Machine learning (ML) approaches are investigated for the prediction of nuclear magnetic resonance (NMR) shifts in aluminosilicate glasses, for which NMR has proven to be a cutting-edge method over the last decade.
International audienceCopper- and cobalt-based layered simple hydroxides (LSH) are successfully functionalized by a series of fluorene mono- and diphosphonic acids, using anionic exchange reactions and a preintercalation strategy. The lateral functionalization of the fluorene moieties has only little impact on the overall structure of the obtained layered hybrid materials but it influences the organization of the molecules within the interlamellar spacing. For bulky fluorene (9,9-dioctyl derivative), luminescence is preserved when inserted into copper and cobalt hydroxydes, whereas it is completely quenched for the other fluorenes. Detailed characterization of the internal structure and chemical bonding properties for copper- and cobalt-based hybrids is performed via ancillary experimental techniques. For the copper-based LSH class, for which more elusive findings are found, first-principles molecular dynamics simulations unravel the fundamental stabilizing role of the H-bonding network promoted within the local environments of the fluorene mono- and diphosphonic acids. The cobalt series of compounds constitute a new class of hybrid magnets, with ordering temperatures ranging from 11.8 to 17.8 K and show a clear magnetoelectric effect. This effect appears above a threshold magnetic field, which is null below the magnetic ordering temperature, and it persists in the paramagnetic regime till about 110 K
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