Actualités

The development of multistep spin crossover materials is of considerable interest for molecular information processing and sensing applications and remains synthetically and mechanistically challenging. Herein, we present the first iron(II) two-dimensional Hofmann structure containing 1,2,4-triazole derivatives and [Au(CN)2]− units, namely, Fe(MeOPhtrz)2[Au(CN)2]2 (1, MeOPhtrz = (E)-1-(2-methoxyphenyl)-N-(4H-1,2,4-triazol-4-yl)methanimine). The complex exhibits a temperature-induced three-step spin crossover behavior, confirmed by magnetic susceptibility, differential scanning calorimetry, Raman spectroscopy, single-crystal X-ray diffraction (SXRD), and optical microscopy. SXRD reveals a pseudothree-dimensional structure assembled through multiple intermolecular interactions, including hydrogen bonding, π–π stacking, and π–Au interactions. These interactions contribute to an anisotropic supramolecular framework that induces a multistep spin crossover process. The sequential spin transition is likely driven by the differential rigidity along the crystallographic axes and the varied response of Fe–N bond lengths, leading to distinct transition steps. This study highlights the significance of supramolecular interactions in governing spin crossover properties and opens new avenues for the design of 2D Hofmann-like materials with tunable functionalities.

In a recent episode of ‘Les visages de la recherche’ (The Faces of Research), viewers were introduced to the work of Ludovic Troian-Gautier.

Particle-exchange heat engines operate without moving parts or time-dependent driving, relying solely on static energy-selective transport. Here, we realize a particle-exchange quantum heat engine based on a single diradical molecule, which is only a few nanometers in size. We experimentally investigate its operation at low temperatures and demonstrate that both the power output and efficiency are significantly enhanced by Kondo correlations, reaching up to 53% of the Curzon–Ahlborn limit. These results establish molecular-scale particle-exchange engines as promising candidates for low-temperature applications where extreme miniaturization and energy efficiency are paramount.

Prof. G.-M. Rignanese was selected to develop the GEMPROMISE project—Generative machine learning for combined process control and materials design—with two other Principal Investigators from the Swiss Federal Institute of Technology in Lausanne (EPFL) and the French National Center for Scientific Research (CNRS).