Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1411.5936

Intervalence Charge Transfer Luminescence: Interplay between anomalous and 5d-4f emissions in Yb-doped fluorite-type crystals

Zoila Barandiaran·Luis Seijo
View original source ↗Read source document ↗Export metadata ↓

Abstract

We report the existence of intervalence charge transfer (IVCT) luminescence in Yb-doped fluorites associated with Yb2+-Yb3+ mixed valence pairs. We show that the very broad band, anomalous emission of Yb2+-doped CaF2 and SrF2, usually associated with impurity-trapped excitons, is, rather, an IVCT luminescence associated with Yb2+-Yb3+ mixed valence pairs. It is very efficiently excited by a two-photon upconversion mechanism. The IVCT vertical transition leaves the pair moieties very far from their equilibrium structures; this explains the unexpectedly large band width of the emission band and its low peak energy, because the large reorganization energies are substracted from the normal emission. The ab initio IVCT energy diagrams explain the different luminescent properties of Yb-doped CaF2, SrF2, BaF2, and SrCl2: the presence of IVCT luminescence in Yb-doped CaF2 and SrF2; its coexistence with regular 5d-4f emission in SrF2; its absence in BaF2 and SrCl2; the quenching of all emissions in BaF2; and the presence of additional 5d-4f emissions in SrCl2 which are absent in SrF2. They also allow to interpret and reproduce recent experiments on transient photoluminescence enhancement in Yb2+-doped CaF2 and SrF2, the appearence of Yb2+ 4f -5d absorption bands in the excitation spectra of the IR Yb3+ emission in partly reduced CaF2:Yb3+ samples, and to identify the broad band observed in the excitation spectrum of the so far called anomalous emission of SrF2:Yb2+ as an IVCT absorption, which corresponds to an Yb2+ 4f5/2 -> Yb3+ 4f7/2 electron transfer.

Explore related subjects

physics.chem-phcond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zoila Barandiaran, Luis Seijo. 2014-11-20. Intervalence Charge Transfer Luminescence: Interplay between anomalous and 5d-4f emissions in Yb-doped fluorite-type crystals. https://doi.org/10.1063/1.4902759

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Global approximations to the error function of real argument for vectorized computation

The error function of real argument can be approximated to a given uniform relative accuracy by a single closed-form expression for the whole variable range either in terms of addition, multiplication, division, and square root operations only, or also using the exponential function. The coefficients have been tabulated for up to 128-bit precision. Tests of a computer code implementation using the standard single- and double-precision floating-point arithmetic show good performance and vectorizability. Approximations to the complementary error function have also been found, including those where only additions, multiplications, and one division are needed to reach a uniform absolute accuracy.

physics.chem-ph↗
arXiv2026

From Heuristics to Machine Learning: The Performance Ceiling for Single-Ion Magnets and Its Electronic Origin

Machine learning (ML) is expected to speed up the discovery of single-ion magnets (SIMs), but does the structural information available before synthesis allow such predictions? For 1215 lanthanide complexes from the SIMDAVIS 1.2.1 database we compared three increasing levels of structural description: tabular features of the coordination site, continuous symmetry measures of the coordination polyhedron, and the complete 3D arrangement of atoms. All three converge to an accuracy near 76%, only slightly above the 71% of the single rule "predict SIM for Dy3+". To explain the failures, we combined multireference ab initio calculations with an inspection of the structures behind the high-confidence errors. The SIMs missed by the geometric models are field-induced relaxers whose ground Kramers doublets are prone to tunnelling, a property invisible to geometric descriptors. Many false positives contain several lanthanide centers or radicals, so their relaxation is collective and outside the single-ion picture. The electronic-structure and connectivity information needed to identify SIMs is therefore not accessible to geometric methods alone. Geometric models remain useful: restricting the screening to compounds with high prediction confidence raises the accuracy to 88% while retaining 48% of the dataset. Building on the analysis of the failures, we propose a strategy that combines simple filters for nuclearity and for radicals with ligand-field descriptors from ab initio calculations.

physics.chem-ph↗
arXiv2026

Composition-Dependent Self-Diffusion Coefficients in Liquid Mixtures from Hybrid Machine Learning

Self-diffusion coefficients are key descriptors of molecular mobility, yet experimental data remain scarce, highlighting the need for reliable prediction methods. In previous work, we introduced the hybrid Enhanced Stokes-Einstein (ESE) model, which advanced the state of the art in the physically consistent prediction of self-diffusion coefficients of solutes at infinite dilution in pure solvents by integrating the Stokes-Einstein equation with machine learning (ML). Here, we extend this approach to concentration-dependent self-diffusion coefficients and multicomponent solvents with HADES. This hybrid architecture leverages a deep-set neural network to connect pure-component and mixture prediction within a single framework. HADES predicts self-diffusion coefficients in liquid mixtures with any number of components at any composition and temperature. The only required inputs are SMILES-encoded molecular structures of the components and the pure-component viscosities, making the method broadly applicable. Trained and evaluated on a comprehensive dataset of 2526 data points for 600 systems, HADES significantly outperforms benchmark prediction methods. The trained model and its source code are fully disclosed, and the application is available via an interactive website https://ml-prop.mv.rptu.de/.

physics.chem-ph↗