ABSTRACT Peptide‐polymer amphiphiles (PPAs) form diverse nanostructures with tunable properties, yet predicting and tuning these morphologies remains challenging due to complex molecular interactions. We systematically investigated the self‐assembly of PPAs with a random coil peptide (XTEN2) base and varying side chains of an oligo (alkyl acrylate) tail (ethyl, n ‐butyl, tert ‐butyl, hexyl, and cyclohexyl) using large‐scale all‐atom molecular dynamics (AMD) simulations validated by experimental observations. Our results revealed that the formation of various micellar morphologies, such as worm‐like, perforated, spherical, and multi‐core assemblies, is governed by the balance between tail‐to‐tail and tail‐to‐water interaction energies, in addition to core hydration levels. Spherical and multi‐core morphologies form when tail‐to‐tail interactions dominate, whereas worm‐like and perforated structures form as tail‐to‐water interactions increase. Additionally, peptide secondary structure dictated by sequence plays an important role in driving assembly, with β‐sheet‐rich conformations promoting more compact cores. These findings elucidate how subtle tail architecture variations direct PPA self‐assembly, providing molecular‐level insights that align with experimental particle sizes. This work advances the rational design of PPA‐based nanomaterials by linking molecular interactions to morphology control.
ABSTRACT Polyethylene (PE), a versatile polymer, exhibits varying degrees of miscibility depending on molecular weight distribution, relative component concentrations (split ratio), and short‐chain branching (SCB) content. This study investigates the thermodynamic factors governing miscibility in bimodal PE blends composed of low‐molecular‐weight polyethylene (LMwPE) and high‐molecular‐weight polyethylene (HMwPE), with emphasis on the effects of SCB and split ratio. Molecular dynamics (MD)‐based solubility parameters, mixing energy (), and the Flory‐Huggins interaction parameter ()were used to determine miscibility thresholds in PE systems. Molecular weight influences chain interaction energies, free volume, and cohesive energy density, thereby affecting blend compatibility. In the absence of SCB, equal‐weight blends exhibit higher miscibility than disproportionate blends, demonstrating the importance of the split ratio. SCB content further affects miscibility depending on its concentration in the blend components and the split ratio. In disproportionate blends where SCB‐containing components are minor, increased SCB enhances miscibility, whereas in equal‐weight blends, SCB reduces compatibility. SCB disrupts chain packing and promotes chain interpenetration by increasing free volume, improving compatibility at specific blend ratios. Overall, SCB influences packing efficiency, interaction energies, and phase‐separation behavior, providing molecular‐level insights for designing bimodal high‐density polyethylene systems with controlled phase behavior.
ABSTRACT Polyhydroxyalkanoates (PHA) are biodegradable alternatives to conventional plastics, as they can decompose without generating harmful residues, thereby addressing global plastic pollution. In this work, a molecular‐level understanding of solvent‐polymer interactions is achieved using Density Functional Theory (DFT). The molecules were optimized at the M06‐2X/cc‐pVDZ level, and the binding energies (−8.45 to −18.89 kcal/mol) confirm the thermodynamic stability of the complexes and are consistent with experimental solubility patterns. Non‐Covalent Interaction (NCI) and Reduced Density Gradient (RDG) analyses demonstrate that the van der Waals interactions dominate with minor contributions from hydrogen bonding. Natural Bond Orbital (NBO) analysis highlights the donor‐acceptor interactions with stabilization energies up to ∼17 kcal/mol, while Quantum Theory of Atoms in Molecules (QTAIM) suggests their predominantly closed‐shell interactions. Frontier Molecular Orbital (FMO) and Molecular Electrostatic Potential (MESP) analyses further explain the charge transfer and key interaction sites. Thus, this study provides a molecular‐level understanding of solvent‐PHA interactions for the rational design of sustainable PHA‐based materials.
ABSTRACT Modeling and optimization of polymerization processes incorporating microstructural quality indices are crucial for determining optimal design and operational strategies in polymer production. However, existing polymerization models that account for these indices are often characterized by large‐scale, nonlinear, and coupled equations, presenting significant computational challenges. Moreover, limited research has been conducted on developing algorithms for accurately predicting polymer microscopic properties, and only a few software tools are capable of simulating and optimizing such complex processes. To address these challenges, this study introduces “ PolymInsight ,” an innovative software tool developed in an open‐source Python environment, designed specifically for modeling polymerization processes with microstructural quality indices. PolymInsight supports both dynamic modeling of batch reactors and steady‐state modeling of continuous stirred tank reactors across a variety of polymerization reactions. The software's logical architecture includes a graphical user interface, a robust data structure, a general modeling framework, model reduction techniques, and an adaptive solution strategy. Case studies demonstrate the effectiveness, accuracy, and generalizability of PolymInsight , highlighting its potential as a powerful tool for research and industrial applications in polymer science.
Curing of thermoset resins is accompanied by a strong exothermic reaction. For large industrial parts, this heat release can significantly affect the temperature distribution within the material, potentially causing local hot spots, inhomogeneous curing, or even thermal degradation. Therefore, it is essential to understand and accurately describe the resin's thermal parameters, in particular its thermal conductivity. This thermophysical property evolves strongly during the reaction progress. In this work, we investigate the evolution of thermal conductivity during curing of an amine-based epoxy resin using complementary measurement techniques (transient hot-wire and light flash analysis). In addition, we investigate the temperature dependence of the thermal conductivity of the fully cured resin employing both light flash analysis and a guarded heat-flow meter.
In this paper, a temperature-dependent storage modulus model for quartz-dispersed polyethylene glycol (PEG) composites is presented. The model is developed by reinterpreting the empirical filler parameter in the Hilmi model as the reinforcement factor of the interphase-modified Halpin-Tsai model, explicitly linking measurable microstructural quantities - filler particle radius R, interphase thickness Ri, and interphase modulus Ei - to the predicted composite storage modulus. Storage modulus data were obtained from dynamic mechanical analysis (DMA) tests in three-point bending mode over a temperature range encompassing the glass transition region of PEG, with varying filler compositions, i.e., 0, 5, 10, and 20 wt.%. The predictions were compared with experimental data to evaluate the ability of the proposed model to capture the combined effects of temperature and filler content. Several model parameters were determined from experimental measurements and values reported in the literature. The results demonstrate good agreement between the model predictions and the temperature- and filler-dependent trends of the storage modulus for low filler compositions. By considering the role of the interphase in the micromechanics formulation, this model provides a more representative predictive framework for analyzing the mechanical behavior of quartz-dispersed PEG composites across a range of temperature and composition conditions.
The computer simulation techniques to generate various network architectures that account for complex chemical kinetics are currently undergoing rapid development. Mesh size is an important property for the application of network polymers. However, it is not straightforward to determine the mesh size based on the network structure data obtained by simulation. The mesh size represents the characteristic size of the repeating circular structural units observed throughout the network polymer. Based on the cycle size distribution in the network structures generated by Monte Carlo simulation, a mesh size index (MSI) is proposed. The MSI of the perfect network, which is a random network consisting of primary chains with infinite length, shows a linear relationship with the average chain length between crosslinks, P c,av. The MSI is a useful measure that allows us to discuss the influence of primary chain length and non-random network architecture on mesh size. It is hoped that the MSI concept proposed here will contribute to bridging the gap between theory and experiment.
This study presents a molecular dynamics investigation of CH4, CO2, and CO transport through aligned single-walled carbon nanotubes (SWCNTs) embedded within a polyamide-6,6 (PA-6,6) matrix. SWCNTs with nearly identical diameters and lengths but different chiral indices were systematically examined to isolate the specific role of chirality in governing gas adsorption, diffusion, permeability, and selectivity. The surrounding PA-6,6 matrix acts as a structural barrier, restricting molecular transport exclusively to the inner nanotube channels. The results reveal that gas diffusion coefficients follow the order CO > CH4 > CO2, whereas adsorption strength follows the reverse trend, with CO2 exhibiting the strongest interaction with the CNT walls. Within the solution-diffusion framework, the enhanced adsorption of CO2 compensates for its lower mobility, resulting in an overall permeability trend of CO > CO2 > CH4 across all investigated systems. Carbon monoxide exhibits exceptionally high permeation rates, reaching 134.85 nm(-)(1) s(-)(1) bar(-)(1) in the SWCNT (14,0) structure. Ideal selectivity analysis demonstrates a strong separation preference for CO over CH4 and CO2, while CO2/CH4 selectivity remains near unity. These findings highlight chirality as a critical parameter for tuning nanoscale gas transport in PA-6,6/SWCNT composite membranes.
Polymer-solvent compatibility is commonly evaluated using Hansen solubility parameters (HSPs), where the relative energy difference (RED) is typically interpreted as a deterministic threshold separating good and poor solvents. Experimental evidence shows that polymer solubility evolves gradually in Hansen space, particularly near the solubility boundary, where swelling and partial dissolution are common. In this work, a probabilistic reformulation of the RED criterion is proposed that preserves the geometric structure of Hansen space while interpreting RED as a continuous descriptor mapped to a probability of solubility. Solubility parameters and radii are estimated through numerical optimization using objective functions that balance geometric consistency and probabilistic reliability. Experimental uncertainty associated with partially soluble systems is incorporated through a weighted encoding scheme. The method is evaluated using literature datasets for thermoplastic polyurethanes, poly(ether sulfone), lignin, and waterborne polyurethane, and further examined using experimental data for microcrystalline cellulose, where swelling dominates over true dissolution. In the waterborne polyurethane system, the optimized solubility radius decreases from 16.3 to 7.8 MPa1/2, yielding a more selective solubility domain. Machine learning models trained in the Hansen space provide decision boundaries that, when approximated by isoprobability contours (p approximate to 0.5), agree with the optimized solubility sphere.
This study investigates the thermal decomposition of a crosslinked epoxy resin system based on diglycidyl ether of bisphenol A (DGEBA) and diamines in both inert and oxidative atmospheres. First, kinetic modeling of the degradation process was performed. Under nitrogen, the reaction was described by two successive steps with activation energies of 157 kJ/mol (first step) and 208 kJ/mol (second step). In air, a five-step model was applied with activation energy varying between 120 and 225 kJ/mol, depending on the step. Gaseous decomposition products were analyzed using a Fourier-transform infrared (FTIR) spectrometer and a gas chromatograph-mass spectrometer (GC-MS), both coupled with a thermobalance. The results indicate that the surrounding atmosphere had only a minor impact on the nature of the degradation products, which predominantly included water, phenol, phenolic derivatives, and ammonia. However, in addition to the products detected in nitrogen, carbon dioxide, and carbon monoxide were additionally detected in the oxidative environment.
The tendency of nanofillers to agglomerate within the polymer matrix severely restricts their reinforcing efficiency for the matrix. This study innovatively designs Rebar graphene with a unique 3D interlocking structure by covalently bonding carbon nanotubes to graphene. This study systematically investigates its effect on enhancing the mechanical properties of composite materials, clarifying the interfacial reinforcement mechanisms via adsorption and pull-out simulations, and reveals the role of dense effective thickness layers around Rebar graphene through atomic distribution analysis. These multi-dimensional findings elucidate the superior synergistic reinforcement mechanism of Rebar graphene, providing novel insights for high-performance composite design. Adsorption simulations reveal that this structure significantly restricts PE chain mobility through interlocking, with reduced MSD and about 200 % higher interfacial interaction energy, strengthening interfacial bonding. Pull-out simulations show Rebar graphene/PE achieves a peak pull-out force of 5.26 nN, 134.8 % higher than graphene/PE, with slower force attenuation during debonding, indicating better interfacial load transfer and toughness. Atomic distribution analysis confirms that Rebar graphene promotes a dense, effective thickness layer of PE chains around it, further enhancing stiffness. These findings clarify Rebar graphene's synergistic reinforcement mechanism from interfacial, load transfer, and microstructure aspects, offering key theoretical support for high-performance nanofiller-reinforced polymer composite design.
Fluctuations in thermodynamic equilibrium states are a useful tool to investigate a dynamic molecular motion in a small system inside a large homogeneous system. The thermodynamic equilibrium fluctuations of the volume, V, pressure, P, entropy, S, and temperature, T, in the small system for polyethylene (PE) were determined empirically based on an empirical P-V-T-S equation of state and theories by Landau and Lifshitz and by Kubo where it was found there was two types of the fluctuation probability density and a simple relationship between a square average of fluctuation for P, V, T and S.
Twin-screw extruders (TSEs) are widely applied in polymer processing, where the screw configuration critically influences melt flow behavior and product performance. Existing analyses of these effects are mostly based on the assumption of fully filled flow, which deviates from real processing conditions and limits accurate evaluation of mixing efficiency and energy dissipation characteristics. To address this limitation, a screw configuration analysis algorithm based on dynamic fill degree is developed to enable real-time evaluation of multi-parameter screw characteristics, material fill degree, and rheological behavior. Experimental validation on an industrial Phi 30 mm extruder processing polypropylene demonstrates high accuracy in characterizing screw performance and material distribution. By integrating the experimental data, an energy correction model is further established, reducing the prediction error to within +/- 5%. This study provides a novel tool for precise control and energy assessment of twin-screw extrusion processes, facilitating reduced energy waste and supporting industrial energy conservation.
The melt flow index (MFI) is a fundamental indicator of polymer processability, directly related to molecular weight and melt viscosity, particularly during industrial granulation with twin-screw extruders. Conventional laboratory measurement of MFI is offline, time-consuming, and unsuitable for real-time quality control. To address this limitation, a machine-learning-based soft sensor was developed to predict MFI in-line using multivariate process-variable data from an industrial extrusion-granulation system. Key process parameters were identified based on field knowledge, followed by data preprocessing and feature engineering. Minority transition regions were augmented using the k-Nearest Neighbour Synthetic Minority Oversampling Technique (KNN-SMOTE), thereby improving model robustness in high-deviation regimes and across grade transition boundaries. Also, eXtreme Gradient Boosting (XGBoost) models, which employ an ensemble gradient-boosting framework, were evaluated via hyperparameter optimization. The results indicated that augmentation with smaller k values and lower thresholds for detecting high-deviation responses achieved superior performance, with an R2 of 0.99 and an RMSE of 0.59. In addition, SHapley Additive exPlanations (SHAP) analysis confirmed model interpretability by identifying the dominant process relevant features and their consistent directional influence on MFI predictions. Overall, the proposed framework enables real-time monitoring of MFI in polyethylene granulation, reducing the frequency of laboratory testing and improving product quality control.
The injection molding process critically affects the mechanical reliability of 110 kV silicone rubber cable intermediate joints. In this study, a 3D geometric model based on the actual joint cross-section was established and applied to insert injection molding simulations. An orthogonal experimental design combined with range analysis and analysis of variance (ANOVA) was used to quantify the effects of key process parameters on flow behavior, viscosity, and volume shrinkage. The results show that melt temperature predominantly controls the flow front temperature and maximum viscosity, with contribution ratios of 87.89% and 75.72%, respectively, while mold surface temperature is the primary factor governing both maximum and average volume shrinkage rates, contributing up to 98.63%. At a significance level of 0.01, packing time is identified as the only parameter with a statistically significant effect on average volume shrinkage, and interaction analysis indicates that only the flow front temperature is sensitive to the interaction between melt and mold surface temperatures. A comprehensive evaluation using the entropy weight method yields an optimal parameter combination: mold surface temperature 470 K, melt temperature 285 K, packing pressure 80%, packing time 12 s, and curing time 30 s. These findings provide quantitative guidance for high-quality molding of high-voltage silicone rubber joints.
Conventional gelation theories have long faced challenges in accounting for the effects of cyclization, a persistent issue in polymerization kinetics. To address this, we introduce a kinetic framework focused on the evolution of structural units (monads), which balances mathematical simplicity with the retention of critical structural information. Analytical solutions for monad distribution functions (MDFs) are derived. A novel concept, polymer growth dimensionality (PGD, D P) derived from MDF, is introduced to quantify monads' connectivity. By defining gelation at D P = 1, the newly developed gelation criterion reproduces the classical gel points of Carothers and Flory-Stockmayer for various self- and cross-condensation systems. The identification of two additional crossover points at D P = 2 and 3, corresponding to the formation of 2D and 3D network structures, respectively, represents an extension of conventional gelation theory. Furthermore, as primary cyclization reactions occur at the monad level, their impact on gelation can be theoretically quantified, addressing a longstanding challenge. Our monad-based PGD offers a simplified, parallel, and expanded toolkit for gelation analysis.
To design high performance of organic/inorganic thermoelectric composite materials by molecular simulation, composite materials (PPy/NG) are constructed by incorporating graphene (GE) modified with N atoms (NG) into the Polypyrrole (PPy) matrix. For different GE doping concentrations and different concentration N atoms modified in GE (mod-Ns), the thermoelectric properties of PPy/n-NG composite materials (n represents different N atoms modified concentration) is systematically investigated using the non-equilibrium molecular dynamics (NEMD) and density functional theory (DFT). It is found that N-modification on GE has a significant influence on the reduction on the thermal conductivity of composites with 7.06 wt.% graphene concentration. Moreover, when the mod-Ns concentration reached 3.66%, the thermal conductivity of the PPy/NG composite material is decreased by 49.19%. Additionally, the electron properties of PPy/n-NG are studied. It is found that the energy differences between the HOMO of n-NG and the LUMO of PPy decrease with the increase of mod-Ns concentration in NG. Overall, this study reveals that n-NG reduces the thermal conductivity of PPy/n-NG composites and promotes the transfer of electrons between n-NG and PPy. It establishes a theoretical foundation for designing high-performance organic/ inorganic thermoelectric materials.
Blade coating is a process used to provide a consistent liquid coating on a moving sheet, with significant applications in coated plastic industries, plastic polyvinyl chloride (PVC) fabrics, and paints. In this article, we studied the non-isothermal analysis of the blade coating process with non-linear slip effects at the blade surface. The material used to coating the substrate/web is characterized using the Yeleswarapu fluid model. The basic laws of fluid dynamics are implemented to modeled the 2D incompressible flow equations in the process of blade coating. The modeled equations are simplified with the help of normalized variables and the low Reynolds approximation theory. The simplified partial differential equations are solved numerically using a hybrid scheme, which is a combination of shooting and finite difference algorithms. The influence of the material parameters and slip coefficient on the engineering variables and flow characteristics are visualized with the help of various graphs and tables. The results reveal that the velocity of the coated substrate and coating thickness increase with increasing slip values compared to the Newtonian model. The temperature distribution rises with increasing Brinkman and Weissenberg numbers. The coating thickness decreases by 2.4% and the blade load increases by 49% relative to Newtonian values as the Weissenberg number increases.
We show that the resistance distance between a pair of adjacent vertices in a phantom network generated randomly by a Monte-Carlo method depends on the existence of short cycles around it. Here, we assume that phantom networks have no fixed points but their centers of mass are located at a point. The resistance distance corresponds to the mean-square deviation of the end-to-end vector along the strand connecting the adjacent vertices. We generate random networks with fixed valency but different densities of short cycles via a Metropolis method that rewires edges among sequentially neighboring four vertices chosen randomly. In the process the cycle rank is conserved. However, the densities of short cycles are determined by the effective temperature , which appears in the acceptance ratio of rewiring. If a strand has few short cycles around itself, the mean squared deviation of the strand is equal to . If it is part of a short cycle, i.e., the network has a short loop that consists of a sequence of strands including the given strand itself, its resistance distance is smaller than , while if it is not included in a cycle but adjacent to cycles, its resistance distance is larger than . We show it via an electrical circuit analogy of the network. Moreover, we numerically show that the effect of multiple cycles on the resistance distance is expressed as a linear combination of the effects of isolated single cycles. It follows that cycles independently have an effect on the fluctuation properties of a strand in a polymer network.
This review discusses methods for calculating the equilibrium interfacial tension in strongly immiscible polymer blends compatibilized with copolymers. The discussion is focused on typical polymer blends with fine phase structures; in these polymer blends, the volume of the interfacial layer is generally not negligible with respect to the volume of the bulk phases. The main models and calculation procedures that are predominantly used are compared, and their practicalities are discussed. Neither the expressions derived using the dry brush approximation nor those derived using the wet brush approximation are applicable to blends compatibilized with copolymers that have block lengths comparable to those of the compatibilized homopolymers. The amount of a copolymer in the interfacial layer of a polymer blend is generally not negligible with respect to its amount in the bulk phases. Therefore, approximations that are successfully used for the calculation of the interfacial tension measured by common methods cannot be applied to calculation of the interfacial tension in the polymer blends.
