Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly.

While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical phase diagrams whenever self-assemblies of novel monomer pairs are sought for specific applications. To alleviate this burden, we develop here the first framework for a data-driven methodology for the probabilistic modeling of PISA morphologies based on a selection and suitable adaption of statistical machine learning methods. As the complexity of PISA precludes generating large volumes of training data with in silico simulations, we focus on interpretable low variance methods that can be interrogated for conformity with chemical intuition and that promise to work well with only 592 training data points which we curated from the PISA literature. We found that among the evaluated linear models, generalized additive models, and rule and tree ensembles, all but the linear models show a decent interpolation performance with around 0.2 estimated error rate and 1 bit expected cross entropy loss (surprisal) when predicting the mixture of morphologies formed from monomer pairs already encountered in the training data. When considering extrapolation to new monomer combinations, the model performance is weaker but the best model (random forest) still achieves highly nontrivial prediction performance (0.27 error rate, 1.6 bit surprisal), which renders it a good candidate to support the creation of empirical phase diagrams for new monomers and conditions. Indeed, we find in three case studies that, when used to actively learn phase diagrams, the model is able to select a smart set of experiments that lead to satisfactory phase diagrams after observing only relatively few data points (5-16) for the targeted conditions. The data set as well as all model training and evaluation codes are publicly available through the GitHub repository of the last author.

Approaches for the inhibition and elimination of microbial biofilms using macromolecular agents.

Biofilms are complex three-dimensional structures formed at interfaces by the vast majority of bacteria and fungi. These robust communities have an important detrimental impact on a wide range of industries and other facets of our daily lives, yet their removal is challenging owing to the high tolerance of biofilms towards conventional antimicrobial agents. This key issue has driven an urgent search for new innovative antibiofilm materials. Amongst these emerging approaches are highly promising materials that employ aqueous-soluble macromolecules, including peptides, proteins, synthetic polymers, and nanomaterials thereof, which exhibit a range of functionalities that can inhibit biofilm formation or detach and destroy organisms residing within established biofilms. In this Review, we outline the progress made in inhibiting and removing biofilms using macromolecular approaches, including a spotlight on cutting-edge materials that respond to environmental stimuli for "on-demand" antibiofilm activity, as well as synergistic multi-action antibiofilm materials. We also highlight materials that imitate and harness naturally derived species to achieve new and improved biomimetic and biohybrid antibiofilm materials. Finally, we share some speculative insights into possible future directions for this exciting and highly significant field of research.

The Importance of Cooperativity in Polymer Blending: Toward Controlling the Thermoresponsive Behavior of Blended Block Copolymer Micelles.

Understanding, predicting, and controlling the self-assembly behavior of stimuli-responsive block copolymers remains a pertinent challenge. As such, the copolymer blending protocol provides an accessible methodology for obtaining a range of intermediate polymeric nanostructures simply by blending two or more block copolymers in the desired molar ratio to target specific stimuli-responsiveness. Herein, thermoresponsive diblock copolymers are blended in various combinations to investigate whether the resultant cloud point temperature can be modulated by simple manipulation of the molar ratio. Thermoresponsive amphiphilic diblock copolymers composed of statistical poly(n-butyl acrylate-co-N,N-dimethylacrylamide) core-forming blocks and four different thermoresponsive corona-forming blocks, namely poly(diethylene glycol monomethyl ether methacrylate) (p(DEGMA)), poly(N-isopropylacrylamide), poly(N,N-diethylacrylamide), and poly(oligo(ethylene glycol) monomethyl ether methacrylate) (p(OEGMA)) are selected for evaluation. Using variable temperature turbidimetry, the thermoresponsive behavior of blended diblock copolymer self-assemblies is assessed and compared to the thermoresponsive behavior of the constituent pure diblock copolymer micelles to determine whether comicellization is achieved and more significantly, whether the two blended corona-forming thermoresponsive blocks exhibit cooperative behavior. Interestingly, blended diblock copolymer micelles composed of p(DEGMA)/p(OEGMA) mixed coronae display cooperative behavior, highlighting the potential of copolymer blending for the preparation of stimuli-responsive nanomaterials in applications such as oil recovery, drug delivery, biosensing, and catalysis.

Enhanced drug delivery to cancer cells through a pH-sensitive polycarbonate platform.

Polymer-drug conjugates are widely investigated to enhance the selectivity of therapeutic drugs to cancer cells, as well as increase circulation lifetime and solubility of poorly soluble drugs. In order to direct these structures selectively to cancer cells, targeting agents are often conjugated to the nanoparticle surface as a strategy to limit drug accumulation in non-cancerous cells and therefore reduce systemic toxicity. Here, we report a simple procedure to generate biodegradable polycarbonate graft copolymer nanoparticles that allows for highly efficient conjugation and intracellular release of S-(+)-camptothecin, a topoisomerase I inhibitor widely used in cancer therapy. The drug-polymer conjugate showed strong efficacy in inhibiting cell proliferation across a range of cancer cell lines over non-cancerous phenotypes, as a consequence of the increased intracellular accumulation and subsequent drug release specifically in cancer cells. The enhanced drug delivery towards cancer cells in vitro demonstrates the potential of this platform for selective treatments without the addition of targeting ligands.

Honey-inspired antimicrobial hydrogels resist bacterial colonization through twin synergistic mechanisms.

Inspired by the interesting natural antimicrobial properties of honey, biohybrid composite materials containing a low-fouling polymer hydrogel network and an encapsulated antimicrobial peroxide-producing enzyme have been developed. These synergistically combine both passive and active mechanisms for reducing microbial bacterial colonization. The mechanical properties of these materials were assessed using compressive mechanical analysis, which revealed these hydrogels possessed tunable mechanical properties with Young's moduli ranging from 5 to 500 kPa. The long-term enzymatic activities of these materials were also assessed over a 1-month period using colorimetric assays. Finally, the passive low-fouling properties and active antimicrobial activity against a leading opportunistic pathogen, Staphylococcus epidermidis, were confirmed using bacterial cell counting and bacterial adhesion assays. This study resulted in non-adhesive substrate-permeable antimicrobial materials, which could reduce the viability of planktonic bacteria by greater than 7 logs. It is envisaged these new biohybrid materials will be important for reducing bacterial adherence in a range of industrial applications.