Filled Elastomers: Mechanistic and Physics-Driven Modeling and Applications as Smart Materials

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer-particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer-particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Unraveling the Mechanical Property Decrease of Electrospun Spider Silk: A Molecular Dynamics Simulation Study

This study investigated the impact of electric fields on Nephila clavipes spider silk using molecular dynamics modeling. Electric fields with varying amplitudes and directions were observed to disrupt the β sheet structure of spider silk and reduce its mechanical properties. However, a notable exception was observed when a 0.1 V/nm electric field was applied in the antiparallel direction, resulting in improvements in Young's modulus and ultimate tensile strength. The antiparallel direction was observed to be particularly sensitive to electric fields, causing disruptions in beta sheets and hydrogen bonds, which significantly influence the mechanical properties. This study demonstrates that spider silk maintains its structural integrity at 0.1 V/nm. Possibly, lowering the power levels of typical electrospinning machines can prevent secondary structural disruption. These findings provide valuable insights for enhancing silk fiber production and applications using natural silk proteins while shedding light on the impact of electric fields on other silk proteins. Finally, this study opens up possibilities for optimizing electrospinning processes to enhance performance in various silk electrospinning applications.

Trends in in-silico guided engineering of efficient polyethylene terephthalate (PET) hydrolyzing enzymes to enable bio-recycling and upcycling of PET

Polyethylene terephthalate (PET) is the largest produced polyester globally, and less than 30% of all the PET produced globally (∼6 billion pounds annually) is currently recycled into lower-quality products. The major drawbacks in current recycling methods (mechanical and chemical), have inspired the exploration of potentially efficient and sustainable PET depolymerization using biological approaches. Researchers have discovered efficient PET hydrolyzing enzymes in the plastisphere and have demonstrated the selective degradation of PET to original monomers thus enabling biological recycling or upcycling. However, several significant hurdles such as the less efficiency of the hydrolytic reaction, low thermostability of the enzymes, and the inability of the enzyme to depolymerize crystalline PET must be addressed in order to establish techno-economically feasible commercial-scale biological PET recycling or upcycling processes. Researchers leverage a synthetic biology-based design; build, test, and learn (DBTL) methodology to develop commercially applicable efficient PET hydrolyzing enzymes through 1) high-throughput metagenomic and proteomic approaches to discover new PET hydrolyzing enzymes with superior properties: and, 2) enzyme engineering approaches to modify and optimize PET hydrolyzing properties. Recently, in-silico platforms including molecular mechanics and machine learning concepts are emerging as innovative tools for the development of more efficient and effective PET recycling through the exploration of novel mutations in PET hydrolyzing enzymes. In-silico-guided PET hydrolyzing enzyme engineering with DBTL cycles enables the rapid development of efficient variants of enzymes over tedious conventional enzyme engineering methods such as random or directed evolution. This review highlights the potential of in-silico-guided PET degrading enzyme engineering to create more efficient variants, including Ideonella sakaiensis PETase (IsPETase) and leaf-branch compost cutinases (LCC). Furthermore, future research prospects are discussed to enable a sustainable circular economy through the bioconversion of PET to original or high-value platform chemicals.

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

Perspectives on development of biomedical polymer materials in artificial intelligence age

Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

Sequence Control of the Self-Assembly of Elastin-Like Polypeptides into Hydrogels with Bespoke Viscoelastic and Structural Properties

The biofabrication of structural proteins with controllable properties via amino acid sequence design is interesting for biomedicine and biotechnology, yet a complete framework that connects amino acid sequence to material properties is unavailable, despite great progress to establish design rules for synthesizing peptides and proteins with specific conformations (e.g., unfolded, helical, β-sheets, or β-turns) and intermolecular interactions (e.g., amphipathic peptides or hydrophobic domains). Molecular dynamics (MD) simulations can help in developing such a framework, but the lack of a standardized way of interpreting the outcome of these simulations hinders their predictive value for the design of de novo structural proteins. To address this, we developed a model that unambiguously classifies a library of de novo elastin-like polypeptides (ELPs) with varying numbers and locations of hydrophobic/hydrophilic and physical/chemical-cross-linking blocks according to their thermoresponsiveness at physiological temperature. Our approach does not require long simulation times or advanced sampling methods. Instead, we apply (un)supervised data analysis methods to a data set of molecular properties from relatively short MD simulations (150 ns). We also experimentally investigate hydrogels of those ELPs from the library predicted to be thermoresponsive, revealing several handles to tune their mechanical and structural properties: chain hydrophilicity/hydrophobicity or block distribution control the viscoelasticity and thermoresponsiveness, whereas ELP concentration defines the network permeability. Our findings provide an avenue to accelerate the design of de novo ELPs with bespoke phase behavior and material properties.

Probing the alignment-dependent mechanical behaviors and time-evolutional aligning process of collagen scaffolds

Efficiently manipulating and reproducing collagen (COL) alignment in vitro remains challenging because many of the fundamental mechanisms underlying and guiding the alignment process are not known. We reconcile experiments and coarse-grained molecular dynamics simulations to investigate the mechanical behaviors of a growing COL scaffold and assay how changes in fiber alignment and various cross-linking densities impact their alignment dynamics under shear flow. We find higher cross-link densities and alignment levels significantly enhance the apparent tensile/shear moduli and strength of a bulk COL system, suggesting potential measures to facilitate the design of stronger COL based materials. Since fibril alignment plays a key factor in scaffold mechanics, we next investigate the molecular mechanism behind fibril alignment with Couette flow by computationally investigating the effects of COL's structural properties such as chain lengths, number of chains, tethering conditions, and initial COL conformations on the COL's final alignment level. Our computations suggest that longer chain lengths, more chains, greater amounts of tethering, and initial anisotropic COL conformations benefit the final alignment, but the effect of chain lengths may be more dominant over other factors. These results provide important parameters for consideration in manufacturing COL-based scaffolds where alignment and cross-linking are necessary for regulating performance.

Rapid prediction of protein natural frequencies using graph neural networks

Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence.

Engineering Natural and Recombinant Silks for Sustainable Biodevices

Silk fibroin (SF) is a structural protein derived from natural silkworm silks. Materials fabricated based on SF usually inherit extraordinary physical and biological properties, including high mechanical strength, toughness, optical transparency, tailorable biodegradability, and biocompatibility. Therefore, SF has attracted interest in the development of sustainable biodevices, especially for emergent bio-electronic technologies. To expand the function of current silk devices, the SF characteristic sequence has been used to synthesize recombinant silk proteins that benefit from SF and other functional peptides, such as stimuli-responsive elastin peptides. In addition to genetic engineering methods, innovated chemistry modification approaches and improved material processing techniques have also been developed for fabricating advanced silk materials with tailored chemical features and nanostructures. Herein, this review summarizes various methods to synthesize functional silk-based materials from different perspectives. This review also highlights the recent advances in the applications of natural and recombinant silks in tissue regeneration, soft robotics, and biosensors, using B. mori SF and silk-elastin-like proteins (SELPs) as examples.

Polymer Structure Predictor (PSP): A Python Toolkit for Predicting Atomic-Level Structural Models for a Range of Polymer Geometries

Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream ab initio and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.

The Impact of Foaming Effect on the Physical and Mechanical Properties of Foam Glasses with Molecular-Level Insights

Foaming effect strongly impacts the physical and mechanical properties of foam glass materials, but an understanding of its mechanism especially at the molecular level is still limited. In this study, the foaming effects of dextrin, a mixture of dextrin and carbon, and different carbon allotropes are investigated with respect to surface morphology as well as physical and mechanical properties, in which 1 wt.% carbon black is identified as an optimal choice for a well-balanced material property. More importantly, the different foaming effects are elucidated by all-atomistic molecular dynamics simulations with molecular-level insights into the structure–property relationships. The results show that smaller pores and more uniform pore structure benefit the mechanical properties of the foam glass samples. The foam glass samples show excellent chemical and thermal stability with 1 wt.% carbon as the foaming agent. Furthermore, the foaming effects of CaSO₄ and Na₂HPO₄ are investigated, which both create more uniform pore structures. This work may inspire more systematic approaches to control foaming effect for customized engineering needs by establishing molecular-level structure–property–process relationships, thereby, leading to efficient production of foam glass materials with desired foaming effects.

Thermo- and Ion-responsive Silk-elastin-like Proteins and Their Multiscale Mechanisms

Silk-elastin-like protein (SELP) is an excellent biocompatible and biodegradable material for hydrogels with tunable properties that can respond to multiple external stimuli. By integrating fully atomistic, replica exchange molecular dynamics...

Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis

Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.

Structure-Property Relationships of Elastin-like Polypeptides: A Review of Experimental and Computational Studies

Elastin is a structural protein with outstanding mechanical properties (e.g., elasticity and resilience) and biologically relevant functions (e.g., triggering responses like cell adhesion or chemotaxis). It is formed from its precursor tropoelastin, a 60-72 kDa water-soluble and temperature-responsive protein that coacervates at physiological temperature, undergoing a phenomenon termed lower critical solution temperature (LCST). Inspired by this behavior, many scientists and engineers are developing recombinantly produced elastin-inspired biopolymers, usually termed elastin-like polypeptides (ELPs). These ELPs are generally comprised of repetitive motifs with the sequence VPGXG, which corresponds to repeats of a small part of the tropoelastin sequence, X being any amino acid except proline. ELPs display LCST and mechanical properties similar to tropoelastin, which renders them promising candidates for the development of elastic and stimuli-responsive protein-based materials. Unveiling the structure-property relationships of ELPs can aid in the development of these materials by establishing the connections between the ELP amino acid sequence and the macroscopic properties of the materials. Here we present a review of the structure-property relationships of ELPs and ELP-based materials, with a focus on LCST and mechanical properties and how experimental and computational studies have aided in their understanding.

High-Throughput Routes to Biomaterials Discovery

Many existing clinical treatments are limited in their ability to completely restore decreased or lost tissue and organ function, an unenviable situation only further exacerbated by a globally aging population. As a result, the demand for new medical interventions has increased substantially over the past 20 years, with the burgeoning fields of gene therapy, tissue engineering, and regenerative medicine showing promise to offer solutions for full repair or replacement of damaged or aging tissues. Success in these fields, however, inherently relies on biomaterials that are engendered with the ability to provide the necessary biological cues mimicking native extracellular matrixes that support cell fate. Accelerating the development of such "directive" biomaterials requires a shift in current design practices toward those that enable rapid synthesis and characterization of polymeric materials and the coupling of these processes with techniques that enable similarly rapid quantification and optimization of the interactions between these new material systems and target cells and tissues. This manuscript reviews recent advances in combinatorial and high-throughput (HT) technologies applied to polymeric biomaterial synthesis, fabrication, and chemical, physical, and biological screening with targeted end-point applications in the fields of gene therapy, tissue engineering, and regenerative medicine. Limitations of, and future opportunities for, the further application of these research tools and methodologies are also discussed.

Multiresponsive 2D Ti3C2Tx MXene via Implanting Molecular Properties

The design and fabrication of active nanomaterials exhibiting multifunctional properties is a must in the so-called global "Fourth Industrial Revolution". In this sense, molecular engineering is a powerful tool to implant original capabilities on a macroscopic scale. Herein, different bioinspired 2D-MXenes have been developed via a versatile and straightforward synthetic approach. As a proof of concept, Ti₃C₂T_x MXene has been exploited as a highly sensitive transducing platform for the covalent assembly of active biomolecular architectures (i.e., amino acids). All pivotal properties originated from the anchored targets were proved to be successfully transferred to the resulting bioinspired 2D-MXenes. Appealing applications have been devised for these 2D-MXene prototypes showing (i) chiroptical activity, (ii) fluorescence capabilities, (iii) supramolecular π-π interactions, and (iv) stimuli-responsive molecular switchability. Overall, this work demonstrates the fabrication of programmable 2D-MXenes, taking advantage of the inherent characteristics of the implanted (bio)molecular components. Thus, the current bottleneck in the field of 2D-MXenes can be overcome after the significant findings reported here.

Equilibrium swelling of multi-stimuli-responsive copolymer gels

Copolymer gels prepared by polymerization of thermo-responsive and anionic monomers demonstrate strong sensitivity to several triggers such as temperature, pH and ionic strength of aqueous solutions. For biomedical applications of these materials (as on-off switches in controlled drug delivery and release), fine tuning of their volume phase transition temperature (VPTT) and a sharp decay in degree of swelling upon transition from the swollen to the collapsed state are needed. These requirements are fulfilled under swelling of copolymer gels and microgels in water under acidic conditions, but are violated when tests are conducted under alkaline conditions or in aqueous solutions of salts with physiological salinity. A model is developed for equilibrium swelling of multi-stimuli-responsive copolymer gels in aqueous solutions with arbitrary pH and molar fractions of a monovalent salt. Unlike conventional approaches, the model accounts for secondary interactions between chains (hydrogen bonding) to describe the kinetics of aggregation of hydrophobic segments above VPTT. Material constants are determined by fitting experimental swelling diagrams on poly(N-isopropylacrylamide-co-sodium acrylate) gels with various molar fractions of ionic monomers. The effects of temperature, pH and molar fraction of salt on the equilibrium degree of swelling below and above VPTT are studied numerically.

Current Insights on the Diverse Structures and Functions in Bacterial Collagen-like Proteins

The dearth of knowledge on the diverse structures and functions in bacterial collagen-like proteins is in stark contrast to the deep grasp of structures and functions in mammalian collagen, the ubiquitous triple-helical scleroprotein that plays a central role in tissue architecture, extracellular matrix organization, and signal transduction. To fill and highlight existing gaps due to the general paucity of data on bacterial CLPs, we comprehensively reviewed the latest insight into their functional and structural diversity from multiple perspectives of biology, computational simulations, and materials engineering. The origins and discovery of bacterial CLPs were explored. Their genetic distribution and molecular architecture were analyzed, and their structural and functional diversity in various bacterial genera was examined. The principal roles of computational techniques in understanding bacterial CLPs' structural stability, mechanical properties, and biological functions were also considered. This review serves to drive further interest and development of bacterial CLPs, not only for addressing fundamental biological problems in collagen but also for engineering novel biomaterials. Hence, both biology and materials communities will greatly benefit from intensified research into the diverse structures and functions in bacterial collagen-like proteins.

Artificial intelligence and machine learning in design of mechanical materials

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.