Design of Polymer Nanodielectrics for Capacitive Energy Storage.

Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson's equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics.

Machine-Learning-Assisted Understanding of Polymer Nanocomposites Composition-Property Relationship: A Case Study of NanoMine Database.

The NanoMine database, one of two nodes in the MaterialsMine database, is a new materials data resource that collects annotated data on polymer nanocomposites (PNCs). This work showcases the potential of NanoMine and other materials data resources to assist fundamental materials understanding and therefore rational materials design. This specific case study is built around studying the relationship between the change in the glass transition temperature Tg (ΔTg) and key descriptors of the nanofillers and the polymer matrix in PNCs. We sifted through data from over 2000 experimental samples curated into NanoMine, trained a decision tree classifier to predict the sign of PNC ΔTg, and built a multiple power regression metamodel to predict ΔTg. The successful model used key descriptors including composition, nanoparticle volume fraction, and interfacial surface energy. The results demonstrate the power of using aggregated materials data to gain insight and predictive capability. Further analysis points to the importance of additional analysis of parameters from processing methodologies and continuously adding curated data sets to increase the sample pool size.

Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning.

Knowing the dielectric properties of the interfacial region in polymer nanocomposites is critical to predicting and controlling dielectric properties. They are, however, difficult to characterize due to their nanoscale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. This paper demonstrates a combined EFM and machine learning (ML) approach to measuring interfacial permittivity in 50 nm silica particles in a PMMA matrix. We show that ML models trained to finite-element simulations of the electric field profile between the EFM tip and nanocomposite surface can accurately determine the interface permittivity of functionalized nanoparticles. It was found that for the particles with a polyaniline brush layer, the interfacial region was detectable (extrinsic interface). For bare silica particles, the intrinsic interface was detectable only in terms of having a slightly higher or lower permittivity. This approach fully accounts for the complex interplay of filler, matrix, and interface permittivity on the force gradients measured in EFM that are missed by previous semianalytic approaches, providing a pathway to quantify and design nanoscale interface dielectric properties in nanodielectric materials.

The materials tetrahedron has a "digital twin".

Abstract: For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework-adapted from the concept of a "digital twin"-which offers a nexus joining materials science and information science. We believe this high-level framework, the materials-information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward. Impact statement: This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials-information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)-factors that recognize how systems impact and interact with other systems-in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead.

In Situ Atomic Force Microscopy Tracking of Nanoparticle Migration in Semicrystalline Polymers.

We present in situ tracking of silica nanoparticle (NP) migration from a poly(ethylene oxide) (PEO) melt into interlamellar region using in situ atomic force microscopy (AFM). Our results confirm the previous hypothesis that NPs migrate into the interlamellar regions at crystallization growth rates smaller than a critical value under isothermal conditions. Under these slow crystallization conditions, bare silica NPs are rejected as defects by the growing crystal of PEO, and the in situ imaging on the large (50 nm) NPs helps track the migration into the amorphous zones. We extend this AFM technique to estimate lamellar growth rates that correlate with spherulite growth rates determined by polarized light optical microscopy (PLOM) but at smaller undercoolings than are typical for PLOM.

FAIR and Interactive Data Graphics from a Scientific Knowledge Graph.

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

Polymer Nanocomposite Data: Curation, Frameworks, Access, and Potential for Discovery and Design.

With the advent of the materials genome initiative (MGI) in the United States and a similar focus on materials data around the world, a number of materials data resources and associated vocabularies, tools, and repositories have been developed. While the majority of systems focus on slices of computational data with an emphasis on metallic alloys, NanoMine is an open source platform with the goal of curating and storing widely varying experimental data on polymer nanocomposites (polymers doped with nanoparticles) and providing access to characterization and analysis tools with the long-term objective of promoting facile nanocomposite design. Data on over 2500 samples from the literature and individual laboratories has been curated to date into NanoMine, including 230 samples from the papers bound in this virtual issue. This virtual issue represents an experiment of the flexibility of the data repository to capture the unique experimental metadata requirements of many data sets at one time and to challenge the authors to participate in the curation of their research data associated with a given publication. In principle, NanoMine offers a FAIR platform in which data published in papers becomes directly Findable and Accessible via simple search tools, with open metadata standards that are Interoperable with larger materials data registries, and allows easy Reuse of data, e.g. benchmarking against new results. Our hope is that with time, platforms such as this one could capture much of the newly published data on materials and form nodes in an interconnected materials data ecosystem which would allow researchers to robustly archive their data, add to the growing body of readily accessible data, and enable new forms of discovery by application of data analysis and design tools.

Nanoparticle Organization by Growing Polyethylene Crystal Fronts.

We investigate the crystallization-induced ordering of C18 grafted 14 nm diameter spherical silica nanoparticles (NPs) in a short chain (Mw = 4 kDa, DM ≈ 2.3) polyethylene and a commercial high-density polyethylene (Mw = 152 kDa, DM ≈ 3.2) matrix. For slow isothermal crystallization of the low molecular weight matrix, the NPs segregate into the interlamellar regions. This result establishes the generality of our earlier work on poly(ethylene oxide) based materials and suggests that crystallization can be used to control NP dispersion across different polymer classes. The incompatibility between the particles and the matrix in the Mw = 152 kDa results in a competition between filler organization and filler agglomeration. The mechanical properties improve due to the addition of NPs and are further enhanced by particle organization, even for the case of the macrophase-separated mixtures in the Mw = 152 kDa matrix. In contrast, dielectric behavior is strongly affected by the scale of NP organization, with the lower molecular weight matrix showing more significant increases in permittivity due to the local scale of NP ordering.

Size effects in plasma-enhanced nano-transfer adhesion.

Plasma bonding and layer-by-layer transfer molding have co-existed for decades, and here we offer a combination of the two that drives both techniques to the nanoscale. Using fluorinated elastomeric stamps, lines of plasma-treated poly(dimethylsiloxane) (PDMS) were stacked into multi-layer woodpile structures via transfer molding, and we observe a pronounced size effect wherein nanoscale lines (≤280 nm period) require ultra-low plasma dose (<20 J) and fail to bond at the much higher range of plasma dose (600 J to 900 J) recommended in the PDMS plasma bonding literature. The size effect appears to be related to the thickness of the oxide film that develops on the PDMS surface during treatment, and we employ an empirical relationship, , to estimate the thickness of this film in the low plasma dose (<100 J) regime. The empirical relationship shows good agreement with existing studies on plasma-treated PDMS oxide film thickness, and the transition between successful transfer and delamination coincides well with a critical value of the oxide thickness relative to the thickness of the transferred layer. Through peel testing, we identified a transition in failure mode of flat plasma-bonded PDMS consistent with the optimal plasma dose in previous literature but otherwise observed strong, irreversible adhesion even at ultra-low plasma dose. By demonstrating the importance of low plasma dose for plasma-enhanced nano-transfer adhesion, these results advance our understanding of irreversible adhesion of soft materials at the nanoscale and open up new opportunities within the relatively unstudied ultra-low dose plasma treatment regime.

Wetting Regimes for Residual-Layer-Free Transfer Molding at Micro- and Nanoscales.

Transfer molding offers a low-cost approach to large-area fabrication of isolated structures in a variety of materials when recessed features of the open-faced mold are filled without leaving a residual layer on the plateaus of the mold. Considering both macroscale dewetting and microscale capillary flow, a proposed map of wetting regimes for blade meniscus coating provides a guide for achieving discontinuous dewetting at maximum throughput. Dependence of meniscus morphology on the azimuthal orientation of the stamp provides insight into the dominant mechanisms for discontinuous dewetting of one-dimensional (1-D) patterns. Critical meniscus velocity is measured and residual-layer-free filling is demonstrated for 1-D patterned soft molds (stamps) with periods ranging from 140 nm to 6 µm. Transfer of isolated lines, and multilayer woodpile structures were achieved through plasma bonding. These results are relevant to other roll-to-roll compatible processes for scalable production of high-resolution structures across large areas.

Tunable Multiscale Nanoparticle Ordering by Polymer Crystallization.

While ∼75% of commercially utilized polymers are semicrystalline, the generally low mechanical modulus of these materials, especially for those possessing a glass transition temperature below room temperature, restricts their use for structural applications. Our focus in this paper is to address this deficiency through the controlled, multiscale assembly of nanoparticles (NPs), in particular by leveraging the kinetics of polymer crystallization. This process yields a multiscale NP structure that is templated by the lamellar semicrystalline polymer morphology and spans NPs engulfed by the growing crystals, NPs ordered into layers in the interlamellar zone [spacing of [Formula: see text] (10-100 nm)], and NPs assembled into fractal objects at the interfibrillar scale, [Formula: see text] (1-10 µm). The relative fraction of NPs in this hierarchy is readily manipulated by the crystallization speed. Adding NPs usually increases the Young's modulus of the polymer, but the effects of multiscale ordering are nearly an order of magnitude larger than those for a state where the NPs are not ordered, i.e., randomly dispersed in the matrix. Since the material's fracture toughness remains practically unaffected in this process, this assembly strategy allows us to create high modulus materials that retain the attractive high toughness and low density of polymers.

Core@Double-Shell Structured Nanocomposites: A Route to High Dielectric Constant and Low Loss Material.

This work reports the advances of utilizing a core@double-shell nanostructure to enhance the electrical energy storage capability and suppress the dielectric loss of polymer nanocomposites. Two types of core@double-shell barium titanate (BaTiO3) matrix-free nanocomposites were prepared using a surface initiated atom transfer radical polymerization (ATRP) method to graft a poly(2-hydroxylethyle methacrylate)-block-poly(methyl methacrylate) and sodium polyacrylate-block-poly(2-hydroxylethyle methacrylate) block copolymer from BaTiO3 nanoparticles. The inner shell polymer is chosen to have either high dielectric constant or high electrical conductivity to provide large polarization, while the encapsulating outer shell polymer is chosen to be more insulating as to maintain a large resistivity and low loss. Finite element modeling was conducted to investigate the dielectric properties of the fabricated nanocomposites and the relaxation behavior of the grafted polymer. It demonstrates that confinement of the more conductive (lossy) phase in this multishell nanostructure is the key to achieving a high dielectric constant and maintaining a low loss. This promising multishell strategy could be generalized to a variety of polymers to develop novel nanocomposites.

Ligand engineering of polymer nanocomposites: from the simple to the complex.

One key to optimizing the performance of polymer nanocomposites for high-tech applications is surface ligand engineering of the nanofiller, which has been used to either tune the nanofiller morphology or introduce additional functionalities. Ligand engineering can be relatively simple such as a single population of short molecules on the nanoparticle surface designed for matrix compatibility. It can also have complexity that includes bimodal (or multimodal) populations of ligands that enable relatively independent control of enthalpic and entropic interactions between the nanofiller and matrix as well as introduce additional functionality and dynamic control. In this Spotlight on Applications, we provide a brief review into the use of brush ligands to tune the thermodynamic interactions between nanofiller and matrix and then focus on the potential for surface ligand engineering to create exciting nanocomposites properties for optoelectronic and dielectric applications.

Enzyme-based listericidal nanocomposites.

Cell lytic enzymes represent an alternative to chemical decontamination or use of antibiotics to kill pathogenic bacteria, such as listeria. A number of phage cell lytic enzymes against listeria have been isolated and possess listericidal activity; however, there has been no attempt to incorporate these enzymes onto surfaces. We report three facile routes for the surface incorporation of the listeria bacteriophage endolysin Ply500: covalent attachment onto FDA approved silica nanoparticles (SNPs), incorporation of SNP-Ply500 conjugates into a thin poly(hydroxyethyl methacrylate) film; and affinity binding to edible crosslinked starch nanoparticles via construction of a maltose binding protein fusion. These Ply500 formulations were effective in killing L. innocua (a reduced pathogenic surrogate) at challenges up to 10(5) CFU/ml both in non-growth sustaining PBS as well as under growth conditions on lettuce. This strategy represents a new route toward achieving highly selective and efficient pathogen decontamination and prevention in public infrastructure.

Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers.

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this "Materials Genomics" approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Bimodal surface ligand engineering: the key to tunable nanocomposites.

Tuning the dispersion of inorganic nanoparticles within organic matrices is critical to optimizing polymer nanocomposite properties and is intrinsically difficult due to their strong enthalpic incompatibility. Conventional attempts to use polymer brushes to control nanoparticle dispersion are challenged by the need for high graft density to reduce particle core-core attractions and the need for low graft density to reduce the entropic penalty for matrix penetration into the brush. We validated a parametric phase diagram previously reported by Pryamtisyn et al. (Pryamtisyn, V.; Ganesan, V.; Panagiotopoulos, A. Z.; Liu, H.; Kumar, S. K. Modeling the Anisotropic Self-Assembly of Spherical Polymer-Grafted Nanoparticles. J. Chem. Phys.2009, 131, 221102) for predicting dispersion of monomodal-polymer-brush-modified nanoparticles in polymer matrices. The theoretical calculation successfully predicted the experimental observation that the monomodal-poly(dimethyl siloxane) (PDMS)-brush-grafted TiO(2) nanoparticles can only be well dispersed within a small molecular weight silicone matrix. We further extended the parametric phase diagram to analyze the dispersion behavior of bimodal-PDMS-brush-grafted particles, which is also in good agreement with experimental results. Utilizing a bimodal grafted polymer brush design, with densely grafted short brushes to shield particle surfaces and sparsely grafted long brushes that favor the entanglement with matrix chains, we dispersed TiO(2) nanoparticles in high molecular weight commercial silicone matrices and successfully prepared thick (about 5 mm) transparent high-refractive-index TiO(2)/silicone nanocomposites.

Preparation and optical properties of indium tin oxide/epoxy nanocomposites with polyglycidyl methacrylate grafted nanoparticles.

Visibly highly transparent indium tin oxide (ITO)/epoxy nanocomposites were prepared by dispersing polyglycidyl methacrylate (PGMA) grafted ITO nanoparticles into a commercial epoxy resin. The oleic acid stabilized, highly crystalline, and near monodisperse ITO nanoparticles were synthesized via a nonaqueous synthetic route with multigram batch quantities. An azido-phosphate ligand was synthesized and used to exchange with oleic acid on the ITO surface. The azide terminal group allows for the grafting of epoxy resin compatible PGMA polymer chains via Cu(I) catalyzed alkyne-azide "click" chemistry. Transmission electron microscopy (TEM) observation shows that PGMA grafted ITO particles were homogeneously dispersed within the epoxy matrix. Optical properties of ITO/epoxy nanocomposites with different ITO concentrations were studied with an ultraviolet-visible-near-infrared (UV-vis-NIR) spectrometer. All the ITO/epoxy nanocomposites show more than 90% optical transparency in the visible light range and absorption of UV light from 300 to 400 nm. In the near-infrared region, ITO/epoxy nanocomposites demonstrate low transmittance and the infrared (IR) transmission cutoff wavelength of the composites shifts toward the lower wavelength with increased ITO concentration. The ITO/epoxy nanocomposites were applied onto both glass and plastic substrates as visibly transparent and UV/IR opaque optical coatings.

Anisotropic self-assembly of spherical polymer-grafted nanoparticles.

It is easy to understand the self-assembly of particles with anisotropic shapes or interactions (for example, cobalt nanoparticles or proteins) into highly extended structures. However, there is no experimentally established strategy for creating a range of anisotropic structures from common spherical nanoparticles. We demonstrate that spherical nanoparticles uniformly grafted with macromolecules ('nanoparticle amphiphiles') robustly self-assemble into a variety of anisotropic superstructures when they are dispersed in the corresponding homopolymer matrix. Theory and simulations suggest that this self-assembly reflects a balance between the energy gain when particle cores approach and the entropy of distorting the grafted polymers. The effectively directional nature of the particle interactions is thus a many-body emergent property. Our experiments demonstrate that this approach to nanoparticle self-assembly enables considerable control for the creation of polymer nanocomposites with enhanced mechanical properties. Grafted nanoparticles are thus versatile building blocks for creating tunable and functional particle superstructures with significant practical applications.

Quantitative equivalence between polymer nanocomposites and thin polymer films.

The thermomechanical responses of polymers, which provide limitations to their practical use, are favourably altered by the addition of trace amounts of a nanofiller. However, the resulting changes in polymer properties are poorly understood, primarily due to the non-uniform spatial distribution of nanoparticles. Here we show that the thermomechanical properties of 'polymer nanocomposites' are quantitatively equivalent to the well-documented case of planar polymer films. We quantify this equivalence by drawing a direct analogy between film thickness and an appropriate experimental interparticle spacing. We show that the changes in glass-transition temperature with decreasing interparticle spacing for two filler surface treatments are quantitatively equivalent to the corresponding thin-film data with a non-wetting and a wetting polymer-particle interface. Our results offer new insights into the role of confinement on the glass transition, and we conclude that the mere presence of regions of modified mobility in the vicinity of the particle surfaces, that is, a simple two-layer model, is insufficient to explain our results. Rather, we conjecture that the glass-transition process requires that the interphase regions surrounding different particles interact.