ACS polymers Au最新文献

{"title":"Entropic Mixing of Ring/Linear Polymer Blends","authors":"Gary S. Grest*,&nbsp;Ting Ge,&nbsp;Steven J. Plimpton,&nbsp;Michael Rubinstein and Thomas C. O’Connor*,&nbsp;","doi":"10.1021/acspolymersau.2c00050","DOIUrl":"10.1021/acspolymersau.2c00050","url":null,"abstract":"<p >The topological constraints of nonconcatenated ring polymers force them to form compact loopy globular conformations with much lower entropy than unconstrained ideal rings. The closed-loop structure of ring polymers also enables them to be threaded by linear polymers in ring/linear blends, resulting in less compact ring conformations with higher entropy. This conformational entropy increase promotes mixing rings with linear polymers. Here, using molecular dynamics simulations for bead-spring chains, ring/linear blends are shown to be significantly more miscible than linear/linear blends and that there is an entropic mixing, negative χ, for ring/linear blends compared to linear/linear and ring/ring blends. In analogy with small angle neutron scattering, the static structure function <i>S</i>(<i>q</i>) is measured, and the resulting data are fit to the random phase approximation model to determine χ. In the limit that the two components are the same, χ = 0 for the linear/linear and ring/ring blends as expected, while χ &lt; 0 for the ring/linear blends. With increasing chain stiffness, χ for the ring/linear blends becomes more negative, varying reciprocally with the number of monomers between entanglements. Ring/linear blends are also shown to be more miscible than either ring/ring or linear/linear blends and stay in single phase for a wider range of increasing repulsion between the two components.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 2","pages":"209–216"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/07/5b/lg2c00050.PMC10103188.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9430205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Competitive Activation Experiments Reveal Significantly Different Mechanochemical Reactivity of Furan–Maleimide and Anthracene–Maleimide Mechanophores","authors":"Stella M. Luo,&nbsp;Ross W. Barber,&nbsp;Anna C. Overholts and Maxwell J. Robb*,&nbsp;","doi":"10.1021/acspolymersau.2c00047","DOIUrl":"10.1021/acspolymersau.2c00047","url":null,"abstract":"<p >During the past two decades, our understanding of mechanochemical reactivity has advanced considerably. Nevertheless, an incomplete knowledge of structure–activity relationships and the principles that govern mechanochemical transformations limits molecular design. The experimental development of mechanophores has thus benefited from simple computational tools like CoGEF, from which quantitative metrics like rupture force can be extracted to estimate reactivity. Furan–maleimide (FM) and anthracene–maleimide (AM) Diels–Alder adducts are widely studied mechanophores that undergo retro-Diels–Alder reactions upon mechanical activation in polymers. Despite possessing significantly different thermal stability, similar rupture forces predicted by CoGEF calculations suggest that these compounds exhibit similar mechanochemical reactivity. Here, we directly probe the relative mechanochemical reactivity of FM and AM adducts through competitive activation experiments. Ultrasound-induced mechanochemical activation of bis-adduct mechanophores comprising covalently tethered FM and AM subunits reveals pronounced selectivity─as high as ∼13:1─for reaction of the FM adduct compared to the AM adduct. Computational models provide insight into the greater reactivity of the FM mechanophore, indicating a more efficient mechanochemical coupling for the FM adduct compared to the AM adduct. The methodology employed here to directly interrogate the relative reactivity of two different mechanophores using a tethered bis-adduct configuration may be useful for other systems where more common sonication-based approaches are limited by poor sensitivity.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 2","pages":"202–208"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ab/43/lg2c00047.PMC10103189.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9322893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A User’s Guide to Machine Learning for Polymeric Biomaterials","authors":"Travis A. Meyer,&nbsp;Cesar Ramirez,&nbsp;Matthew J. Tamasi and Adam J. Gormley*,&nbsp;","doi":"10.1021/acspolymersau.2c00037","DOIUrl":"10.1021/acspolymersau.2c00037","url":null,"abstract":"<p >The development of novel biomaterials is a challenging process, complicated by a design space with high dimensionality. Requirements for performance in the complex biological environment lead to difficult <i>a priori</i> rational design choices and time-consuming empirical trial-and-error experimentation. Modern data science practices, especially artificial intelligence (AI)/machine learning (ML), offer the promise to help accelerate the identification and testing of next-generation biomaterials. However, it can be a daunting task for biomaterial scientists unfamiliar with modern ML techniques to begin incorporating these useful tools into their development pipeline. This Perspective lays the foundation for a basic understanding of ML while providing a step-by-step guide to new users on how to begin implementing these techniques. A tutorial Python script has been developed walking users through the application of an ML pipeline using data from a real biomaterial design challenge based on group’s research. This tutorial provides an opportunity for readers to see and experiment with ML and its syntax in Python. The Google Colab notebook can be easily accessed and copied from the following URL: www.gormleylab.com/MLcolab</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 2","pages":"141–157"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/da/lg2c00037.PMC10103193.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9551321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding and Modeling Polymers: The Challenge of Multiple Scales","authors":"F. Schmid","doi":"10.1021/acspolymersau.2c0004","DOIUrl":"https://doi.org/10.1021/acspolymersau.2c0004","url":null,"abstract":"Polymer materials have the characteristic feature that they are multiscale systems by definition. Already the description of a single molecules involves a multitude of different scales, and coopera-tive processes in polymer assemblies are governed by the interplay of these scales. Polymers have been among the first materials for which systematic multiscale techniques were developed, yet they continue to present extraordinary challenges for modellers. In this perspective, we review popular models that are used to describe polymers on different scales and discuss scale bridging strategies such as static and dynamic coarse-graining methods and multiresolution approaches. We close with a list of hard problems which still need to be solved in order to gain a comprehensive quantitative understanding of polymer systems on all scales.","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46851042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding and Modeling Polymers: The Challenge of Multiple Scales","authors":"Friederike Schmid*,&nbsp;","doi":"10.1021/acspolymersau.2c00049","DOIUrl":"https://doi.org/10.1021/acspolymersau.2c00049","url":null,"abstract":"<p >Polymer materials are multiscale systems by definition. Already the description of a single macromolecule involves a multitude of scales, and cooperative processes in polymer assemblies are governed by their interplay. Polymers have been among the first materials for which systematic multiscale techniques were developed, yet they continue to present extraordinary challenges for modellers. In this Perspective, we review popular models that are used to describe polymers on different scales and discuss scale-bridging strategies such as static and dynamic coarse-graining methods and multiresolution approaches. We close with a list of hard problems which still need to be solved in order to gain a comprehensive quantitative understanding of polymer systems.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 1","pages":"28–58"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acspolymersau.2c00049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49768376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasticization of a Semicrystalline Metallosupramolecular Polymer Network","authors":"Franziska Marx,&nbsp;Subhajit Pal,&nbsp;Julien Sautaux,&nbsp;Nazim Pallab,&nbsp;Grégory Stoclet,&nbsp;Christoph Weder* and Stephen Schrettl*,&nbsp;","doi":"10.1021/acspolymersau.2c00044","DOIUrl":"10.1021/acspolymersau.2c00044","url":null,"abstract":"<p >The assembly of ligand-functionalized (macro)monomers with suitable metal ions affords metallosupramolecular polymers (MSPs). On account of the reversible and dynamic nature of the metal–ligand complexes, these materials can be temporarily (dis-)assembled upon exposure to a suitable stimulus, and this effect can be exploited to heal damaged samples, to facilitate processing and recycling, or to enable reversible adhesion. We here report on the plasticization of a semicrystalline, stimuli-responsive MSP network that was assembled by combining a low-molecular-weight building block carrying three 2,6-bis(1′-methylbenzimidazolyl) pyridine (Mebip) ligands and zinc bis(trifluoromethylsulfonyl)imide (Zn(NTf₂)₂). The pristine material exhibits high melting (<i>T</i>_m = 230 °C) and glass transition (<i>T</i>_g ≈ 157 °C) temperatures and offers robust mechanical properties between these temperatures. We show that this regime can be substantially extended through plasticization. To achieve this, the MSP network was blended with diisodecyl phthalate. The weight fraction of this plasticizer was systematically varied, and the thermal and mechanical properties of the resulting materials were investigated. We show that the <i>T</i>_g can be lowered by more than 60 °C and the toughness above the <i>T</i>_g is considerably increased.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 1","pages":"132–140"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acspolymersau.2c00044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10773343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Healing Thermosensitive Hydrogel for Sustained Release of Dexamethasone for Ocular Therapy","authors":"Ada Annala,&nbsp;Blessing C. Ilochonwu,&nbsp;Danny Wilbie,&nbsp;Amir Sadeghi,&nbsp;Wim E. Hennink and Tina Vermonden*,&nbsp;","doi":"10.1021/acspolymersau.2c00038","DOIUrl":"10.1021/acspolymersau.2c00038","url":null,"abstract":"<p >The aim of this study was to develop an injectable hydrogel delivery system for sustained ocular delivery of dexamethasone. To this end, a self-healing hydrogel consisting of a thermosensitive ABA triblock copolymer was designed. The drug was covalently linked to the polymer by copolymerization of methacrylated dexamethasone with <i>N</i>-isopropylacrylamide (NIPAM) and <i>N</i>-acryloxysuccinimide (NAS) through reversible addition–fragmentation chain transfer (RAFT) polymerization, using poly(ethylene glycol) (PEG) functionalized at both ends with a chain transfer agent (CTA). Hydrogel formation was achieved by mixing aqueous solutions of the formed thermosensitive polymer (with a cloud point of 23 °C) with cystamine at 37 °C, to result in covalent cross-linking due to the reaction of the <i>N</i>-hydroxysuccimide (NHS) functionality of the polymer and the primary amines of cystamine. Rheological analysis showed both thermogelation and covalent cross-linking at 37 °C, as well as the self-healing properties of the formed network, which was attributed to the presence of disulfide bonds in the cystamine cross-links, making the system injectable. The release of dexamethasone from the hydrogel occurred through ester hydrolysis following first-order kinetics in an aqueous medium at pH 7.4 over 430 days at 37 °C. Based on simulations, administration of 100 mg of hydrogel would be sufficient for maintaining therapeutic levels of dexamethasone in the vitreous for at least 500 days. Importantly, dexamethasone was released from the hydrogel in its native form as determined by LC–MS analysis. Cytocompatibility studies showed that at clinically relevant concentrations, both the polymer and the cross-linker were well tolerated by adult retinal pigment epithelium (ARPE-19) cells. Moreover, the hydrogel did not show any toxicity to ARPE-19 cells. The injectability of the hydrogel, together with the long-lasting release of dexamethasone and good cytocompatibility with a retinal cell line, makes this delivery system an attractive candidate for treatment of ocular inflammatory diseases.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 1","pages":"118–131"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f4/2a/lg2c00038.PMC9912331.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9267589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"There is Signal in Your Noise: A Case for Advanced Mass Analysis","authors":"Benjamin A. Suslick*,&nbsp;Harm-Anton Klok and Jeffrey S. Moore,&nbsp;","doi":"10.1021/acspolymersau.2c00057","DOIUrl":"10.1021/acspolymersau.2c00057","url":null,"abstract":"■ WHY WE NEED NEW ANALYTICAL TOOLS Synthetic chemists often take modern characterization techniques for granted and do not appreciate the fortuitous process of analytical development. Imagine yourself as a 1950s chemist without easy access to spectroscopic instrumentation: how would you unambiguously assign chemical structures to small molecules? This question fueled an explosive growth of technologies that provide molecule-specific fingerprints. The advent of classical characterization tools (e.g., NMR spectroscopy, mass spectrometry) accelerated the rate of discovery in organic chemistry as they provide sufficient information to deduce the identity and purity of a sample. In the context of polymer synthesis, however, these classical tools only provide insights related to bulk composition and often fail to fully capture terminal group speciation. As an unintended consequence, many graphical representations omit the chainends. Despite the lack of comprehensive knowledge, ambitious research programs have sparked a renaissance of renewed interest in developing new analytical methodologies. Postpolymerization reactions, for example, often exploit end-groups for practical applications (e.g., upcycling, dynamic network cross-linking). A need, therefore, exists for new tools that uncover currently elusive structural details. Advanced mass analysis has begun to reemerge as an effective solution to these problems. While the initial development of Kendrick analysis dates to the early 1960s, only recent work from Fouquet and co-workers developed the tools necessary for adaptation to polymer characterization. The power of this mass spectral method is readily apparent in Figure 1. Signals are elegantly pulled from the noise in a traditional mass spectrum by deconvoluting the data and rendering it across multiple dimensions. The resultant Kendrick plot extracts compositional information within a homologous series of polymers. Despite its obvious utility, Kendrick analysis has not yet received the attention it deserves nor is it a common-place technique. Indeed, synthetic polymer chemists are often unaware of its existence despite routinely acquiring mass spectra (e.g., MALDI). Indeed, we only learned of Kendrick analysis from an enlightening tutorial by Fouquet entitled “The Kendrick analysis for polymer mass spectrometry”. Our serendipitous introduction to this technique occurred while we were characterizing complex mixtures of oligomers derived from dicyclopentadiene (DCPD). Monomer resins with the second generation Grubbs catalyst (G2, [(SIMes)Ru(�CHPh)(PCy3)Cl2]) produced short chain oligomers when in the presence of a chain-transfer agent (CTA; e.g., styrene). Traditional characterization techniques approximated the molecular weights of the resultant materials but did not report on the chain-end speciation or fate of the pendent cyclopentene groups. While the MALDI pattern revealed the existence of multiple species, only Kendrick analysis provided defini","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"2 6","pages":"392–396"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f6/bd/lg2c00057.PMC9954250.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"N-Coordinated Organoboron in Polymer Synthesis and Material Science","authors":"Congze He,&nbsp;Jin Dong,&nbsp;Chaoran Xu and Xiangcheng Pan*,&nbsp;","doi":"10.1021/acspolymersau.2c00046","DOIUrl":"10.1021/acspolymersau.2c00046","url":null,"abstract":"<p >Organoboron chemistry has been widely explored and developed in synthetic chemistry for over half a century and provides various elegant synthetic protocols in polymer synthesis. Compared with most trivalent bare organoboron compounds, N-coordinated organoboron shows better performances, such as air and moisture stability. This review summarizes the application of various N-coordinated boranes and boronic acid/esters in polymer synthesis and materials science. We introduce the significance of N-coordinated boranes and boronate esters for controllable polymer synthesis and systematically summarize the structures and properties of polymers containing N-coordinated boronate esters. Furthermore, we highlight the effect of N→B dative bonds on improving the performance of self-healing materials. We hope that, through this review, more researchers will realize the advantages of N-coordinated organoboron and promote the development of this direction in polymer synthesis and materials science.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 1","pages":"5–27"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acspolymersau.2c00046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47847039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale Modeling of Grain-Boundary Motion in Cylinder-Forming Block Copolymers","authors":"Niklas Blagojevic,&nbsp; and ,&nbsp;Marcus Müller*,&nbsp;","doi":"10.1021/acspolymersau.2c00048","DOIUrl":"10.1021/acspolymersau.2c00048","url":null,"abstract":"<p >Using the combination of a soft, coarse-grained, particle-based model, a free-energy functional that depends on the local composition, and a lattice model of local, metastable states, we study the structure and motion of a grain boundary between two orthogonal grains of cylindrical domains in asymmetric block copolymers. The particle-based model provides direct insights into the elementary class of transitions of the self-assembled morphology in the course of grain-boundary translation. These processes are correlated in space and time. We identify a minimal set of transitions, whose free-energy changes and barriers are obtained by describing the system by a free-energy functional of the local composition and calculating the minimum free-energy path (MFEP). The spatiotemporal correlation arises from the dependence of the free-energy characteristics on the local environment. We use this information to parametrize a lattice model of the correlated processes in the course of grain-boundary motion. This allows us to investigate the grain-boundary motion by kinetic Monte Carlo (kMC) simulation and determine its free-energy landscape. Grain-boundary motion proceeds by nucleating a two-dimensional, anisotropic cluster inside the plane of the grain boundary.</p>","PeriodicalId":72049,"journal":{"name":"ACS polymers Au","volume":"3 1","pages":"96–117"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acspolymersau.2c00048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42535749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}