ABSTRACT Gradient polymer materials exhibit great potential for applications in fields such as sensors and biomedicine. While photoinitiated radical polymerization represents a common strategy for gradient fabrication, mesoscopic insights into polymerization kinetics and topological evolution under the control of gradient initiation—similar to photoinitiation—remain limited. In this study, using CGMD simulations, we developed a radical polymerization model incorporating a space‐dependent initiation rate inspired by the mathematical form of Beer's Law. This model was utilized to investigate the reaction kinetics, topological features, and mechanical responses of radical polymerization under a decaying initiation reaction. The results indicate that the decay of the initiation reaction induces spatio‐temporal discrepancies in the reactions within the system. In highly graded systems, small clusters preferentially form and merge in the high‐initiation region, whereas the low‐initiation region exhibits a pronounced reaction lag due to kinetic inhibition, ultimately yielding a gradient distribution of particles and crosslinkers. Under this kinetic heterogeneity, the fraction of “bridges” (elastically effective chains) decreases in the top region but increases in the bottom region due to prolonged free‐radical lifetimes, thereby optimizing the overall topological structure. Mechanical analysis confirms that the initiation decay marginally enhances the overall mechanical performance while reallocating the stress‐bearing capacity toward the high‐initiation region.
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 beta-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.
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.
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 similar 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.
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.
