Debra J. Audus

Striped, Ellipsoidal Particles by Controlled Assembly of Diblock Copolymers

Control of interfacial interactions leads to a dramatic change in shape and morphology for particles based on poly(styrene-b-2-vinylpyridine) diblock copolymers. Key to these changes is the addition of Au-based surfactant nanoparticles (SNPs) which are adsorbed at the interface between block copolymer-containing emulsion droplets and the surrounding amphiphilic surfactant to afford asymmetric, ellipsoid particles. The mechanism of formation for these novel nanostructures was investigated by systematically varying the volume fraction of SNPs, with the results showing the critical nature that the segregation of SNPs to specific interfaces plays in controlling structure. A theoretical description of the system allows the size distribution and aspect ratio of the asymmetric block copolymer colloidal particles to be correlated with the experimental results.

A Facile Synthesis of Dynamic, Shape‐Changing Polymer Particles

We herein report a new facile strategy to ellipsoidal block copolymer nanoparticles that exhibit a pH-triggered anistropic swelling profile. In a first step, elongated particles with an axially stacked lamellae structure are selectively prepared by utilizing functional surfactants to control the phase separation of symmetric polystyrene-b-poly(2-vinylpyridine) (PS-b-P2VP) in dispersed droplets. In a second step, the dynamic shape change is realized by cross-linking the P2VP domains, thereby connecting glassy PS discs with pH-sensitive hydrogel actuators.

Small Angle Neutron Scattering Study of Complex Coacervate Micelles and Hydrogels Formed from Ionic Diblock and Triblock Copolymers

A complex coacervate is a fluid phase that results from the electrostatic interactions between two oppositely charged macromolecules. The nature of the coacervate core structure of hydrogels and micelles formed from complexation between pairs of diblock or triblock copolymers containing oppositely charged end-blocks as a function of polymer and salt concentration was investigated. Both ABA triblock copolymers of poly[(allyl glycidyl ether)-b-(ethylene oxide)-b-(allyl glycidyl ether)] and analogous poly[(allyl glycidyl ether)-b-(ethylene oxide)] diblock copolymers, which were synthesized to be nearly one-half of the symmetrical triblock copolymers, were studied. The poly(allyl glycidyl ether) blocks were functionalized with either guanidinium or sulfonate groups via postpolymerization modification. Mixing of oppositely charged block copolymers resulted in the formation of nanometer-scale coacervate domains. Small angle neutron scattering (SANS) experiments were used to investigate the size and spacing of the coacervate domains. The SANS patterns were fit using a previously vetted, detailed model consisting of polydisperse core-shell micelles with a randomly distributed sphere or body-centered cubic (BCC) structure factor. For increasing polymer concentration, the size of the coacervate domains remained constant while the spatial extent of the poly(ethylene oxide) (PEO) corona decreased. However, increasing salt concentration resulted in a decrease in both the coacervate domain size and the corona size due to a combination of the electrostatic interactions being screened and the shrinkage of the neutral PEO blocks. Additionally, for the triblock copolymers that formed BCC ordered domains, the water content in the coacervate domains was calculated to increase from approximately 16.8% to 27.5% as the polymer concentration decreased from 20 to 15 wt %.

Coupling of isotropic and directional interactions and its effect on phase separation and self-assembly

The interactions of molecules and particles in solution often involve an interplay between isotropic and highly directional interactions that lead to a mutual coupling of phase separation and self-assembly. This situation arises, for example, in proteins interacting through hydrophobic and charged patch regions on their surface and in nanoparticles with grafted polymer chains, such as DNA. As a minimal model of complex fluids exhibiting this interaction coupling, we investigate spherical particles having an isotropic interaction and a constellation of five attractive patches on the particles surface. Monte Carlo simulations and mean-field calculations of the phase boundaries of this model depend strongly on the relative strength of the isotropic and patch potentials, where we surprisingly find that analytic mean-field predictions become increasingly accurate as the directional interactions become increasingly predominant. We quantitatively account for this effect by noting that the effective interaction range increases with increasing relative directional to isotropic interaction strength. We also identify thermodynamic transition lines associated with self-assembly, extract the entropy and energy of association, and characterize the resulting cluster properties obtained from simulations using percolation scaling theory and Flory-Stockmayer mean-field theory. We find that the fractal dimension and cluster size distribution are consistent with those of lattice animals, i.e., randomly branched polymers swollen by excluded volume interactions. We also identify a universal functional form for the average molecular weight and a nearly universal functional form for a scaling parameter characterizing the cluster size distribution. Since the formation of branched clusters at equilibrium is a common phenomenon in nature, we detail how our analysis can be used in experimental characterization of such associating fluids.

A Hybrid Human-computer Approach to the Extraction of Scientific Facts from the Literature

A wealth of valuable data is locked within the millions of research articles published each year. Reading and extracting pertinent information from those articles has become an unmanageable task for scientists. This problem hinders scientific progress by making it hard to build on results buried in literature. Moreover, these data are loosely structured, encoded in manuscripts of various formats, embedded in different content types, and are, in general, not machine accessible. We present a hybrid human-computer solution for semi-automatically extracting scientific facts from literature. This solution combines an automated discovery, download, and extraction phase with a semi-expert crowd assembled from students to extract specific scientific facts. To evaluate our approach we apply it to a challenging molecular engineering scenario, extraction of a polymer property: the Flory-Huggins interaction parameter. We demonstrate useful contributions to a comprehensive database of polymer properties.

Polymer Informatics: Opportunities and Challenges

We are entering an era where large volumes of scientific data, coupled with algorithmic and computational advances, can reduce both the time and cost of developing new materials. This emerging field known as materials informatics has gained acceptance for a number of classes of materials, including metals and oxides. In the particular case of polymer science, however, there are important challenges that must be addressed before one can start to deploy advanced machine learning approaches for designing new materials. These challenges are primarily related to the manner in which polymeric systems and their properties are reported. In this viewpoint, we discuss the opportunities and challenges for making materials informatics as applied to polymers, or equivalently polymer informatics, a reality.

Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline

The emerging field of materials informatics has the potential to greatly reduce time-to-market and development costs for new materials. The success of such efforts hinges on access to large, high-quality databases of material properties. However, many such data are only to be found encoded in text within esoteric scientific articles, a situation that makes automated extraction difficult and manual extraction time-consuming and error-prone. To address this challenge, we present a hybrid Information Extraction (IE) pipeline to improve the machine-human partnership with respect to extraction quality and person-hours, through a combination of rule-based, machine learning, and crowdsourcing approaches. Our goal is to leverage computer and human strengths to alleviate the burden on human curators by automating initial extraction tasks before prioritizing and assigning specialized curation tasks to humans with different levels of training: using non-experts for straightforward tasks such as validation of higher accuracy results (e.g., completing partial facts) and domain experts for low-certainty results (e.g., reviewing specialized compound labels). To validate our approaches, we focus on the task of extracting the glass transition temperature of polymers from published articles. Applying our approaches to 6 090 articles, we have so far extracted 259 refined data values. We project that this number will grow considerably as we tune our methods and process more articles, to exceed that found in standard, expert-curated polymer data handbooks while also being easier to keep up-to-date. The freely available data can be found on our Polymer Properties Predictor and Database website at http://pppdb.uchicago.edu.

Field-Based Simulations of Nanostructured Polyelectrolyte Gels

Nanostructured, responsive hydrogels composed of oppositely charged triblock copolymers with charged end-blocks and neutral, hydrophilic mid-blocks in aqueous solution were recently discovered. Due to electrostatic interactions, the end-blocks microphase separate and form physical cross-links that are bridged by the mid-blocks. Since these hydrogels are hydrophilic and have the ability to respond to a variety of stimuli including temperature and salt concentration, they are promising for a variety of biomedical applications including, but not limited to, drug delivery and tissue scaffolds. For such applications, there is a need to understand how to control the structure of the hydrogel. To this end, we use a new, efficient model along with self-consistent field theory to determine the structure as a function of polymer concentration and end-block fraction. After identifying numerous phases including a sphere phase, a hexagonally packed cylinder phase, a lamellar phase, and regions of phase coexistence, we determine how the polymer functionality can be tuned to manipulate the resulting phase diagram.