Sensitized near-infrared luminescence in sulfur-coordinated lanthanide complexes for machine learning-enhanced thermometry
Year: 2026
Type: preprint
Abstract: Lanthanide (Ln 3+ ) coordination complexes featuring only Ln–S bonds have optical properties uniquely modulated by soft donor atoms. Yet, these complexes and their applications are underexplored, due to the stricter environmental control required for their typical preparation relative to complexes containing Ln–O bonds. Herein, we show that Ln-S coordination is ideally suited to generate information-rich near-infrared (NIR) photoluminescence, laying the optimal ground for machine learning-driven optical sensing. We prepared three new homoleptic complexes with general formula [K(2.2.2-cryptand)][Ln( L ) 4 ] (Ln = Nd 3+ ( 1-Nd ), Gd 3+ ( 1-Gd ), or Yb 3+ ( 1-Yb ); L = 2,6-bis-(4- tert -butylphenyl)phenyldithiocarboxylate). In all complexes, the Ln 3+ ion is dodecahedrally coordinated to eight sulfur atoms, generating a crystal field that deviates from the ideal anisotropic case and leads to substantial mixing of the crystal-field states, as revealed by magnetic measurements and ab initio calculations. Temperature-dependent photoluminescence and optical absorption measurements allowed identifying the electronic transitions and energy transfer mechanisms underpinning the complexes’ optical properties, which are dominated by ligand sensitization of the Ln 3+ emission. Survey of the photoluminescence-vs-temperature (calibration) datasets indicated the amenability of 1-Nd and 1-Yb to luminescence thermometry. Chiefly, the complex spectral variations induced by thermal changes can be comprehensively analyzed via principal component analysis, thus extracting global optical features with readout precision that is one order of magnitude higher than classical, local spectral features. More broadly, this study demonstrates that spectral complexity in molecular systems sets the necessary preconditions for advanced optical sensing supported by machine learning algorithms.
Source: ChemRxiv
Institution: Universidad Autónoma de Madrid
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Awards: G4212894969, G4984359381, G7149777892
OpenAlex ID: W7213882416