Ultrathin films containing polymer-grafted nanoparticles (PGNs) show promise for use in hybrid electronics and high energy density materials. In this work, we use a coarse-grained model to simulate a hexagonally packed monolayer of PGNs adsorbed on a smooth surface that is attractive to both nanoparticles and polymer chains. We find that decreasing graft density at the same graft length increases interpenetration of the polymer-grafted layers, as expected. We quantify both overall and interparticle entanglements (between polymers grafted to different PGNs). While the higher graft density particles have a higher overall number of entanglements per chain, the lower graft density particles have higher interparticle entanglements per chain due to their increased chain interpenetration. Finally, we apply uniaxial tensile deformation to the monolayers; the peak stress occurs at lower strain values for higher graft density particles, which is attributed to the relatively lower number of interparticle entanglements. Our analysis provides a molecular picture of how decreasing graft density leads to better interpenetration, increased interparticle entanglements, and increased toughness of PGN monolayers, though it can lead to slightly less uniformly spaced monolayers, in agreement with previous experimental observations. These tradeoffs are crucial to understand for the design of robust, well-ordered inorganic−organic hybrid films.

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.

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.

No abstract

The phase behavior of polymers in solution is crucial to many applications in polymer processing, synthesis, self-assembly, and purification. Quantitative prediction of polymer solubility space for an arbitrary polymer–solvent pair and across a large composition range is challenging. Qualitative agreement is provided by many current theoretical models, but only a portion of the phase space is quantitatively predicted. Here, we utilize a curated database for binary polymer solutions comprised of 21 linear polymers, 61 solvents, and 97 unique polymer–solvent combinations (6524 cloud point temperatures) to construct phase diagrams from machine learning predictions. A generalizable feature vector is developed that includes component descriptors concatenated with state variables and an experimental data descriptor (phase direction). The impact of several types of descriptors (Morgan fingerprints, molecular descriptors, and Hansen solubility parameters) to encode polymer–solvent interactions is assessed. Hansen solubility parameters are also introduced as a means to understand the general breadth of the linear polymer–solvent space as well as the density and distribution of curated data. Two common regression algorithms (XGBoost and neural networks) establish the generality of the descriptors; provide a root mean squared error (RMSE) within 3 °C for predicted cloud points in the test set; and offer excellent agreement with upper and lower critical solubility curves, isopleths, and closed-loop phase behavior by a single model. The ability to extrapolate to polymers that are very dissimilar from the curated data is poor, but with as little as 20 cloud points or a single phase boundary, RMSE error of predictions are within 5 °C. This implies that the current model captures aspects of the underlying physics and can readily exploit correlations to reduce required data for additional polymer–solvent pairs. Finally, the model and data are accessible via the Polymer Property Predictor and Database (3PDb).

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.

Public health and safety concerns around the SARS-CoV-2 novel coronavirus (COVID-19) pandemic have greatly changed human behaviour. Such shifts in behaviours including travel patterns, consumerism, and energy use, are variously impacting biodiversity during the human-dominated geological epoch known as the Anthropocene. Indeed, the dramatic reduction in human mobility and activity has been termed the "Anthropause". COVID-19 has highlighted the current environmental and biodiversity crisis and has provided an opportunity to redefine our relationship with nature. Here we share 10 considerations for conservation policy makers to support and rethink the development of impactful and effective policies in light of the COVID-19 pandemic. There are opportunities to leverage societal changes as a result of COVID-19, focus on the need for collaboration and engagement, and address lessons learned through the development of policies (including those related to public health) during the pandemic. The pandemic has had devastating impacts on humanity that should not be understated, but it is also a warning that we need to redefine our relationship with nature and restore biodiversity. The considerations presented here will support the development of robust, evidence-based, and transformative policies for biodiversity conservation in a post-COVID-19 world.

Human-in-the-loop experimentation enables interactive machine learning for continuous flow chemistry reaction planning and optimization.