Engineering Periodic Dinuclear Lanthanide-Directed Networks Featuring Tunable Energy Level Alignment and Magnetic Anisotropy by Metal Exchange.
Daniel MorenoSofia de Oliveira ParreirasJosé I UrgelBeatriz Muñiz CanoCristina Martín-FuentesKoen LauwaetManuel ValvidaresMiguel Angel ValbuenaJosé M GallegoJose Ignacio MartínezPierluigi GargianiJulio CamareroRodolfo MirandaDavid EcijaPublished in: Small (Weinheim an der Bergstrasse, Germany) (2022)
The design of lanthanide multinuclear networks is an emerging field of research due to the potential of such materials for nanomagnetism, spintronics, and quantum information. Therefore, controlling their electronic and magnetic properties is of paramount importance to tailor the envisioned functionalities. In this work, a multidisciplinary study is presented combining scanning tunneling microscopy, scanning tunneling spectroscopy, X-ray absorption spectroscopy, X-ray linear dichroism, X-ray magnetic circular dichroism, density functional theory, and multiplet calculations, about the supramolecular assembly, electronic and magnetic properties of periodic dinuclear 2D networks based on lanthanide-pyridyl interactions on Au(111). Er- and Dy-directed assemblies feature identical structural architectures stabilized by metal-organic coordination. Notably, despite exhibiting the same +3 oxidation state, there is a shift of the energy level alignment of the unoccupied molecular orbitals between Er- and Dy-directed networks. In addition, there is a reorientation of the easy axis of magnetization and an increment of the magnetic anisotropy when the metallic center is changed from Er to Dy. Thus, the results show that it is feasible to tune the energy level alignment and magnetic anisotropy of a lanthanide-based metal-organic architecture by metal exchange, while preserving the network design.
Keyphrases
- single molecule
- density functional theory
- high resolution
- molecularly imprinted
- molecular dynamics
- energy transfer
- electron microscopy
- healthcare
- dual energy
- computed tomography
- machine learning
- breast cancer cells
- deep learning
- social media
- hydrogen peroxide
- high throughput
- mass spectrometry
- molecular dynamics simulations
- nitric oxide
- solid phase extraction
- magnetic resonance
- sensitive detection
- neural network
- reduced graphene oxide
- contrast enhanced
- single cell
- solid state
- quantum dots