Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities.

Polymer processing, purification, and self-assembly have significant roles in the design of polymeric materials. Understanding how polymers behave in solution (e.g., their solubility, chemical properties, etc.) can improve our control over material properties via their processing-structure-property relationships. For many decades the polymer science community has relied on thermodynamic and physics-based models to aid in this endeavor, but all rely on disparate data sets and use-case scenarios. Hence, there are still significant challenges to predict a priori the solubility of a polymer, whether it is for selecting sustainable solvents, obtaining thermodynamic parameters for phase separation, or navigating the coexistence curve. This perspective aims to discuss the different approaches of applying computational tools to predict polymer solubility, with a significant focus on machine learning techniques to capture the rapid progress in that space. We examine challenges and opportunities that remain for creating a comprehensive solubility toolset that can accelerate the design of a broad range of applications including films, membranes, and pharmaceuticals.

Rearing density and food variety impact growth, development, and survival of larvae in the declining amphibian, Pseudacris maculata.

Boreal chorus frogs (Pseudacris maculata Agassiz 1850) are a widespread amphibian in North America, but several populations are in decline. Specifically, we are developing captive breeding and reintroduction methods for the Great Lakes/St. Lawrence-Canadian Shield population. Here we present the effects of tadpole density, food variety, and addition of supplemental minerals to rearing water on the growth, development, and survival during the larval and metamorph/juvenile stages. We conducted two experiments using a factorial design. We found that low tadpole density (1 vs. 2 tadpoles/L) and high food variety (five food items vs. three food items) significantly increased tadpole body length and Gosner stage after 2 weeks (p < .001), increased survival to metamorphosis (p < .001), decreased time to metamorphosis (p < .001), and increased weight after metamorphosis (p < .001). On average, tadpoles in the high density/low food treatment, compared to the low density/high food, were 25% smaller after 2 weeks, had 3.9× lower survival to metamorphosis, took 1.25× longer to reach metamorphosis, and weighed 1.5× less after metamorphosis. In contrast, neither density (0.5 vs. 1 tadpole/L) nor mineral supplemented water affected growth and development, but tadpole survival was higher at 1 tadpole/L. Our findings demonstrate the feasibility of rearing boreal chorus frogs in captivity and provide guidelines for rearing this and similar species in a laboratory environment.

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning.

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

Life history traits and reproductive ecology of North American chorus frogs of the genus Pseudacris (Hylidae).

Amphibian biodiversity is declining globally, with over 40% of species being considered threatened to become extinct. Crucial to the success of conservation initiatives are a comprehensive understanding of life history and reproductive ecology of target species. Here we provide an overview of the Pseudacris genus, including breeding behaviour, reproduction, development, survival and longevity. We present an updated distribution map of the 18 species found throughout North America. We also summarize the conservation status at the national and subnational (state, provincial, and territorial) levels, in Canada, USA, and Mexico, to evaluate the relationship between life history traits and extinction risk. Results show a high degree of consistency in the life history traits of Pseudacris species considering their relative diversity and wide distribution in North America. However, data are lacking for several species, particularly in the Fat Frog and West Coast clades, causing some uncertainties and discrepancies in the literature. We also found that the most threatened populations of chorus frog were located in the east coast of the USA, potentially as a result of increased levels of anthropogenic disturbance. We suggest that the similarities in life history traits among chorus frog species provides an opportunity for collaboration and united efforts for the conservation of the genus.

Deep Learning of Binary Solution Phase Behavior of Polystyrene.

Predicting binary solution phase behavior of polymers has remained a challenge since the early theory of Flory-Huggins, hindering the processing, synthesis, and design of polymeric materials. Herein, we take a complementary data-driven approach by building a machine learning framework to make fast and accurate predictions of polymer solution cloud point temperatures. Using polystyrene, both upper and lower critical solution temperatures are predicted within experimental uncertainty (1-2 °C) with a deep neural network, Gaussian process regression (GPR) model, and a combination of polymer, solvent, and state features. The GPR model also enables intelligent exploration of solution phase space, where as little as 25 cloud points are required to make predictions within 2 °C for polystyrene of arbitrary molecular weight in cyclohexane. This study demonstrates the effectiveness of machine learning for the prediction of liquid-liquid equilibrium of polymer solutions and establishes a framework to incorporate other polymers and complex macromolecular architectures.

Uniaxial Deformation and Crazing in Glassy Polymer-Grafted Nanoparticle Ultrathin Films.

The deformation behavior of neat, glassy polymer-grafted nanoparticle (PGN) monolayer films is studied using coarse-grained molecular dynamics simulations and experiments on polystyrene-grafted silica. In both the simulations and experiments, apparent crazing behavior is observed during deformation. The PGN systems show a relatively more uniform, perforated sheet craze structure and significantly higher strain at break than reference homopolymers of the same length. Short chain, unentangled PGN monolayers are also simulated for comparison; these are brittle and break apart without crazing. The entangled PGN simulations are analyzed in detail for systems at both high and moderate graft density. Stress-strain curves show three distinct regions: yielding and strain localization, craze widening, and strain hardening preceding catastrophic failure. The PGN stress-strain behavior appears more similar to that of longer chain, highly entangled homopolymer films than to the reference homopolymer films of the same length as the graft chains, suggesting that the particles effectively add additional entanglement points. The moderate graft density particles have higher strain-to-failure and maximum stress than the high graft density particles. We suggest this increased robustness for lower graft density systems is due to their increased interpenetration of graft chains between neighboring particles, which leads to increased interparticle entanglements per chain.

Modeling individual and pairs of adsorbed polymer-grafted nanoparticles: structure and entanglements.

We analyze the canopy structure and entanglement network of isolated polymer-grafted nanoparticles (PGNs) adsorbed on a surface. As expected, increasing the monomer-surface adsorption strength causes the polymer chains to spread out to increase contact with the surface, leading to a canopy shape that is in qualitative agreement with recent experimental results. We compare height profiles and other structural features of four PGN systems to show the separate and combined effects of increasing chain length and graft density. At moderate graft density and low surface attraction strength, nearby PGN canopies interpenetrate substantially and their combined shape is similar to that of a single PGN canopy. At high graft density or surface interaction, the interparticle spacing increases significantly. We use a geometrical primitive path analysis to calculate average entanglement properties including canopy-canopy entanglements between pairs of PGNs. The longer chain systems are well entangled at both graft densities considered, and we find that as the monomer-surface interaction strength is increased (and the interparticle distance increases), entanglements between the two PGNs are reduced. We find that the number of inter-PGN entanglements per chain is slightly larger at the lower graft density, likely because steric constraints at high graft density tend to reduce interparticle entanglements.