Characterization of strain recovery and “self-healing” in a self-assembled metallo-gel
Literature Information
Pierre Terech, Minhao Yan, Manuel Maréchal, Guy Royal, Jose Galvez, Sabareesh K. P. Velu
We report a self-assembled metallo suprapolymer gel exhibiting remarkable self-healing features. The Ni2BTC metallo suprapolymer gels result from the complexation of Ni2+ metal ions by a tritopic ligand (bis-terpyridine cyclam) in dimethylformamide (DMF) and an annealing step at 50 °C for 24 hours. The self-healing properties are characterized by visual inspection, rheological and impedance spectroscopy measurements: the results are compared with those of a fatty acid-based molecular organogel chosen as a reference system. The creep–recovery analysis uses the Burgers model for low strains and characterizes a recovery capability of up to 72% of the deformation in Ni2BTC gels while it is only 32% for the fatty acid organogel. At very large strains, the impedance spectroscopy confirms the slow repairing process consistently with the visual observations. Rheological measurements demonstrate the restructuring of the fractured networks. The fatigue of the self-healed gel networks undergoing long sequences of strain-relaxation steps is characterized.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.
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