Hexagonal (β-) NaYF and LiYF doped with trivalent lanthanide ions (Ln, , Er, Tm, and Yb) are well-known photon upconverting materials. This property is crucially determined by the precise location of the Ln dopant ions and their closest neighbouring ions in the host material. However, due to the inherent disorder of the crystal structures the atomistic structure of a disordered crystal such as β-NaYF is not unambiguously provided by X-ray diffraction techniques. Here, theoretical estimates for the true structure of the material are obtained periodic density functional theory (DFT) calculations of large supercells. Our results reveal that Ln doping of β-NaYF occurs in a variety of low-symmetry sites, which are significantly altered by the occupational disorder of the crystal structure. Mainly, the distribution of Na and Y around a doping site significantly influences the positions of the F closest to the dopant. The results of this study are substantiated by applying the same method on the well-ordered host crystal LiYF and by comparison with available experimental and theoretical data. Similar results are expected for other disordered crystalline host materials such as β-NaGdF or cubic (α-) NaYF. The obtained structural information is a prerequisite for future accurate simulations and prediction of key parameters for the upconversion process in bulk materials and nanoparticles.
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http://dx.doi.org/10.1039/d4nr04880f | DOI Listing |
Nanoscale
March 2025
Institute of Physical and Theoretical Chemistry, University of Tübingen, Tübingen (Baden-Württemberg), Germany.
Hexagonal (β-) NaYF and LiYF doped with trivalent lanthanide ions (Ln, , Er, Tm, and Yb) are well-known photon upconverting materials. This property is crucially determined by the precise location of the Ln dopant ions and their closest neighbouring ions in the host material. However, due to the inherent disorder of the crystal structures the atomistic structure of a disordered crystal such as β-NaYF is not unambiguously provided by X-ray diffraction techniques.
View Article and Find Full Text PDFJ Mol Model
March 2025
College of Water Conservancy and Civil Engineering, Shandong Agricultural University, Tai'an 271018, China.
Context: TEMPO-oxidized cellulose nanofibers (TOCNFs) show significant potential for developing high-performance resistive humidity sensors due to their hydrophilicity and structural adaptability. However, the underlying atomic-scale mechanisms governing their humidity response remain poorly understood. Using molecular dynamics simulations, this study investigates how crystal facets, nanopore widths, and humidity levels influence the surface wettability, water permeability, and swelling of TOCNFs.
View Article and Find Full Text PDFNanomaterials (Basel)
February 2025
Deparment of Chemistry, Stockholm University, Svante Arrhenius väg 16 C, 10691 Stockholm, Sweden.
Hydrated anatase (101) titanium dioxide surfaces with oxygen vacancies have been studied using a combination of classical and ab initio molecular dynamics simulations. The reactivity of surface oxygen vacancies was investigated using ab initio calculations, showing that water molecules quickly adsorb to oxygen vacancy sites upon hydration. The oxygen vacancy then quickly reacts with the adsorbed water, forming a protonated bridging oxygen atom at the vacancy site and at a neighboring oxygen bridge.
View Article and Find Full Text PDFNPJ Comput Mater
March 2025
Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Cluster expansions are commonly employed as surrogate models to link the electronic structure of an alloy to its finite-temperature properties. Using cluster expansions to model materials with several alloying elements is challenging due to a rapid increase in the number of fitting parameters and training set size. We introduce the (eCE) formalism that enables the parameterization of accurate on-lattice surrogate models for alloys containing several chemical species.
View Article and Find Full Text PDFThe structural dynamics of proteins play a crucial role in their function, yet most experimental and deep learning methods produce only static models. While molecular dynamics (MD) simulations provide atomistic insight into conformational transitions, they remain computationally prohibitive, particularly for large-scale motions. Here, we introduce DeepPath, a deep-learning-based framework that rapidly generates physically realistic transition pathways between known protein states.
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