Search

Self‐healing polymer coatings have gained increased attention as a pathway for regenerating the inherent properties of surface following mechanical, physical or chemical damages. In most cases, the adhesion between the polymer coating layer and the substrate is driven by physical interactions. For some applications, immersing the polymer coating under water or exposing the coating to organic solvent vapors will undoubtedly lead to the coating delamination. One way to circumvent this issue is to attach the coating to the substrate via covalent bonds. However, this process will impede the self‐healing feature of the polymer coating due to the confinement of the volume change of the polymer to one dimension. Herein, surface‐attached metallo‐supramolecular polymer conetwork coatings are reported. The ability of the polymer conetworks to swell in organic solvent while maintaining their dimensional stability combined with reversible supramolecular cross‐links based on metal complexes enables the design of scratch‐healing polymer coatings. These metallo‐supramolecular polymer conetworks are prepared in a three‐step UV‐initiated polymerization reaction starting from the poly(pentafluorophenyl acrylate)‐l‐polydimethylsiloxane (PPFPA‐l‐PDMS)‐activated ester covalently attached to the substrate. Reacting the polymer conetwork coating with the 4‐(2‐aminoethyl)pyridine and cross‐linking the pyridine phases with ZnCl2 lead to the poly(N‐(2(pyridin‐4‐yl)ethyl)acrylamide‐Zn(II))‐l‐polydimethylsiloxane (PNP4EA‐Zn(II)‐l‐PDMS) polymer conetwork coatings with scratch‐healing ability. [ABSTRACT FROM AUTHOR]

The current challenge in self‐healing materials resides in the design of materials which exhibit improved mechanical properties and self‐healing ability. The design of phase‐separated nanostructures combining hard and soft phases represents an attractive approach to overcome this limitation. Amphiphilic polymer conetworks are nanostructured materials with robust mechanical properties, which can be tailored by tuning the polymer composition and chemical functionality. This article highlights the design of phase‐separated nanostructured polymers from metallo‐supramolecular amphiphilic polymer conetworks, and their application for self‐healing surfaces. The synthesis of poly(N‐(pyridin‐4‐yl)acrylamide)‐l‐polydimethylsiloxane polymer conetworks from the poly(pentafluorophenyl acrylate)‐l‐polydimethylsiloxane activated ester is presented. Loading of ZnCl2 salt into the phase‐separated polymer conetwork strengthens the network by cross‐linking the poly(N‐(pyridin‐4‐yl)acrylamide) phases, while offering reversible interactions needed for self‐healing ability. [ABSTRACT FROM AUTHOR]

A set of poly(isobornyl methacrylate)s (PIBOMA) having molar mass in the range of 26,000–283,000 g mol−1 was prepared either via RAFT process or using free radical polymerization. These linear polymers demonstrated high glass transition temperatures (Tg up to 201 °C) and thermal stability (Tonset up to 230 °C). They were further applied as reinforcing agents in the preparation of the vulcanized rubber compositions based on poly(styrene butadiene rubber) (SBR). The influence of the PIBOMA content and molar mass on the cure characteristics, rheological and mechanical properties of rubber compounds were studied in detail. Moving die rheometry revealed that all rubber compounds filled with PIBOMA demonstrated higher torque increase values ΔS in comparison with rubber compositions without filler, independent of PIBOMA content or molar mass, thus confirming its reinforcing effect. Reinforcement via PIBOMA addition was also observed for vulcanized rubbers in the viscoelastic region and the rubbery plateau, i.e. from −20 to 180 °C, by dynamic mechanical thermal analysis. Notably, while at temperatures above ~125 °C, ultra-high-molecular-weight polyethylene (UHMWPE) rapidly loses its ability to provide reinforcement due to softening/melting, all PIBOMA resins maintained their ability to reinforce rubber matrix up to 180 °C. For rubber compositions containing 20 phr of PIBOMA, both tensile strength and elongation at break decreased with increasing PIBOMA molecular weight. In summary, PIBOMA, with its outstanding high Tg among known poly(methacrylates), may be used in the preparation of advanced high-stiffness rubber compositions, where it provides reinforcement above 120 °C and gives properties appropriate for a range of applications. [ABSTRACT FROM AUTHOR]

{ 1 H- 29 Si}- 1 H double cross polarization inverse detection (DCPi) solid-state NMR, has recently been shown to be a powerful tool for studying molecules adsorbed on the silica surface. In this contribution, we develop an improved version (MCPi) which incorporates a block of multiple contact pulses, and quantitatively compare the sensitivities of MCPi and DCPi over a typical range of experimental parameters. The MCPi pulse sequence aims at higher sensitivity and robustness for studying samples with various relaxation characteristics. In the case of dimethyl sulfoxide (DMSO) molecules adsorbed on the silica surface, MCPi performs equally well or up to 2.5 times better than DCPi over a wide range of parameters. The applicability to and performance of MCPi on composite materials was demonstrated using a sample of polymer-silica composite, where significantly higher sensitivity could be achieved at very long total mixing times. The results also showed that both techniques are surface specific in the sense that only the groups close to the surface can be detected., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The dimethyl sulfoxide (DMSO) solubility data from Enamine and two UCB pharma compound collections were analyzed using 8 different machine learning methods and 12 descriptor sets. The analyzed data sets were highly imbalanced with 1.7-5.8% nonsoluble compounds. The libraries' enrichment by soluble molecules from the set of 10% of the most reliable predictions was used to compare prediction performances of the methods. The highest accuracies were calculated using a C4.5 decision classification tree, random forest, and associative neural networks. The performances of the methods developed were estimated on individual data sets and their combinations. The developed models provided on average a 2-fold decrease of the number of nonsoluble compounds amid all compounds predicted as soluble in DMSO. However, a 4-9-fold enrichment was observed if only 10% of the most reliable predictions were considered. The structural features influencing compounds to be soluble or nonsoluble in DMSO were also determined. The best models developed with the publicly available Enamine data set are freely available online at http://ochem.eu/article/33409 .