Archimedean Spirals with Controllable Chirality: Disk Substrate-Mediated Solution Assembly of Rod-Coil Block Copolymers.

Spirals are common in nature; however, they are rarely observed in polymer self-assembly systems, and the formation mechanism is not well understood. Herein, we report the formation of two-dimensional (2D) spiral patterns via microdisk substrate-mediated solution self-assembly of polypeptide-based rod-coil block copolymers. The spiral pattern consists of multiple strands assembled from the block copolymers, and two central points are observed. The spirals fit well with the Archimedean spiral model, and their chirality is dependent on the chirality of the polypeptide blocks. As revealed by a combination of experiments and theoretical simulations, these spirals are induced by an interplay of the parallel ordering tendency of the strands and circular confinement of the microdisks. This work presents the first example regarding substrate-mediated self-assembly of block copolymers into spirals. The gained information could not only enhance our understanding of natural spirals but also assist in both the controllable preparations and applications of spiral nanostructures.

Living Growth Kinetics of Polymeric Micelles on a Substrate.

Living growth of micelles on the substrate is an intriguing phenomenon; however, little is known about its growth kinetics, especially from a theoretical viewpoint. Here, we examine the living growth kinetics of polymeric micelles on a hydrophobic substrate immersed in an aqueous solution. The block copolymers first assemble into short cylinder seeds anchored on the substrate. Then, the small aggregates of block copolymers in the solutions fuse onto the active ends of the anchored seeds, leading to micelle growth on the substrate. A theoretical model is proposed to interpret such living growth kinetics. It is revealed that the growth rate coefficient on the substrate is independent of the copolymer concentration and the multistep feedings; however, it is significantly affected by the surface hydrophobicity. Brownian dynamics simulations further support the proposed growth mechanism and the kinetic model. This work enriches living assembly systems and provides guidance for fabricating bioinspired surface nanostructures.

Prediction and Interpretability of Glass Transition Temperature of Homopolymers by Data-Augmented Graph Convolutional Neural Networks.

Establishing the structure-property relationship by machine learning (ML) models is extremely valuable for accelerating the molecular design of polymers. However, existing ML models for the polymers are subject to scarcity issues of training data and fewer variations of graph structures of molecules. In addition, limited works have explored the interpretability of ML models to infer the latent knowledge in the field of polymer science that could inspire ML-assisted molecular design. In this contribution, we integrate graph convolutional neural networks (GCNs) with data augmentation strategy to predict the glass transition temperature Tg of polymers. It is demonstrated that the data-augmented GCN model outperforms the conventional models and achieves a higher accuracy for the prediction of Tg despite a small amount of training data. Furthermore, taking advantage of molecular graph representations, the data-augmented GCN model has the capability to infer the importance of atoms or substructures from the understanding of Tg, which generally agrees with the experimental findings in the field of polymer science. The inferred knowledge of the GCN model is used to advise on the design of functional polymers with specific Tg. The data-augmented GCN model possesses prominent superiorities in the establishment of structure-property relationship and also provides an efficient way for accelerating the rational design of polymer molecules.

Efficient exploration of compositional space for high-performance copolymers via Bayesian optimization.

The traditional approach employed in copolymer compositional design, which relies on trial-and-error, faces low-efficiency and high-cost obstacles when attempting to simultaneously improve multiple conflicting properties. For example, designing co-cured polycyanurates that exhibit both moisture and thermal resistance, along with high modulus, is a long-term challenge because of the intrinsic trade-offs between these properties. In this work, to surmount these barriers, we developed a Bayesian optimization (BO)-guided method to expedite the discovery of co-cured polycyanurates exhibiting low water uptake, coupled with higher glass transition temperature and Young's modulus. By virtue of the knowledge of molecular simulations, benchmarking studies were carried out to develop an effective BO-guided method. Propelled by the developed method, several copolymers with improved comprehensive properties were obtained experimentally in a few iterations. This work provides guidance for efficiently designing other high-performance copolymers.

Harnessing Data Augmentation and Normalization Preprocessing to Improve the Performance of Chemical Reaction Predictions of Data-Driven Model.

As a template-free, data-driven methodology, the molecular transformer model provides an alternative by which to predict the outcome of chemical reactions and design the route of the retrosynthetic plane in the field of organic synthesis and polymer chemistry. However, in consideration of the small datasets of chemical reactions, the data-driven model suffers from the difficulty of low accuracy in the prediction tasks of chemical reactions. In this contribution, we integrate the molecular transformer model with the strategies of data augmentation and normalization preprocessing to accomplish the three tasks of chemical reactions, including the forward predictions of chemical reactions, and single-step retrosynthetic predictions with and without the reaction classes. It is clearly demonstrated that the prediction accuracy of the molecular transformer model can be significantly raised by the use of proposed strategies for the three tasks of chemical reactions. Notably, after the introduction of the 40-level data augmentation and normalization preprocessing, the top-1 accuracy of the forward prediction increases markedly from 71.6% to 84.2% and the top-1 accuracy of the single-step retrosynthetic prediction with additional reaction class increases from 53.2% to 63.4%. Furthermore, it is found that the superior performance of the data-driven model originates from the correction of the grammatical errors of the SMILES strings, especially for the case of the reaction classes with small datasets.

Patterning of Polymer-Functionalized Nanoparticles with Varied Surface Mobilities of Polymers.

The polymers can be either dynamically tethered to or permanently grafted to the nanoparticle to produce polymer-functionalized nanoparticles. The surface mobility of polymer ligands with one end anchored to the nanoparticle can affect the surface pattern, but the effect remains unclear. Here, we addressed the influence of lateral polymer mobility on surface patterns by performing self-consistent field theory calculations on a modeled polymer-functionalized nanoparticle consisting of immobile and mobile brushes. The results show that except for the radius of nanoparticles and grafting density, the fraction of mobile brushes substantially influences the surface patterning of polymer-functionalized nanoparticles, including striped patterns and patchy patterns with various patches. The number of patches on a nanoparticle increases as the fraction of mobile brushes decreases, favored by the entropy of immobile brushes. Critically, we found that broken symmetry usually occurs in patchy nanoparticles, associated with the balance of enthalpic and entropic effects. The present work provides a fundamental understanding of the dependence of surface patterning on lateral polymer mobility. The work could also guide the preparation of diversified nanopatterns, especially for the asymmetric patchy nanoparticles, enabling the fundamental investigation of the interaction between polymer-functionalized nanoparticles.

Hierarchical 2D-1D micelles self-assembled from the heterogeneous seeded-growth of rod-coil block copolymers.

Precise control of size and dimension is the key to constructing complex hierarchical nanostructures, particularly multi-dimensional hybrid nanoassemblies. Herein, we conducted Brownian dynamics simulations to examine the seeded-growth of rod-coil block copolymer assemblies and discovered that 2D-1D (disk-cylinder) hybrid micelles could be formed via liquid-crystallization-driven self-assembly (LCDSA). 2D nanodisk micelles with smectic-like LC cores served as seeds. After adding rod-coil block copolymers into the seed solution, the copolymers incorporated onto the 2D seed edges to generate junction points. Several cylindrical arms were formed from the elongation of junction points, resulting in 2D-1D multi-dimensional hybrid micelles. The structural transition of the micelle core from smectic-like (disk) to cholesteric-like (cylindrical arms) LC packing manners benefit from the fluidity of LC. Such a seeded-growth behavior simultaneously exhibits the features of heterogeneous nucleation and homogenous epitaxy growth. Intriguingly, the arms generate in sequence, and its junction position is in the para-position first, followed by ortho-position or meta-position, resembling the difference in the substituent activities on the benzene ring. These theoretical findings are consistent with experimental results, and provide explanations to some unaddressed issues in experiments. The obtained results also reveal that the hybrid micelles are a good stabilizer due to their high surface area and distinctive suspension behaviors.

Supramolecular Polymerization of Polymeric Nanorods Mediated by Block Copolymers.

Introducing a second component is an effective way to manipulate polymerization behavior. However, this phenomenon has rarely been observed in colloidal systems, such as polymeric nanoparticles. Here, we report the supramolecular polymerization of polymeric nanorods mediated by block copolymers. Experimental observations and simulation results illustrate that block copolymers surround the polymeric nanorods and mainly concentrate around the two ends, leaving the hydrophobic side regions exposed. These polymeric nanorods connect in a side-by-side manner through hydrophobic interactions to form bundles. As polymerization progresses, the block copolymers gradually deposit onto the bundles and finally assemble into helical nanopatterns on the outermost surface, which terminates the polymerization. It is anticipated that this work could offer inspiration for a general strategy of controllable supramolecular polymerization.

Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers.

Despite advances in machine learning for accurately predicting material properties, forecasting the performance of thermosetting polymers remains a challenge due to the sparsity of historical experimental data and their complicated crosslinked structures. We proposed a machine-learning-assisted materials genome approach (MGA) for rapidly designing novel epoxy thermosets with excellent mechanical properties (high tensile moduli, high tensile strength, and high toughness) through high-throughput screening in a vast chemical space. Machine-learning models were established by combining attention- and gate-augmented graph convolutional networks, multilayer perceptrons, classical gel theory, and transfer learning from small molecules to polymers. Proof-of-concept experiments were carried out, and the structures designed by the MGA were verified. Gene substructures affecting the modulus, strength, and toughness were also extracted, revealing the mechanisms of polymers with high mechanical properties. The developed strategy can be employed to design other thermosetting polymers efficiently.

Kinetically Programming Copolymerization-like Coassembly of Multicomponent Nanoparticles with DNA.

Programmable coassembly of multicomponent nanoparticles (NPs) into heterostructures has the capability to build upon nanostructured metamaterials with enhanced complexity and diversity. However, a general understanding of how to manipulate the sequence-defined heterostructures using straightforward concepts and quantitatively predict the coassembly process remains unreached. Drawing inspiration from the synthetic concepts of molecular block copolymers is extremely beneficial to achieve controllable coassembly of NPs and access mesoscale structuring mechanisms. We herein report a general paradigm of kinetic pathway guidance for the controllable coassembly of bivalent DNA-functionalized NPs into regular block-copolymer-like heterostructures via the stepwise polymerization strategy. By quantifying the coassembly kinetics and structural statistics, it is demonstrated that the coassembly of multicomponent NPs, through directing the specific pathways of prepolymer intermediates, follows the step-growth copolymerization mechanism. Meanwhile, a quantitative model is developed to predict the growth kinetics and outcomes of heterostructures, all controlled by the designed elements of the coassembly system. Furthermore, the stepwise polymerization strategy can be generalized to build upon a great variety of regular nanopolymers with complex architectures, such as multiblock terpolymers and ladder copolymers. Our theoretical and simulation results provide fundamental insights on quantitative predictions of the coassembly kinetics and coassembled outcomes, which can aid in realizing a diverse set of supramolecular DNA materials by the rational design of kinetic pathways.

Mobile cabin hospital compulsory quarantine for mild patients can serve as an alternative treatment for COVID-19: the Chinese experience.

OBJECTIVE: To explore the application value of mobile cabin hospitals in combating COVID-19 outbreak. METHODS: The basic clinical data, the number of admission, CT scan, novel coronavirus nucleic acid testing results were collected and calculated. The operational elements of running this temporary hospital were reviewed from its construction to closing. RESULTS: Wuhan Hanyang Mobile Cabin Hospital was transformed from Hall B1 of Wuhan International Expo Center. With a total of 930 beds in this temporary hospital, 1,028 patients were admitted, among them, 598 patients were cured, and 430 patients were transferred to designated hospitals in the special period. Totally, 1,206 mobile CT scan were conducted. 2,295 novel coronavirus nucleic acid tests were performed, among which, 1,032 tests showed two continuous negative results, 924 tests with one negative, while 302 tests with positive result (13.16%). No nosocomial infection of working staff was found due to the conduction of multiple measures. The patients' livelihoods were well safeguarded in mobile cabin hospitals. CONCLUSION: The mobile cabin hospital compulsory quarantine for mild patients can serve as an alternative method to combat COVID-19.

2D Liquid-Crystallization-Driven Self-Assembly of Rod-Coil Block Copolymers: Living Growth and Self-Similarity.

Liquid-crystallization-driven self-assembly (LCDSA) is an emerging methodology, which has been employed to construct controllable 1D nanostructures. However, 2D nanostructures via living LCDSA are rarely reported, and the complicated growth kinetics are not well-known. Herein, we perform Brownian dynamics (BD) simulations to investigate the 2D living growth of disklike micelles via LCDSA of rod-coil block copolymers. The 2D seeded-growth behavior is achieved by incorporating the unimers onto the edges of disklike seeds with smectic-like liquid-crystalline (LC) cores. The fluidity of such LC-like micellar cores is conducive to the chain adjustments of rod blocks during the 2D living growth process. The apparent growth rate and unique self-similarity kinetics are governed by the interplay between the variations in the growth rate coefficient and the reactive sites at the micelle edges. This work provides an in-depth understanding of the 2D living growth of micelles and guidance to construct well-defined 2D hierarchical nanostructures.

Hierarchically Chiral Nanostructures Self-Assembled from Nanoparticle Tethered Block Copolymers.

Chiral nanostructures of nanoparticle assemblies have attracted tremendous interest for their fascinating functional properties. Herein, through theoretical simulations, it is shown that nanoparticle tethered block copolymers can self-assemble into hierarchically chiral nanostructures. Twofold helices are formed in the hierarchically chiral nanostructures: the diblock copolymers form helical supercylinders while the nanoparticles arrange into chiral assemblies wrapped around the helical supercylinders. The hierarchically chiral nanostructures can be formed in a large parameter window. Circular dichroism calculations demonstrate that the coexistence of polymeric helices and chiral nanoparticle assemblies improves the chiroptical activity. These findings can provide guidelines for designing hierarchically ordered chiral nanostructures with advanced functional properties.

Membrane Nanopores Induced by Nanotoroids via an Insertion and Pore-Forming Pathway.

The formation of membrane nanopores is one of the crucial activities of cells and has attracted considerable attention. However, the understanding of their types and mechanisms is still limited. Herein, we report a novel nanopore formation phenomenon achieved through the insertion of polymeric nanotoroids into the cellular membrane. As revealed by theoretical simulations, the nanotoroid can embed in the membrane, leaving a nanopore on the cell. The through-the-cavity wrapping of lipids is critical for the retention of the nanotoroid in the membrane, which is attributed to both a relatively large inner cavity of the nanotoroid and a moderate attraction between the nanotoroid and membrane lipids. Under the guidance of the simulation predictions, experiments using polypeptide toroids as pore-forming agents were performed, confirming the unique biophysical phenomenon. This work demonstrates a distinctive pore-forming pathway, deepens the understanding of the membrane nanopore phenomenon, and assists in the design of advanced pore-forming materials.

Anchorage-Dependent Living Supramolecular Self-Assembly of Polymeric Micelles.

Anchorage-dependent contact-inhibited growth usually refers to on-surface cell proliferation inhibited by the proximity of other cells. This phenomenon, prominent in nature, has yet to be achieved with polymeric micelles. Here, we report the control living supra-macromolecular self-assembly of elongated micelles with a liquid crystalline core onto a hydrophobic substrate via the synergetic interactions between the substrate and aggregates dispersed in solution. In this system, seed formation is a transient phenomenon induced by the adsorption and rearrangement of the core-swollen aggregates. The seeds then trigger the growth of elongated micelles onto the substrate in a living controllable manner until the contact with the substrate is disrupted. Brownian dynamic simulations show that this unique behavior is due to the fusion of the aggregates onto both ends of the anchored seeds. More important, the micelle length can be tuned by varying the substrate hydrophobicity, a key step toward the fabrication of intricate structures.

Spiral- and meridian-patterned spheres self-assembled from block copolymer/homopolymer binary systems.

Spiral nanostructures, mainly in the 2D form, have been observed in polymer self-assembly, while well-defined 3D spirals are rarely reported. Here we report that a binary system containing polypeptide-based block copolymers and homopolymers can self-assemble into well-defined spiral spheres (3D spirals), in which the homopolymers form the core and the copolymers form the spirals. Upon increasing the preparation temperature, meridian spheres were obtained. Mixing polypeptide block copolymers with opposite backbone chirality also leads to the formation of meridian spheres. In the meridian patterns, a tighter packing manner of the phenyl groups appended to the polypeptide blocks was observed, which is responsible for the spiral-to-meridian transitions. This work enriches the research of spiral assemblies and provides a facile route to switch chiral/achiral nanostructures by regulating the packing manner of the pendant groups.

A theoretical insight into the curing mechanism of phthalonitrile resins promoted by aromatic amines.

High-temperature phthalonitrile resins have a wide range of applications, and understanding their curing mechanism is of great importance for academic research and engineering applications. However, the actual curing mechanism is still elusive. We presented a density functional theory study on the curing mechanism of phthalonitrile resins promoted by aromatic amines using phthalonitrile and aniline as the model compounds. We found that the rate-determining step is the initial nucleophilic addition of amines with nitrile groups on phthalonitrile to generate an amidine intermediate. The amines play a vital role in the H-transfer promoter throughout the curing reaction. The amidine and isoindoline are the critical intermediates, which can readily react with phthalonitrile through 6-membered transition states. The intramolecular cyclization of amidine intermediates is the vital step in forming isoindoline intermediates, which can be significantly promoted by amines. The proposed curing reaction pathways are kinetically more favorable than the previously reported ones, which can account for the formation of triazine, polyisoindoline, and phthalocyanine and provide a molecular-level understanding of the curing reaction.

Effect of curing reaction types on the structures and properties of acetylene-containing thermosets: towards optimization of curing procedure.

High-temperature thermosets are usually prepared from resins containing alkynyl groups, and their properties depend much upon the curing process containing various types of curing reactions. However, how the curing process affects the properties remains unclear due to the complicated curing reactions. We used molecular dynamics simulations to investigate the effect of curing reaction types, including cyclotrimerization, Diels-Alder reaction, and radical reaction, on the structures and properties of imide oligomers terminated with alkynyl groups. The results show that the cycloadditions such as cyclotrimerization and Diels-Alder reaction endow the thermosets with rigid structures and high moduli. Compared with the cycloadditions, the radical reaction enables the formation of flexible cured structures, which can enhance the toughness of thermosets. The differences in thermal and mechanical properties caused by different curing types were elucidated by the relaxation processes of fragments in these cured systems and were explained by the variation of torsion energy in different curing forms. As this work aims to optimize the curing procedure to obtain high-performance resins with desired properties, different curing procedures were finally designed according to the theoretical studies, and the obtained cured polymers show significant differences in the properties from different curing ways. The results can guide the preparation of desired thermosetting resins by tuning the curing procedure.

Distinctive Dielectric Permittivity of Hierarchical Nanostructures with Ordered Nanoparticle Networks Self-Assembled from AB-g-NP/AC Block Copolymer Mixtures.

Directing nanoparticles into ordered organization in polymer matrix to improve macroscopic properties of nanocomposites remains a challenge. Herein, by means of theoretical simulations, we show the high permittivity of hybrid nanostructures designed with mixtures of AB block copolymer-grafted nanoparticles and lamella-forming AC diblock copolymers. The grafted nanoparticles self-assemble into parallel stripes or highly ordered networks in the lamellae of the AC diblock copolymers. The ordered nanoparticle networks, including honeycomb-like and kagomé networks, provide bending and conductive pathways for concentrating electric fields, which results in the improvement of the permittivity. We envisage that this strategy will open a gateway to prepare hierarchically ordered functional nanocomposites with distinctive dielectric properties.

Programmable Morphology Evolution of Rod-Coil-Rod Block Copolymer Assemblies Induced by Variation of Chain Ordering.

Morphology transition of block copolymer assemblies in response to external stimuli has attracted considerable attention. However, our knowledge about the mechanism of such a transition is still limited, especially for rod-coil block copolymers. Here, we report a programmable morphology evolution of assemblies induced by variation of chain ordering for rod-coil-rod triblock copolymers. A sequence of morphology transition from ellipsoids to disks, bowls, and vesicles is observed by increasing the solution temperature. At high temperatures, the mobility of the rod chain increases and the rigidity of the rod chain decreases. This gives rise to an ellipsoid-to-vesicle morphology transition. Dissipative particle dynamics theoretical simulations were performed to reveal the mechanism of this morphology transition process. It was found that the increase of rod chain mobility and the decrease of rod chain rigidity induce a decrease of chain ordering of rod blocks as temperature increases, which results in an ellipsoid-to-vesicle morphology transition. The gained information can guide the construction of nanoassemblies based on the rod-coil block copolymers.