpretations concerning the sorption and diffusion of moisture in this network are discussed.

Electron Beam Crosslinked Polyurethane Shape Memory Polymers with Tunable Mechanical Properties.

Novel electron beam crosslinked polyurethane shape memory polymers with advanced processing capabilities and tunable thermomechanical properties have been synthesized and characterized. We demonstrate the ability to manipulate crosslink density in order to finely tune rubbery modulus, strain capacity, ultimate tensile strength, recovery stress, and glass transition temperature. This objective is accomplished for the first time in a low-molecular-weight polymer system through the precise engineering of thermoplastic resin precursors suitable for mass thermoplastic processing. Neurovascular stent prototypes were fabricated by dip-coating and laser machining to demonstrate processability.

The stability of polysiloxanes incorporating nano-scale physical property modifiers.

Reported here is the synthesis and subsequent characterization of the physical and chemical properties of novel polysiloxane elastomers modified with a series of polyhedraloligomericsilsequioxane (POSS) molecular silicas. The physical properties of the formulated nanocomposite systems have been characterized with a combination of dynamic mechanical analysis (DMA), broadband dielectric spectroscopy (BDS) and confocal Raman microscopy. The results of the physical property characterization demonstrate that the incorporation of low levels (1-4% by wt.) of POSS particles into the polysiloxane network leads to significant improvements in the mechanical properties of the elastomer and significantly alters the motional chain dynamics of the system as a whole. The results of studies performed to assess the long-term stability of these novel nanocomposite systems have demonstrated that POSS physical property modifiers can significantly alter the thermal stability of polysiloxane elastomers. Physically dispersed POSS has also been shown in some cases to be both mobile and disruptive within the polysiloxane networks, agglomerating into domains on a micron scale and migrating to the surface of the elastomers. This work demonstrates both the potential of POSS nanoparticles as physical property modifiers and describes the effects of POSS on the physical and chemical stability of polysiloxane systems.