Chinese Journal of Polymer Science最新文献

Sustained antigen release from delivery systems is a pivotal strategy to enhance vaccine-induced immune responses, primarily by mimicking the antigen exposure kinetics of natural infections to synchronously boost humoral and cellular immunity. However, the absence of an “antigen boost” effect in current approaches stands as a critical bottleneck, limiting the intensity and durability of immune responses. To address the critical gap of insufficient antigen boosting in sustained-release vaccine platforms, we engineered an ultrasound-responsive hydrogel (URH) with diselenide-functionalized 4-arm PEG-ONH₂ (4-arm PEG-Se-Se-ONH₂), 4-arm PEG-ONH₂ and ODEX. Leveraging its exceptional ultrasonic sensitivity, the URH enables timely controlled, multiple-boost antigen release both in vitro and in vivo, overcoming the limitations of conventional sustained-release systems. With the multiple boost release mode triggered by ultrasound, the immune response in lymph nodes was significantly stronger than that in sustained release group without ultrasonic trigger. At the same time, it also greatly improved the humoral immunity level, URH+US-OVA elicited 7.5×10⁴-fold higher anti-OVA IgG titers over commercial Al-OVA vaccines and 440-fold higher than URH-OVA vaccines at day 40 post-vaccination, while the levels of blood routine and inflammatory factors were within the normal range, which proved that the safety of URH vaccines. The results support that the antigen release mode is a key factor affecting the immunological efficacy of vaccines, and URH can be modularized to regulate the multiple boost antigen release mode.

Enhancing the mechanical properties is crucial for polyimide films, but the mechanical properties (Young’s modulus, tensile strength, and elongation at break) mutually constrain each other, complicating simultaneous enhancement via traditional trial-and-error methods. In this work, we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechanical properties. We first established machine learning models to predict Young’s modulus, tensile strength, and elongation at break to explore the chemical space containing thousands of candidate structures. The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films. The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations, and the structural rationale was revealed by “gene” analysis and feature importance evaluation. This work provides a cost-effective strategy for designing polyimide films with enhanced mechanical properties.

Polymer informatics faces challenges owing to data scarcity arising from complex chemistries, experimental limitations, and processing-dependent properties. This review presents the recent advances in data-efficient machine learning for polymers. First, data preparation techniques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning. Second, modeling approaches, including classical algorithms and physics-informed methods, enhance the model robustness and reliability under limited data conditions. Third, learning strategies, such as transfer learning and active learning, aim to improve generalization and guide efficient data acquisition. This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers. This review is expected to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.

Photothermal therapy has been renowned for its non-invasive and highly precise approach in cancer treatment. Therefore, the synthesis of suitable photothermal agent has attracted wide attention. In this study, a non-covalent method for modifying carbon nanotube by hyaluronic acid based azo polymer was proposed. Hyaluronic acid based azo polymer was attached to the surface of carbon nanotube via the π-π interaction and hydrophobic interaction between azo moiety and the wall of carbon nanotube. After the self-assembly process, the modification was demonstrated with transmission electron microscopy, infrared spectra and thermal gravimetric analysis. The modified carbon nanotube (HA-MWCNT) was endowed with both the tumor cell targeting property of hyaluronic acid and the excellent photothermal effect of carbon nanotube, enabling it to serve as photothermal agent for eliminating tumor cells. Upon co-culture with HeLa cells, HA-MWCNT was proved to exhibit low cytotoxicity without irradiation. After being irradiated with infrared laser light, the HeLa cells cultured with HA-MWCNT perished due to the heat generated by the endocytosed HA-MWCNT, which attested to the potential of HA-MWCNT as a photothermal agent for cancer treatment.

The flocculation behavior of carbon black (CB)-filled isoprene rubber (IR) nanocomposites was systematically investigated under both dynamic and static conditions to unravel the distinct mechanisms governing filler network evolution. Under dynamic conditions, small oscillatory shear strains (0.1%) significantly enhanced filler particle motion, leading to pronounced agglomeration and a flocculation degree of about 4.3 MPa at 145 °C. In contrast, static flocculation exhibited a fundamentally different mechanism dominated by polymer chain dynamics, which is driven mainly by thermal activation. Radial distribution function (RDF) analysis of transmission electron microscopy (TEM) images revealed a slight decrease (2 nm) in the interparticle distance peak after static annealing at 100 °C for 7 h, indicating localized motion of CB particles. However, the overall filler network remained stable, with no significant agglomeration observed. The increase in bound rubber content from about 23% to 28% with rising temperature further confirmed the dominant role of polymer chain adsorption and interfacial reinforcement in static flocculation. These findings highlight the critical influence of external strain on filler network formation and provide new insights into the polymer-dominated mechanism of static flocculation. The results offer practical guidance for optimizing the storage and processing of rubber nanocomposites, particularly in applications where static flocculation during prolonged storage is a concern.

Fluorescent polyurea-carbon dots (PU-CD) were successfully achieved through a co-pyrolysis technique, combining polyurea (PU) with carboxyl-containing carbon dots (PCD) at a temperature of 220 °C. The PU was fabricated via a simple precipitation polymerization process using toluene diisocyanate in a water/acetone binary solvent system. PCD was generated by thermal treatment of poly(ethylene glycol) (PEG) at the same elevated temperature. To elucidate the structural characteristics of PU-CD, as well as its precursor components PU and PCD, a comprehensive suite of analytical techniques was employed, including transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), dynamic light scattering (DLS) and X-ray photoelectron spectroscopy (XPS). These analyses confirmed the formation of amide bonds resulting from the reaction between the terminal amines of PU and the carboxyl groups of PCD. An in-depth comparison of the fluorescence properties of PU-CD revealed marked enhancements in fluorescence intensity when contrasted with PU, PEG, and the individual PCD. The research explored the impact of various factors such as concentration, pH in aqueous solutions, and solvent type on the fluorescence emission of these materials, providing valuable insights into their emission mechanisms. It was particularly noteworthy that both PCD and PU-CD exhibited a confined-domain crosslink-enhanced emission effect. Utilizing the aqueous dispersion of PU-CD as a fluorescent probe, the detection of doxycycline (DOX), a long-acting, broad-spectrum, semi-synthetic tetracycline antibiotic, was achieved with a detection limit of 2.9×10⁻⁷ mol/L. This study introduces a simple, green, and cost-effective fluorescent probe for the detection of DOX, which has significant potential for application in the realms of analytical chemistry and food safety monitoring in the future.

The phenomena of thermal runaway and accidental deformation due to external stresses in lithium batteries or film capacitors constitute their primary failure mechanisms. Therefore, monitoring and early warning of overheating or localized strain are of great value for the safe use of lithium batteries or film capacitors; however, this function usually requires a system of multiple complex sensors. The realization of the above multiple hazards using a single sensor for monitoring and alarm functions has not been reported. Here, we exploit the thermally induced conductivity and modulus change during solid-liquid conversion of low melting point polyalloys to modulate the electronic relaxation polarization and interfacial polarization in the composites for dielectric switching, and the reduction of alloy particle spacing during bending/compressive strain can be used to generate switchable tunneling effects for insulator-conductor transition. By synergizing dielectric switching and insulator-conductor transition, the final flexible thermoplastic polyurethane elastomer/low-melting-point polyalloy composite film achieves the functional integration of multi-level overheating warning and small deformation monitoring.

The chain conformation of polymers in binary solvent mixtures is a key issue in the study of functional soft matter and lies at the heart of various applications such as smart soft materials. Based on a minimal lattice model, we employ Monte Carlo (MC) simulation to systematically investigate the effects of solvent qualities on the conformation of a single homopolymer chain in binary mixed solvents. We also perform calculations using a Flory-type mean-field theory. We focus on how the introduction of a second solvent B affects the dependence of chain conformation on the quality of solvent A. We mainly examine the effects of the composition of solvent B, denoted by x, and the interactions between the two solvents. First, when x is low, the mean-square chain radius of gyration exhibits qualitatively similar behaviors to those in an individual solvent A, with a slight chain contraction when solvent A is very good. Second, in equal-molar mixtures with x=0.5, a homopolymer chain collapses when solvent A is either poor or very good, while expands at intermediate qualities. Lastly, at large x, a chain undergoes a coil-to-globule transition with the increasing quality of solvent A when solvent B is good, but mainly adopts the collapsed conformation when solvent B is poor. Our findings not only improve our understanding on the chain conformation in binary solvent mixtures, but also provide valuable guidance on the rational design of stimuli-responsive polymeric materials.

Large-scale molecular dynamics (MD) simulations of crosslinked epoxy with quantum-level accuracy while capturing complex reactivity is a compelling yet unrealized challenge. In this work, through the construction of a chemical-environment-directing dataset, a reactive machine learning force field that accurately captures both reactive events and thermos-mechanical properties is developed. The force field achieves energy and force root-mean-square errors of 1.3 meV/atom and 159 meV/Å, respectively, and operates approximately 1200 times faster than ab initio molecular dynamics. MD simulations demonstrate excellent predictive capabilities across multiple critical thermos-mechanical properties (radial distribution function, density, and elastic modulus), with results being well consistent with experimental values. In particular, the force field can provide accurate prediction of the bond dissociation energies for typical bonds with a mean absolute error of 7.8 kcal/mol (<8%), which enables the simulation of tensile-induced failure caused by chemical bond breaking. Our work demonstrates the capability of the machine learning force field to handle the extraordinary complexity of crosslinked epoxy systems, providing a valuable blueprint for future development of more generalized reactive force fields applicable to most polymers.