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Published on: April 14, 2020
Luminescent copper(i) coordination clusters: structure-property relationships, transformation into hybrid functional
L Anjo1, N Ananikian1,2, G Amatuni1
1Advanced Materials and Microelectronics Laboratory, CANDLE Synchrotron Research Institute 31 Acharyan Yerevan 0022 Armenia anjo@asls.candle.am.
Abstract:
Atomically precise luminescent copper(i) coordination clusters have emerged as a sustainable, earth-abundant, and structurally versatile class of materials, offering a compelling alternative to noble-metal (Au, Ag) luminophores. This review comprehensively addresses recent advances spanning the period 2020 to 2026 in the rational design, photophysical characterization, and functional hybridization of these clusters. We systematically discuss nuclearity control (Cu2 to Cu22), ligand engineering strategies, and the structure-property relationships that govern emission color and quantum yield, with particular emphasis on Cu⋯Cu metallophilic interactions, tetrahedral distortion parameter (Δθ), and ligand rigidity. The roles of metal to ligand charge transfer (MLCT), ligand to metal charge transfer (LMCT), cluster-centered (CC) transitions, and thermally activated delayed fluorescence (TADF) are critically analyzed. Sonochemical synthesis is evaluated as a potentially useful laboratory-scale approach for accelerating nucleation and facilitating in situ hybridization; however, its scalability, passivation efficiency and ability to suppress aggregation-caused quenching remain to be demonstrated through matched comparisons with conventional routes. The review further explores the transformation of discrete Cu(i) clusters into advanced hybrid functional materials incorporating polymer matrices, metal-organic frameworks (MOFs), oxide supports, and carbon-based scaffolds. Emerging applications in chemical sensing, TADF-based optoelectronics (Φ PL up to 99.9%, EQE >35%), and visible-light photocatalysis are critically evaluated. Challenges including Cu(i) oxidation susceptibility, ACQ, and synthetic scalability are addressed, and future directions including defect engineering, machine-learning-assisted design, and sonochemical hybridization are proposed.
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