Variable temperature NMR of organogelators: the intensities of a single sample describe the full phase diagram
Literature Information
Elliot Christ, Dominique Collin, Jean-Philippe Lamps
Organogelators constitute a numerous class of compounds, able to form gels in organic solvents. Their phase diagrams are useful to understand their mechanisms of formation and their stability, but their mapping is often a tedious task. We show that liquid NMR can simplify and quicken the acquisition of phase diagrams. In liquid NMR spectra of organogels, the visible signals of the gelator represent only its soluble fraction. The intensities increase with temperature, until the gel melts. Suitable normalization of these intensities yields the solubility as a function of temperature, which is sufficient to map the phase diagram. We verified it experimentally with three organogelators, chosen because independent authors have previously mapped out their phase diagram by other techniques including DSC and rheology. We show that the curves obtained by NMR superimpose with these diagrams. A variable temperature NMR experiment with a single sample can yield the phase diagram with sensitivity of the order of 0.01 wt%.
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Physical Chemistry Chemical Physics

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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