Perspectives on Theoretical Models and Molecular Simulations of Polymer Brushes

Underwater Adhesion of Oil Droplets to Surfaces Grafted with Hydrophilic Polymer Brushes: Many-Body Dissipative-Particle Simulations

A droplet probe atomic force microscopy (AFM) simulation using many-body dissipative particle dynamics is employed to investigate the effects of polymer hydrophilicity and grafting density on the underwater adhesion behavior of oil droplets at hydrophilic polymer brush layers. The results show that increasing the grafting density suppresses oil penetration, leading to reduced adhesion. Moreover, a higher polymer hydrophilicity weakens the sensitivity of adhesion behavior to grafting density, causing adhesion forces to converge under strong polymer-water interactions. Further analysis reveals that interfacial properties, including contact area, penetration depth, and contribution of polymers at the interface, significantly influence oil droplet adhesion. Additionally, polymer hydrophilicity plays a key role in regulating the relative contribution of polymers at the contact interface, ultimately determining the adhesion and its sensitivity to interfacial properties.

Multiscale Modeling and Simulation of Zwitterionic Anti-fouling Materials

Zwitterionic materials with cationic and anionic moieties in the same chain, being electrically neutral, have excellent hydrophilicity, stability, biocompatibility, and outstanding anti-biofouling performance. Because of their unique properties, zwitterionic materials are widely applied to membrane separation, drug delivery, surface coating, etc. However, what is the root of their unique properties? It is necessary to study the structure-property relationships of zwitterionic compounds to guide the design and development of zwitterionic materials. Modeling and simulation methods are considered to be efficient technologies for understanding advanced materials in principle. This Review systematically summarizes the computational exploration of zwitterionic materials in recent years. First, the classes of zwitterionic materials are summarized. Second, the different scale simulation methods are introduced briefly. To reveal the structure-property relationships of zwitterionic materials, multiscale modeling and simulation studies at different spatial and temporal scales are summarized. The study results indicated that the strong electrostatic interaction between zwitterions with water molecules promotes formation of a stable hydration layer, namely, superhydrophilicity, leading to the excellent anti-fouling properties. Finally, we offer our viewpoint on the development and application of simulation techniques on zwitterionic materials exploration in the future. This work establishes a bridge from atomic and molecular scales to mesoscopic and macroscopic scales and helps to provide an in-depth understanding of the structure-property relationships of zwitterionic materials.

Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties

Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure-property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for various applications, such as protein immobilization supports, biosensors, and antibiofouling surfaces. However, protein adsorption is a complex phenomenon that is difficult to predict quantitatively owing to the involvement of multiple factors. Therefore, this study aims to establish a machine learning model for protein adsorption on densely packed polymer brushes with various chemical structures, as these surfaces are well-suited for analyzing structure-property correlations between the polymer's chemical structure and adsorption amount during initial protein adsorption. Two proteins, bovine serum albumin (BSA) and lysozyme, are adopted as target proteins, with the expectation that differences in their charge profiles will be reflected in the resulting machine learning model. The descriptors of the polymer brush surfaces include their grafted structures (thickness) and chemical properties, which are described by the contact angle and ζ potential. This allows physicochemical knowledge to be incorporated into the machine learning model. Random forest exhibits the best performance in all situations, accurately predicting the amounts of adsorbed BSA and lysozyme. In addition, the prediction of the contact angle and ζ potential by machine learning also enables a quantitative and explainable prediction of protein adsorption based on theoretical molecular descriptors, ensuring that no characteristics are overlooked. Moreover, the model is used to analyze the contributions of electrostatic and hydrophobic interactions to protein adsorption. In conclusion, a machine learning model is developed to predict protein adsorption on polymer brush surfaces, incorporating descriptors such as the grafted structure, contact angle, and ζ potential. It provides quantitative predictions and analyzes the roles of electrostatic and hydrophobic interactions, advancing the design of functional polymer surfaces for applications in biosensors and antifouling technologies.

All-Atom Molecular Dynamics Simulations of Grafted Poly(N,N-dimethylaminoethyl methacrylate) Brushes

Modeling polymer brushes is essential for understanding their complex behavior at surfaces and interfaces, enabling the design of materials with tunable properties. We present a computational protocol to model polymer brushes composed of grafted, brush-like chains of the charged polymer poly(N,N-dimethylaminoethyl methacrylate) (p(DMAEMA)) using an all-atom representation that captures detailed molecular interactions and structural properties. The approach is flexible and non-grid-based and allows for randomized strand configurations and the incorporation of periodic boundary conditions, enabling the construction of asymmetric polymer brush setups. An atactic p(DMAEMA) configuration is demonstrated as an example, though the protocol can be readily adapted to construct other brush-like polymer systems with varying tacticities or compositions, depending on the pH environment. Furthermore, this can be extended to stimuli-responsive materials, which generate conformation or charge upon changes in pH value or other external triggers. Molecular dynamics simulations are then employed to gain insights into the conformational behavior of the grafted p(DMAEMA) brushes and their surrounding aqueous environment, as well as their response to temperature, protonation, and variations in grafting densities, in terms of the solvent-accessible surface area, radius of gyration, and radial distribution functions. This versatile protocol provides a robust tool for simulating and analyzing the properties of diverse polyelectrolyte polymer brush systems and also as composite materials.

Molecular Effects of Zwitterionic Peptide on Monolayer Lipid Membranes upon Enzyme-Catalyzed Degradation

Secretory phospholipase A2 (sPLA2), an enzyme overexpressed in numerous diseases, has been used to trigger structural transformations in lipid-based drug delivery systems, enabling payload release at target sites. Zwitterionic peptides are known for their superior antifouling properties, often outperforming poly(ethylene glycol) (PEG) surface modification by resisting protein adsorption. In this study, we examined lipid monolayers at the water-vapor interface on a Langmuir trough, incorporating varying molar fractions of zwitterionic peptide-conjugated lipids or PEG-conjugated lipids. Synchrotron X-ray surface techniques, including X-ray reflectivity and grazing incidence X-ray diffraction, were employed to analyze molecular packing, enzyme adsorption, enzyme-catalyzed lipid degradation, and metabolite reorganization at the interface, and microscopy was used to observe domain morphologies. The results demonstrate that zwitterionic peptides exhibit a significantly greater efficiency than PEG in stabilizing the interfacial monolayer packing structure against enzyme-catalyzed lipid degradation. However, contrary to most studies reporting strong resistance of zwitterionic materials to nonspecific protein absorption, enzyme absorption to the interface, which is interfacial and phospholipids specific, was not impeded by the presence of zwitterionic peptides at low molar ratios (≤10 mol %).

Antifouling Hydrogel Based on Zwitterionic Poly(carboxybetaine diacrylate) Cross-Linkers

Antifouling zwitterionic materials have extensive applications in the biomedical field. This study designed and successfully synthesized a novel poly(carboxybetaine) diacrylate (PCBDA) via cationic ring-opening polymerization of 2-methyl-2-oxazine, chain modification by the Michael reaction, and chain end transformation to acrylate. The cross-linker was obtained with a tunable molecular weight. Through photopolymerization, poly(carboxybetaine) (PCB) hydrogels with varying solid contents were obtained, and the effects of the solid content on the hydration properties, mechanical properties, and microstructure of the PCB hydrogels were investigated. Furthermore, the non-fouling properties of the PCB hydrogels were compared to those of commercial polyethylene glycol (PEG) hydrogels. Protein adsorption on PCB hydrogels was reduced by more than 60% compared to low-fouling PEG hydrogels. PCB hydrogels exhibit antibacterial adhesion properties similar to those of PEG hydrogels. In cell adhesion experiments, no cell adhesion was observed on the PCB hydrogels, indicating their superior anti-cell adhesion function. This advancement offers a more promising alternative to polyethylene glycol diacrylate (PEGDA) cross-linkers in the design of hydrogels.

Amphiphilic Marine Antifouling Coatings Based on Zwitterion-Modified Silicone Polymers

Silicone coatings are widely employed in marine antifouling applications due to their low surface energy. However, in static marine environments, pure silicone coatings are ineffective in preventing the adhesion of marine biofilms, which consist of proteins, marine bacteria, and extracellular matrices, ultimately promoting the attachment of macrofouling organisms. To address the limitations in antifouling performance under static conditions, this study introduces a silicone-based antifouling coating modified with zwitterionic polymers. Sulfobetaine (SB) zwitterionic segments were grafted onto the side chains of poly(dimethylsiloxane) (PDMS) to synthesize the amphiphilic polymer P(DMS-SB), which was incorporated into the PDMS network to create an interpenetrating network-structured silicone coating. The zwitterionic segments effectively inhibited the adhesion of proteins, bacteria, and algae through hydration effects. Compared to pure PDMS coatings, the adhesion of proteins, bacteria, and algae was reduced by 88%, 98.9%, and 99.3%, respectively. Additionally, the coating demonstrated excellent fouling-release properties, achieving a 91.3% removal rate for settled algae under water flow conditions and reducing the simulated barnacle adhesion strength by 68.4%. This coating presents a promising antifouling solution for ships, offshore structures, and aquaculture facilities in static marine environments with significant potential for widespread application.

Understanding the Termination Effect of Ti3C2TX MXene Membrane on Water Structure and Interaction with Alginate Foulants: A Molecular Dynamics Study

The effects of termination functional groups of the Ti3C2Tx MXene membrane on the structural and dynamics properties of nearby water molecules and foulants are investigated through molecular dynamics simulations. The simulation results show that a much denser water layer can be formed at the vicinity of hydroxyl (OH) termination than that near fluorine (F) or oxygen (O) termination. Particular focus is given to the molecular binding properties of β-d-mannuronic acid (M) and α-l-guluronic acid (G) alginate monomers on the MXene membrane surface with different termination groups. Further steered molecular dynamics (SMD) simulations show that M alginate monomers exhibit significant binding with the MXene membrane surface with O termination, due to the strong electrostatic interaction and the van der Waals attraction. In contrast, the binding between the alginate monomers and the MXene membrane surface with OH termination is negligible, as the stable hydration water network prevents them from direct contact. In addition, SMD simulation results show that calcium (Ca2+) ions could significantly enhance the surface fouling between M alginate monomers and the MXene with an O termination through the formation of contact ionic pair (CIP) and solvent-shared ionic pair (SSIP) structures.

Atomistic Simulations of Hydration and Antibiofouling Behavior of Amphiphilic Polymer Brush Surfaces Functionalized with TMAO and Short Fluorocarbon

Developing fouling-resistant materials is of paramount interest in marine industries and biomedical applications. In this work, we studied the interfacial hydration and surface-protein interactions of the amphiphilic brush surface functionalized with hybrid hydrophilic trimethylamine N-oxide (TMAO) and hydrophobic pentafluoroethyl groups using a combination of atomistic molecular dynamics simulations and free-energy computations. Our results show that while the interfacial hydration density of the amphiphilic surface slightly decreases with the introduction of small fluorocarbons compared to that of the pure TMAO-functionalized surface, the amphiphilic surface remains relatively strong in resisting protein adsorption. The nanosized clustering of hydrophobic fluorine atoms on the top of the amphiphilic brush surface introduces weak protein adsorption; however, due to the strong interfacial hydration and weak hydrophobic interaction, the amphiphilic surface exhibits sufficient antibiofouling activities. Our fundamental studies will be critical for the discovery of marine fouling-resistant coating surfaces.

Nanoengineering Spikey Surfaces: Investigation of Reversible Organizational Control of Surface-Tethered Polypeptide Brushes

Nature serves as an important source of inspiration for the innovation and development of micro- and nanostructures for advanced functional surfaces and substrates. One example used in nature is a spikey surface ranging from micrometer-sized spikes on pollen grains down to the nanometer-scale protein spikes found on viruses. This study explored the realization of such highly textured surfaces via the nanoengineering of self-assembled poly(γ-benzyl-l-glutamate) "nanospikes", exploiting solvent-induced chain organization, controlled surface chemical functionality, and enhanced stability in the form of polymer brushes. The reversible solvent-responsive behavior of these polymer chains and the aggregation behavior of the chain-ends were investigated via fluorescence characterization and studied through molecular simulations. Vapor-based solvent treatments were developed for orientation control with in situ analysis to understand film response and brush organizational behavior under different selected conditions. The effect of sub-100 nm nanopatterning on surface morphology and chain organization was examined via an integrated approach of experimental and computational studies. The methodologies established in this study present opportunities for engineering sophisticated nanoscale spikey surfaces with high customizability by means of nanolithography combined with solvent-assisted treatments.

Machine Learning Aided Design and Optimization of Antifouling Surfaces

Antifouling surfaces, renowned for their strong surface resistance to proteins, cells, or tissues in various biological and environmental conditions, have broad applications in implanted devices, antibacterial coatings, biosensors, responsive materials, water treatment, and lab-on-a-chip. While extensive experimental research exists on antifouling surfaces, machine learning studies on this topic are relatively few. This perspective specifically focuses on exploring the complex relationships between the composition, structure, and properties of antifouling surfaces, examining how these factors correlate with surface hydration and protein adsorption. Different machine learning models have been developed to analyze and predict single and multiple protein adsorptions on various types of surfaces, ranging from structureless surfaces to well-ordered and rigid self-assembled monolayers, dynamically ordered polymer brushes, and complex filtration membranes. These models not only identify key descriptors or functional groups critical for antifouling performance (surface hydration, protein adsorption) but also predict the antifouling properties for a specific surface. Recognizing current challenges, this perspective delineates future research directions in the antifouling field. By leveraging and comparing current machine learning approaches, it aims to advance both the design and fundamental understanding of antifouling surfaces, thereby pushing the boundaries of innovation in this critical field.

The Kinetics of Polymer Brush Growth in the Frame of the Reaction Diffusion Front Formalism

We studied the properties of a reaction front that forms in irreversible reaction-diffusion systems with concentration-dependent diffusivities during the synthesis of polymer brushes. A coarse-grained model of the polymerization process during the formation of polymer brushes was designed and investigated for this purpose. In this model, a certain amount of initiator was placed on an impenetrable surface, and the "grafted from" procedure of polymerization was carried out. The system consisted of monomer molecules and growing chains. The obtained brush consisted of linear chains embedded in nodes of a face-centered cubic lattice with excluded volume interactions only. The simulations were carried out for high rafting densities of 0.1, 0.3, and 0.6 and for reaction probabilities of 0.02, 0.002, and 0.0002. Simulations were performed by means of the Monte Carlo method while employing the Dynamic Lattice Liquid model. Some universal behavior was found, i.e., irrespective of reaction rate and grafting density, the width of the reaction front as well as the height of the front show for long times the same scaling with respect to time. During the formation of the polymer layer despite the observed difference in dispersion of chain lengths for different grafting densities and reaction rates at a given layer height, the quality of the polymer layer does not seem to depend on these parameters.

Molecular Dynamics Study on Adsorption and Desorption of the Model Oligosaccharide above Polymer Antifouling Membranes

Polysaccharide foulants play a key role in the adhesion of many fouling organisms, which may cause severe marine biofouling. However, the detailed interaction mechanism between polysaccharides and antifouling membranes is still indistinct compared with that between the fouling protein and antifouling surfaces. In this paper, a model oligosaccharide built based on the monosaccharide composition found in diatom extracellular polymeric substances (EPS) was used as a model foulant to investigate its adsorption and desorption above three T4 antifouling membranes. It was found that the anionic poly(3-(methacryloyloxy)propane-1-sulfonate) (T4-SP) antifouling membrane had excellent antifouling ability with respect to the model oligosaccharide, while the oligosaccharide can be easily adsorbed on the poly(2-(dimethylamino)ethyl methacrylate) (T4-DM) membrane with vdW attraction and on the zwitterionic poly(sulfobetaine methacrylate) (T4-SB) membrane with electrostatic attraction. As little is known about the details of polysaccharides' adsorption above antifouling membranes at the molecular level, we hope this work will serve as a theoretical basis for finding more effective materials to prevent or control marine biofouling.

Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning

Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure-property relationship for the hydrophilicity/hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted.

Molecular behavior of silicone adhesive at buried polymer interface studied by molecular dynamics simulation and sum frequency generation vibrational spectroscopy

Silicones have excellent material properties and are used extensively in many applications, ranging from adhesives and lubricants to electrical insulation. To ensure strong adhesion of silicone adhesives to a wide variety of substrates, silane-based adhesion promotors are typically blended into the silicone adhesive formulation. However, little is known at the molecular level about the true silane adhesion promotion mechanism, which limits the ability to develop even more effective adhesion promoters. To understand the adhesion promotion mechanism of silane molecules at the molecular level, this study has used sum frequency generation vibrational spectroscopy (SFG) to determine the behavior of (3-glycidoxypropyl)trimethoxy silane (γ-GPS) at the buried interface between poly(ethylene terephthalate) (PET) and a bulk silicone adhesive. To complement and extend the SFG results, atomistic molecular dynamics (MD) simulations were applied to investigate molecular behavior and interfacial interaction of γ-GPS at the silicone/PET interface. Free energy computations were used to study the γ-GPS interaction in the sample system and determine the γ-GPS interfacial segregation mechanism. Both experiments and simulations consistently show that γ-GPS molecules prefer to segregate at the interface between PET and PDMS. The methoxy groups on γ-GPS molecules orient toward the PDMS polymer phase. The consistent picture of interfacial structure emerging from both simulation and experiment provides enhanced insight on how γ-GPS behaves in the silicone - PET system and illustrates why γ-GPS could improve the adhesion of silicone adhesive, leading to further understanding of silicone adhesion mechanisms useful in the design of silicone adhesives with improved performance.

Molecular Modeling of the Adsorption of an Egg Yolk Protein on a Water-Oil Interface

Egg yolk contains several molecular species with emulsifying properties, such as proteins and phospholipids. In particular, these molecules have both polar and nonpolar parts and thus can act as surfactants. One of the most surface-active proteins from egg yolk low-density lipoproteins is the so-called Apovitellenin-1. Experimental studies have been hindered by difficulties in isolating individual species from egg yolk lipoproteins. The purpose of this work was to assess the emulsifying properties of Apovitellenin-1 and any potential cooperative or competitive behavior in the presence of phospholipids. To do so, molecular simulations were carried out in a liquid-liquid interfacial system consisting of water and soybean oil, with varying concentrations of phospholipids and for different spatial configurations. To evaluate the conformational stability of the protein at the water-oil interface, the Gibbs free energy was computed from Metadynamics simulations as a function of the distance from the interface and of the radius of gyration. Moreover, a detailed analysis was also performed to determine which peptide residues were responsible for the protein adsorption at the oil-water interface as well as the lowering of the interfacial tension. Lastly, we combined the simulation results with a thermodynamic model to predict the interfacial tension behavior at increasing protein bulk concentration, which cannot be measured experimentally.