abstract
A regular MCM-41 type mesostructured silica was used as a support for the incorporation of the highly luminescent tris(beta-diketonate) complex Eu(tta)(3)ephen yielding the hybrid MCM-Eu material. Suitable characterization by powder X-ray diffraction (XRD), thermogravimetric analyses (TGA), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), C-13 and Si-21 solid state NMR spectroscopy and photoluminescence was accomplished. The combination of ultraviolet-visible spectroscopy (UV-Vis) and photoluminescence techniques shows that the complex incorporation seems to modify essentially the second Eu3+ coordination shell. For a material that has a simply impregnated lanthanide complex, the herein reported maximum D-5(0) quantum yield value of 0.31 is a significantly high value, being almost in the same scale of the values obtained for the materials with covalently bonded complexes. A detailed theoretical photoluminescence study of the MCM-Eu with the recently developed Luminescence Package - LUMPAC is presented. The high accuracy of the theoretical calculations is achieved through the comparison with the experimental values. Aiming at a deeper understanding of the photoluminescence process, the ligand-to-Eu3+ intramolecular energy transfer and back-transfer rates were also predicted. The dominant pathway involves the energy transfer between the lowest energy ligand triplet and the D-5(0) level (9.70 x 10(7) s(-1)).
keywords
ORGANIC-INORGANIC HYBRIDS; INTRAMOLECULAR ENERGY-TRANSFER; EMISSION QUANTUM YIELD; RARE-EARTH COMPLEX; MESOPOROUS SILICA; COORDINATION-COMPOUNDS; SPECTROSCOPIC PROPERTIES; PHOTOPHYSICAL PROPERTIES; LANTHANIDE LUMINESCENCE; DIFFERENTIAL-OVERLAP
subject category
Materials Science; Physics
authors
Felicio, MR; Nunes, TG; Vaz, PM; Botas, AMP; Ribeiro-Claro, P; Ferreira, RAS; Freire, RO; Vaz, PD; Carlos, LD; Nunes, CD; Nolasco, MM
our authors
Groups
G2 - Photonic, Electronic and Magnetic Materials
G3 - Electrochemical Materials, Interfaces and Coatings
G6 - Virtual Materials and Artificial Intelligence
acknowledgements
The authors are grateful to FCT, COMPETE and FEDER programs (Pest-C/CTM/LA0011/2013, Pest-C/CTM/LA0025/2013, PEst-OE/QUI/UI0612/2014, PEst-OE/QUI/UI0100/2014). QREN is thanked for a grant to M.M.N under the project CENTRO-07-ST24-FEDER-002032 (grant number BPD/UI96/3340/2014). The Brazilian author appreciates the financial support from the Brazilian agencies, institutes and networks: CNPq, INAMI and FAPITEC-SE.