Chinese Journal of Polymer Science最新文献

Flexible wearable electronic devices based on hydrogels have immense potential in a wide range of applications. However, many existing strain sensors suffer from significant limitations including poor mechanical properties, low adhesion, and insufficient conductivity. To address these challenges, this study successfully developed an organic-inorganic double-network conductive hydrogel using acrylic-modified bentonite (AABT) as a key component. The incorporation of AABT significantly enhanced the mechanical properties of the ATHG@LiCl hydrogel, achieving an impressive stretchability of 4000% and tensile strength of 250 kPa. Moreover, it improved the electrical conductivity of the hydrogel to a maximum of 1.53 mS/cm. The catechol structure of tannic acid (TA) further augmented the adhesive properties of the ATHG@LiCl hydrogel toward various substrates such as copper, iron, glass, plastic, wood, and pigskin. The addition of lithium chloride (LiCl) and dimethyl sulfoxide (DMSO) endowed the hydrogel with exceptional freezing resistance and flexibility, even at low temperatures of -20 °C. Remarkably, the hydrogel maintained a conductivity of 0.53 mS/cm under these conditions, surpassing the performance of many other reported hydrogels. Furthermore, the ATHG@LiCl hydrogel demonstrated outstanding characteristics, such as high sensitivity (gauge factor GF=4.50), excellent transparency (90%), and reliable strain-sensing capabilities, indicating that the ATHG@LiCl hydrogel is a highly promising candidate for flexible wearable soft materials, offering significant advancements in both functionality and performance.

Chitin, distinguished by its nitrogen-rich acetamido and amino groups, imparts a distinctive cationic nature, enabling chitin to have indispensable features in various applications. Despite its significant promise in the textile industry, particularly for sustainable and functional fabric applications, the practical utilization of chitin fibers remains constrained by insufficient mechanical strength. The degree of deacetylation (DD), a key molecular-level structural determinant, has not been adequately addressed in previous studies despite its critical role in influencing chitin properties across multiple scales. In this study, a deacetylation-mediated design strategy was used to achieve enhanced mechanical performance coupled with multifunctional efficacy using an aqueous KOH/urea solution dissolution system. We prepared a series of deacetylated chitins with different DD values and systematically studied the effect of deacetylation on the multiple-scale structure of regenerated fibers, such as intermolecular interactions and chain orientation at the molecular level, and the aggregation behavior of chitin nanofibers within the gel-state and dried fibers at the micro/nano scale. To achieve an enhanced mechanical performance coupled with multifunctional efficacy by relying on an aqueous KOH/urea solution dissolution system. Moreover, deacetylation enhances intermolecular interactions, resulting in densified internal structures and improved fiber orientation. Concomitantly, it augmented the antimicrobial functionality of the fibers. This deacetylation-mediated design strategy provides a deeper understanding of the structure and properties of regenerated chitin and advances the utility of chitin in strong and sustainable fibers.

Polymer flooding is a widely used technique in enhanced oil recovery (EOR), but its effectiveness is often hindered by the poor viscosity retention of conventional polymers like hydrolyzed polyacrylamide (HPAM) under high-salinity conditions. Although recent advances in molecular engineering have concentrated on modifying polymer architecture and functional groups to address this issue, the complex interplay among polymer topology, charge distribution and hydrophilic-hydrophobic balance renders rational molecular design challenging. In this work, we present an AI-driven inverse design framework that directly maps target viscosity performance back to optimal molecular structures. Guided by practical molecular design strategies, the topological features (grafting density, side-chain length) and functional group-related features (copolymerization ratio, hydrophilic-hydrophobic balance) are encoded into a multidimensional design space. By integrating dissipative particle dynamics simulations with particle swarm algorithm, the framework efficiently explores the design space and identifies non-intuitive, high-performing polymer structure. The optimized polymer achieves a 12% enhancement in viscosity, attributed to the synergistic effect of electrostatic chain extension and hydrophobic aggregation. This study demonstrates the promise of AI-guided inverse design for developing next-generation EOR polymers and provides a generalizable approach for the discovery of functional soft materials.

Advancing the integration of artificial intelligence and polymer science requires high-quality, open-source, and large-scale datasets. However, existing polymer databases often suffer from data sparsity, lack of polymer-property labels, and limited accessibility, hindering systematic modeling across property prediction tasks. Here, we present OpenPoly, a curated experimental polymer database derived from extensive literature mining and manual validation, comprising 3985 unique polymer-property data points spanning 26 key properties. We further develop a multi-task benchmarking framework that evaluates property prediction using four encoding methods and eight representative models. Our results highlight that the optimized degree-of-polymerization encoding coupled with Morgan fingerprints achieves an optimal trade-off between computational cost and accuracy. In data-scarce condition, XGBoost outperforms deep learning models on key properties such as dielectric constant, glass transition temperature, melting point, and mechanical strength, achieving R2 scores of 0.65—0.87. To further showcase the practical utility of the database, we propose potential polymers for two energy-relevant applications: high temperature polymer dielectrics and fuel cell membranes. By offering a consistent and accessible benchmark and database, OpenPoly paves the way for more accurate polymer-property modeling and fosters data-driven advances in polymer genome engineering.

The facile synthesis of high-valued polymers from waste molecules or low-cost common chemicals presents a significant challenge. Here, we develop a series of degradable poly(thiocarbonate)s from the new step-growth polymerization of diols, carbonyl sulfide (COS, or carbon disulfide, CS₂), and dichlorides. Diols and dichlorides are common chemicals, and COS (CS₂) is released as industrial waste. In addition to abundant feedstocks, the method is efficient and performed under mild conditions, using common organic bases as catalysts, and affording unprecedented polymers. When COS, diols, and dihalides were used as monomers, optimized conditions could completely suppress the oxygen-sulfur exchange reaction, enabling the efficient synthesis of well-defined poly(monothiocarbonate)s with melting points ranging from 48 °C to 101 °C. These polymers, which have a structure similar to polyethylene with low-density in-chain polar groups, exhibit remarkable toughness and ductility that rival those of high-density polyethylene (melting point: 90 °C, tensile strength: 21.6±0.7 MPa, and elongation at break: 576%). Moreover, the obtained poly(monothiocarbonate)s can be chemically degraded by alcoholysis to yield small-molecule diols and dithiols. When CS₂ was used in place of COS, a pronounced oxygen-sulfur exchange reaction occurred. By optimizing reaction condition, it was found that polymers with -S(C=O)S- and -S(C=S)S- as the main repeating units exhibited high thermal stability and crystallinity. Thus, a new approach for regulating the structure of polythiocarbonates via the oxygen-sulfur exchange reaction is developed. Overall, the polymers hold great potential for green materials due to their facile synthesis, readily available feedstocks, excellent performance, and chemical degradability.

Semicrystalline polymers usually undergo multilevel microstructural evolutions with annealing and stretching processes, which is essential to tailor the physical properties of the polymer. Here, poly(butylene carbonate) (PBC) sheets were prepared via isothermal annealing and unidirectional pre-stretching processes, then the changes of PBC in crystallinity, mechanical properties, thermal properties and microscopic changes before and after annealing and stretching were measured, as well as the relationship between microstructure and macroscopic properties before and after stretching. The strengthening mechanism of PBC was also described. It was demonstrated that shish-kabab structure emerged under the pre-stretching process. With the increase of the tensile ratio, the crystallinity, structure and mechanical properties are increased differently. Among them, the crystallinity and tensile strength after annealing-stretching treatment increased to 24.45% and 104.5 MPa, respectively, which were about 1.55 times and 3.4 times of those without any treatment.

We report a general method for the synthesis of polymer-decorated metal-organic frameworks (MOFs) for the fabrication of superhydrophobic materials through photoinduced metal-free atom transfer radical polymerization (ATRP). Firstly, an MOF material, ZIF-8-NH₂, was synthesized through the self-assembly of metal ions and organic ligands at room temperature. ZIF-8-NH₂ was then reacted with glycidyl methacrylate (GMA) to form ZIF-8@GMA. Finally, ZIF-8@GMA-PHFBA was prepared by grafting fluorinated monomer 2,2,3,4,4,4-hexafluorobutyl acrylate (HFBA), from the ZIF-8@GMA surface via photoinduced ATRP under 365 nm UV light. The structural evolution during the metal-free ATRP modification of ZIF-8-NH₂ was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and thermogravimetry analysis (TGA). The test results verified that ZIF-8-NH₂ and ZIF-8@GMA-PHFBA were successfully synthesized, and that the surface graft polymerization did not change the structure and morphology of ZIF-8-NH₂. After anchoring the ZIF-8@GMA-PHFBA hybrid material on the fabric surface, the water contact angle (WCA) of the ZIF-8@GMA-PHFBA hybrid material-modified fabric surface reached 154.2°, which achieved a superhydrophobic state. In addition, the oil-water separation experiment and self-cleaning test demonstrated that the ZIF-8@GMA-PHFBA hybrid material-modified fabric has an excellent oil-water separation effect and self-cleaning performance. This material shows promising potential for applications in self-cleaning and oil-water separation technologies.

Stimuli-responsive polymers capable of rapidly altering their chain conformation in response to external stimuli exhibit broad application prospects. Experiments have shown that pressure plays a pivotal role in regulating the microscopic chain conformation of polymers in mixed solvents, and one notable finding is that increasing the pressure can lead to the vanishing of the co-nonsolvency effect. However, the mechanisms underlying this phenomenon remain unclear. In this study, we systematically investigated the influence of pressure on the co-nonsolvency effect of single-chain and multi-chain homopolymers in binary mixed good-solvent systems using molecular dynamics simulations. Our results show that the co-nonsolvency-induced chain conformation transition and aggregation behavior significantly depend on pressure in all single-chain and multi-chain systems. In single-chain systems, at low pressures, the polymer chain maintains a collapsed state over a wide range of co-solvent fractions (x-range) owing to the co-nonsolvency effect. As the pressure increases, the x-range of the collapsed state gradually narrows, accompanied by a progressive expansion of the chain. In multichain systems, polymer chains assemble into approximately spherical aggregates over a broad x-range at low pressures owing to the co-nonsolvency effect. Increasing the pressure reduces the x-range for forming aggregates and leads to the formation of loose aggregates or even to a state of dispersed chains at some x-range. These findings indicate that increasing the pressure can weaken or even offset the co-nonsolvency effect in some x-range, which is in good agreement with the experimental observations. Quantitative analysis of the radial density distributions and radial distribution functions reveals that, with increasing pressure, (1) the densities of both polymers and co-solvent molecules within aggregates decrease, while that of the solvent molecule increases; and (2) the effective interactions between the polymer and the co-solvent weaken, whereas those between the polymer and solvent strengthen. This enhances the incorporation of solvent molecules within the chains, thereby weakening or even suppressing the chain aggregation. Our study not only elucidates the regulatory mechanism of pressure on the microscopic chain conformations and aggregation behaviors of polymers, but also may provide theoretical guidance for designing smart polymeric materials based on mixed solvents.

In this study, an amine-reactive poly(pentafluorophenyl acrylate) (PPFPA) platform was developed for advanced surface engineering of next-generation sequencing (NGS) chips. Through post-polymerization modification, PPFPA was functionalized with dual moieties: azide groups for covalent immobilization of DBCO-modified DNA primers via click chemistry and tunable hydrophilic side chains to optimize biocompatibility and surface properties. Systematic screening revealed that hydrophobic azide carriers combined with neutral hydroxyl groups maximized the DNA immobilization efficacy, approaching the performance of commercial polyacrylamide-based polymers. The negatively charged carboxyl groups severely impede DNA primer attachment. Higher molecular weight derivatives further enhance the efficacy of DNA immobilization. In NGS validation, optimized surface modification polymers achieved robust surface density of clustered DNA and high sequencing accuracy, surpassing quality benchmarks and comparable to those of conventional analogs. This platform demonstrates significant potential for tailoring high-sensitivity surfaces for genomic applications, advancing clinical diagnostics, and personalized medicine.

The self-assembly of block copolymers serves as an effective approach for fabricating various periodic ordered nanostructures. By employing self-consistent field theory (SCFT) to calculate the phase diagrams of block copolymers, one can accurately predict their self-assembly behaviors, thus providing guidance for the fabrication of various novel structures. However, SCFT is highly sensitive to initial conditions because it finds the free energy minima through an iterative process. Consequently, constructing phase diagrams using SCFT typically requires predefined candidate structures based on the experience of researchers. Such experience-dependent strategies often miss some structures and thus result in inaccurate phase diagrams. Recently, artificial intelligence (AI) techniques have demonstrated significant potential across diverse fields of science and technology. By leveraging AI methods, it is possible to reduce reliance on human experience, thereby constructing more robust and reliable phase diagrams. In this work, we demonstrate how to combine AI with SCFT to automatically search for self-assembled structures of block copolymers and construct phase diagrams. Our aim is to realize automatic construction of block copolymer phase diagrams while minimizing reliance on human prior knowledge.