Macromolecules最新文献

{"title":"Cyclization of Polyacrylonitrile Nanofibers Enhanced by Cu2O During Electrospinning","authors":"Adam Elbataioui, Daniel Bautista-Anguís, Barbora Mayer, Huanqing Zhang, Sandra Schlögl, Libor Kobera, Jiri Brus, Monika Stupavska, Felix Römer, Lidija D. Rafailovic, Jürgen Eckert","doi":"10.1021/acs.macromol.5c00369","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00369","url":null,"abstract":"This work uncovers a novel room-temperature cyclization pathway for polyacrylonitrile (PAN), facilitated by copper(I) oxide (Cu₂O) during electrospinning, paving the way for advanced material design of functional materials. Cyclization, traditionally requiring temperatures around 290 °C, is an essential step in carbon fiber production and the development of advanced functional materials. We demonstrate that thermal pretreatment and the incorporation of Cu₂O enable partial cyclization at ambient conditions, leading to the formation of nonaromatic structures such as azine and imine derivatives during electrospinning. The catalytic role of Cu₂O in influencing cyclization is confirmed by dedicated analytical studies showing a significant extent in its presence. Solid-state nuclear magnetic resonance spectroscopy (ssNMR) and Fourier-transform infrared spectroscopy (FTIR) reveal the chemical transformations induced by high-voltage during electrospinning, emphasizing the interplay between solution preparation, selection of catalysts, and electrospinning conditions. These findings highlight the significance of metal oxides in tailoring polymer chemistry within fiber structures and provide a foundation for exploring alternative catalysts to design nanofiber electrodes optimized for energy conversion applications.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"30 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Heat Resistance, Barrier Property, and Low Carbon Emission PET for Sustainable Food Packaging Applications Based on Biobased Functional Monomers","authors":"Xiaolong Han,&nbsp;Yifei Zhang,&nbsp;Ruidong Wang,&nbsp;Chunxiao Ren,&nbsp;Chenjing Qu,&nbsp;Xiaohui Niu,&nbsp;Weisheng Xiao,&nbsp;Huaxiang Chen* and Penggang Yin*,&nbsp;","doi":"10.1021/acs.macromol.4c0261010.1021/acs.macromol.4c02610","DOIUrl":"https://doi.org/10.1021/acs.macromol.4c02610https://doi.org/10.1021/acs.macromol.4c02610","url":null,"abstract":"<p >PET has a wide range of applications in human life. To address the issues including low glass transition temperature, inadequate oxygen barrier properties, and slight brittleness that limit certain applications of PET, 1,4-cyclohexanedimethanol (CHDM) and isosorbide (IS) were incorporated into the PET main chains using a one-pot two-step method, resulting in the formation of PETGI copolymers. Dynamic mechanical and melt rheological properties revealed that the V-shaped fused ring structure of isosorbide hinders the free movement of the polymer chains, which significantly enhanced the heat resistance of the PETGI copolymers. The glass transition temperature of the PETGI copolymers ranges from 80 to 106 °C. Furthermore, CHDM and IS facilitate the amorphous structure of PETGI, yielding a transparency of 98% and superior toughness, with an elongation at break of 234%. The incorporation of isosorbide markedly increases the molecular chain rigidity of the copolymer while reducing the free volume of the polymer. Time-temperature superposition (TTS) provided insight into the structural influence of different monomers in the molecular chain on the fractional free volume (<i>f</i>_g). As a result of these combined factors, the oxygen barrier performance of the PETGI copolymer containing 40% mol of isosorbide was improved by 41% compared to standard PET. The results of sustainability assessment showed that the equivalent PETGIE₆C₀I₄ reduces carbon dioxide emissions by nearly 15.4% compared to PET.</p>","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"58 10","pages":"5305–5317 5305–5317"},"PeriodicalIF":5.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Heat Resistance, Barrier Property, and Low Carbon Emission PET for Sustainable Food Packaging Applications Based on Biobased Functional Monomers","authors":"Xiaolong Han, Yifei Zhang, Ruidong Wang, Chunxiao Ren, Chenjing Qu, Xiaohui Niu, Weisheng Xiao, Huaxiang Chen, Penggang Yin","doi":"10.1021/acs.macromol.4c02610","DOIUrl":"https://doi.org/10.1021/acs.macromol.4c02610","url":null,"abstract":"PET has a wide range of applications in human life. To address the issues including low glass transition temperature, inadequate oxygen barrier properties, and slight brittleness that limit certain applications of PET, 1,4-cyclohexanedimethanol (CHDM) and isosorbide (IS) were incorporated into the PET main chains using a one-pot two-step method, resulting in the formation of PETGI copolymers. Dynamic mechanical and melt rheological properties revealed that the V-shaped fused ring structure of isosorbide hinders the free movement of the polymer chains, which significantly enhanced the heat resistance of the PETGI copolymers. The glass transition temperature of the PETGI copolymers ranges from 80 to 106 °C. Furthermore, CHDM and IS facilitate the amorphous structure of PETGI, yielding a transparency of 98% and superior toughness, with an elongation at break of 234%. The incorporation of isosorbide markedly increases the molecular chain rigidity of the copolymer while reducing the free volume of the polymer. Time-temperature superposition (TTS) provided insight into the structural influence of different monomers in the molecular chain on the fractional free volume (<i>f</i>_g). As a result of these combined factors, the oxygen barrier performance of the PETGI copolymer containing 40% mol of isosorbide was improved by 41% compared to standard PET. The results of sustainability assessment showed that the equivalent PETGIE₆C₀I₄ reduces carbon dioxide emissions by nearly 15.4% compared to PET.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"42 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Origin of Solvent-Accelerated II–I Phase Transition of Isotactic Poly(1-butene)","authors":"Meilin Ma, Yunpeng Li, Binghui Xu, Hao Zhang, Jian Hu, Wenpeng Zhao, Shaojuan Wang, Rui Xin, Shouke Yan","doi":"10.1021/acs.macromol.5c00823","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00823","url":null,"abstract":"The II–I phase transition process of isotactic poly(1-butene) (iPBu) in xylene and the natural environment has been systemically investigated by FTIR, WAXD, and AFM techniques. It is found that while the nucleation of form I crystals during aging in a natural environment takes a long time, normally over hours, the nucleation of form I crystals in xylene occurs immediately, generally in minutes or even in seconds. In addition, AFM in situ study on the phase transition process of iPBu form II single crystals demonstrates that the growth rate of form I iPBu crystals during phase transition in xylene is about 61 ± 1.7 nm/min, which is 218 times faster than that during aging in a natural environment (0.28 ± 0.05 nm/min). It is thus clear that the quick nucleation and fast growth of form I crystals in xylene result in an accelerated II–I phase transition of iPBu. Moreover, it is found that the thickness of form I iPBu single crystals obtained through the II–I phase in xylene is similar to that of its form II precursor, whereas that of the form I crystal attained in a natural environment decreases by around 15% compared to the original form II crystal. This suggests that xylene vertically expands the chain fold loops at the single-crystal surface and results in the packing of some chain segments in connect with the chains in crystals into regular crystals, which compensates for the contraction of crystals in chain direction and leads to a slight (approximately 5%) increase of crystallinity after transformation in xylene. These results clearly shed more light on the mechanism of the solvent-accelerated II–I phase transition of iPBu.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"121 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Block Copolymer Nanocomposites under Soft Confinement","authors":"Javier Diaz*,&nbsp;Marco Pinna*,&nbsp;Andrei Zvelindovsky* and Ignacio Pagonabarraga*,&nbsp;","doi":"10.1021/acs.macromol.4c0318410.1021/acs.macromol.4c03184","DOIUrl":"https://doi.org/10.1021/acs.macromol.4c03184https://doi.org/10.1021/acs.macromol.4c03184","url":null,"abstract":"<p >Block copolymer (BCP) melts can be blended with solvents to self-assemble into complex droplets with internal structures. Controlling the morphology of these softly confined structures is crucial for various applications, including drug delivery. The addition of nanoparticles (NPs) to BCP droplets produces hierarchical co-assembly with intricate structures, where BCPs act as scaffolds. However, incorporating NPs can significantly alter the BCP droplet structure, leading to emergent behavior. Computer simulations reveal that confinement-induced frustration leads to a Janus-like morphology, with spatially segregated hexagonal and lamellar structures within the droplet bulk. Systematic exploration of NP loading and chemical interactions demonstrates various phase transitions, which are rationalized based on changes in the effective composition and solubility of the BCP droplet. A time-dependent model enables the study of the kinetics of several NP-induced layered morphologies, indicating that changes in the effective solubility of the BCP droplet result in a slow progression toward an onion morphology.</p>","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"58 10","pages":"5240–5253 5240–5253"},"PeriodicalIF":5.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acs.macromol.4c03184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Block Copolymer Nanocomposites under Soft Confinement","authors":"Javier Diaz, Marco Pinna, Andrei Zvelindovsky, Ignacio Pagonabarraga","doi":"10.1021/acs.macromol.4c03184","DOIUrl":"https://doi.org/10.1021/acs.macromol.4c03184","url":null,"abstract":"Block copolymer (BCP) melts can be blended with solvents to self-assemble into complex droplets with internal structures. Controlling the morphology of these softly confined structures is crucial for various applications, including drug delivery. The addition of nanoparticles (NPs) to BCP droplets produces hierarchical co-assembly with intricate structures, where BCPs act as scaffolds. However, incorporating NPs can significantly alter the BCP droplet structure, leading to emergent behavior. Computer simulations reveal that confinement-induced frustration leads to a Janus-like morphology, with spatially segregated hexagonal and lamellar structures within the droplet bulk. Systematic exploration of NP loading and chemical interactions demonstrates various phase transitions, which are rationalized based on changes in the effective composition and solubility of the BCP droplet. A time-dependent model enables the study of the kinetics of several NP-induced layered morphologies, indicating that changes in the effective solubility of the BCP droplet result in a slow progression toward an onion morphology.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"78 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Guided Neural Networks for Transferable Property Prediction in Architecturally Diverse Copolymers","authors":"Shengli Jiang,&nbsp; and ,&nbsp;Michael A. Webb*,&nbsp;","doi":"10.1021/acs.macromol.5c0072010.1021/acs.macromol.5c00720","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00720https://doi.org/10.1021/acs.macromol.5c00720","url":null,"abstract":"<p >The architectural, compositional, and chemical complexities of polymers are fundamentally important to their properties; however, these same factors obfuscate effective predictions. Machine learning offers a promising approach for predicting polymer properties, but model transferability remains a major challenge, particularly when data is scarce due to high acquisition costs or the growth of the parameter space. Here, we examine whether integration with polymer physics theory effectively enhances the transferability of machine learning models to predict properties of architecturally and compositionally diverse polymers. To do so, we first generate <span>ToPoRg-18k</span>─a data set reporting the moments of the distribution of squared radius of gyration for 18,450 polymers with diverse architectures, molecular weights, compositions, and chemical patterns. We then systematically assess the performance of several different models on a series of transferability tasks, such as predicting properties of high-molecular-weight systems from smaller ones or predicting properties of copolymers from homopolymers. We find that a tandem model, <span>GC-GNN</span>, which combines a graph neural network with a fittable model based on ideal Gaussian chain theory, surpasses both standalone polymer physics and graph neural network models in predictive accuracy and transferability. We also demonstrate that predictive transferability varies with polymer architecture due to deviations from the ideal Gaussian chain assumption. Furthermore, the integration with theory endows <span>GC-GNN</span> with additional interpretability, as its learned coefficients correlate strongly with polymer solvophobicity. Overall, this study illustrates the utility of combining polymer physics with data-driven models to improve predictive transferability for architecturally diverse copolymers, showcasing an extension of physics-informed machine learning for macromolecules.</p>","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"58 10","pages":"4971–4984 4971–4984"},"PeriodicalIF":5.1,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Guided Neural Networks for Transferable Property Prediction in Architecturally Diverse Copolymers","authors":"Shengli Jiang, Michael A. Webb","doi":"10.1021/acs.macromol.5c00720","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00720","url":null,"abstract":"The architectural, compositional, and chemical complexities of polymers are fundamentally important to their properties; however, these same factors obfuscate effective predictions. Machine learning offers a promising approach for predicting polymer properties, but model transferability remains a major challenge, particularly when data is scarce due to high acquisition costs or the growth of the parameter space. Here, we examine whether integration with polymer physics theory effectively enhances the transferability of machine learning models to predict properties of architecturally and compositionally diverse polymers. To do so, we first generate <span>ToPoRg-18k</span>─a data set reporting the moments of the distribution of squared radius of gyration for 18,450 polymers with diverse architectures, molecular weights, compositions, and chemical patterns. We then systematically assess the performance of several different models on a series of transferability tasks, such as predicting properties of high-molecular-weight systems from smaller ones or predicting properties of copolymers from homopolymers. We find that a tandem model, <span>GC-GNN</span>, which combines a graph neural network with a fittable model based on ideal Gaussian chain theory, surpasses both standalone polymer physics and graph neural network models in predictive accuracy and transferability. We also demonstrate that predictive transferability varies with polymer architecture due to deviations from the ideal Gaussian chain assumption. Furthermore, the integration with theory endows <span>GC-GNN</span> with additional interpretability, as its learned coefficients correlate strongly with polymer solvophobicity. Overall, this study illustrates the utility of combining polymer physics with data-driven models to improve predictive transferability for architecturally diverse copolymers, showcasing an extension of physics-informed machine learning for macromolecules.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"13 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143946370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Butterfly Patterns for Stretched Inhomogeneous Gel Networks Using Large-Scale Molecular Dynamics Simulations","authors":"Katsumi Hagita*,&nbsp; and ,&nbsp;Takahiro Murashima,&nbsp;","doi":"10.1021/acs.macromol.5c0020710.1021/acs.macromol.5c00207","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00207https://doi.org/10.1021/acs.macromol.5c00207","url":null,"abstract":"<p >Large-scale coarse-grained molecular dynamics simulations of inhomogeneous gel networks were performed to investigate abnormal butterfly patterns in 2D scattering patterns. The networks were diamond lattice-based with distributions in the number of beads between the cross-link points. Remarkably, the results confirm that the abnormal butterfly pattern originates from a stronger inhomogeneity. For the examined systems, the range of scattering wavevector <i>q</i> for the normal butterfly pattern was markedly different from those for the abnormal butterfly patterns. The findings address an essential aspect of the discrepancy between the theoretical prediction and experimental observations. We also confirmed that the peak position <i>q</i>_<i>x</i>* and intensity of the abnormal butterfly pattern decreases and increases, respectively, with increasing stretching ratio λ. As increasing inhomogeneity, it was found that the lower threshold of λ for the region where <i>q</i>_<i>x</i>* and λ have a linear relationship decreased and the peak intensity increased. This analytical approach to the abnormal butterfly pattern allows us to study the difference in network properties of gel networks for different random cross-linking conditions.</p>","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"58 10","pages":"5368–5376 5368–5376"},"PeriodicalIF":5.1,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Butterfly Patterns for Stretched Inhomogeneous Gel Networks Using Large-Scale Molecular Dynamics Simulations","authors":"Katsumi Hagita, Takahiro Murashima","doi":"10.1021/acs.macromol.5c00207","DOIUrl":"https://doi.org/10.1021/acs.macromol.5c00207","url":null,"abstract":"Large-scale coarse-grained molecular dynamics simulations of inhomogeneous gel networks were performed to investigate abnormal butterfly patterns in 2D scattering patterns. The networks were diamond lattice-based with distributions in the number of beads between the cross-link points. Remarkably, the results confirm that the abnormal butterfly pattern originates from a stronger inhomogeneity. For the examined systems, the range of scattering wavevector <i>q</i> for the normal butterfly pattern was markedly different from those for the abnormal butterfly patterns. The findings address an essential aspect of the discrepancy between the theoretical prediction and experimental observations. We also confirmed that the peak position <i>q</i>_<i>x</i>* and intensity of the abnormal butterfly pattern decreases and increases, respectively, with increasing stretching ratio λ. As increasing inhomogeneity, it was found that the lower threshold of λ for the region where <i>q</i>_<i>x</i>* and λ have a linear relationship decreased and the peak intensity increased. This analytical approach to the abnormal butterfly pattern allows us to study the difference in network properties of gel networks for different random cross-linking conditions.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":"19 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}