Energy transfer in Cr and Nd co-doped Si-B-Na-Al-Ca-Zr-O glasses

Abstract

Cr and Nd co-doped glasses are potential gain media for solar pumped lasers (SPLs). Tanabe-Sugano analysis shows that Cr doped Si-B-Na-Al-Ca-Zr-O (SBNACZ) glass contains a mixture of Cr 3+ with octahedral coordination and Cr 6+ with unknown coordination. The crystal field parameters for Cr 3+ were Dq = 1574 cm-1 , B = 792.2 cm-1 , C = 3005 cm-1 and Dq/B = 1.99, indicating a low covalency of Cr-O bonds. We compared the quantum efficiency (QE) of Nd and Cr co-doped SBNACZ glass with Nd doped SBNACZ glass to determine the Cr 3+ →Nd 3+ transfer QE. The Cr2O3 content was fixed at 0.1 mol.% and the Nd2O3 content was varied between 0.1 and 1 mol.%. A maximum transfer QE of 20% was obtained at 0.5 mol.% Nd2O3. Cr 3+ →Nd 3+ energy transfer is dominated by the coulombic interaction mechanism. The clustering of Nd 3+ was a limiting factor for increasing the transfer QE with higher Nd 3+ concentrations.

Gd2O3-modulated borate glass for the enhancement of near-infrared emission via energy transfer from Gd3+ to Nd3+

“…Radiative or non-radiative mechanisms are responsible for ET from a donor to an acceptor. Inorganic systems usually have a small absorption strength for the donor, where radiative transfer mostly does not take place, 49 while its occurrence is further lessened due to the low-absorption cross-section of neodymium ions. Two resonance mechanisms cause strong non-radiative energy transfer: the Dexter mechanism (electron exchange) and the Förster mechanism (coulombic interaction).…”

Prediction of metastable energy level distribution of D3+ (D = Cr and Fe) doped phosphors based on machine learning

“…During data collection, we systematically extracted data from renowned academic databases such as Scopus, Web of Science, ScienceDirect, and IEEE Xplore, which is fundamental for constructing machine learning models. The dataset for Cr-doped samples is shown in Table 2, 43–71 and the dataset for Fe-doped samples is presented in Table 3. 72–96 These data, after preprocessing, serve as input features for the regression models listed in Section 2.3, enabling the prediction of Cr 3+ and Fe 3+ behavior in different environments.…”