RESUMEN
Applications of organic dyes, ranging from basic research to industry, are functions of their photophysical properties. Two important aspects- (1) knowledge of the photophysical properties of existing dyes long before real applications and (2) discovery of new organic dyes with desired photophysical properties for either upgradation of existing or development of new applications-are needed to be addressed. These two cases are coupled together with the common goal of estimating photophysical properties with high accuracy at the minimum cost of time and money long before the hard-core laboratory experiment. For this purpose, machine learning-based techniques are the most suitable approach. In this study, we used optimized machine-learning techniques to assess a dataset of 3066 organic dyes, which were evaluated using three evaluation parameters: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The Quadratic Support Vector Machine (QSVM) was the best predictive model for RMSE-16.614, MAE-10.837, and R2-0.961 for absorption wavelengths and RMSE-23.636, MAE-16.278, and R2-0.929 for emission wavelengths. These R2 values are 0.7% and 0.4% greater than the Gradient Boost Regression Tree (GBRT) model's recently reported values of 0.954 and 0.925 for absorption and emission wavelengths, respectively. Furthermore, we estimated the quantum yield and found that the Coarse Gaussian Support Vector Machine (CGSVM) outperformed all examined models. For more validation of these models, we compared the predicted results with the experimental results of selective dyes. The proposed automated approach can be used for predicting photophysical properties without much computer programming knowledge.
RESUMEN
Two different pairs of laser dyes, Rhodamine-110 (Rh-110)/Rhodamine-6G (Rh-6G) and Rhodamine-19 (Rh-19)/Rhodamine-B (Rh-B) (the first dye in each pair as a donor and the second as an acceptor) were impregnated in silica samples prepared by the sol-gel method and spectroscopically studied using absorption and steady-state fluorescence techniques. The critical transfer distance (R0), actual distance (r) between the donor and acceptor, overlap integral [J(υ¯)], FRET (fluorescence resonance energy transfer) efficiency (E), and antenna effect efficiency (AE) were investigated in detail based on the variation in acceptor concentration. The FRET efficiency, antenna effect efficiency, and actual donor-acceptor distance for Rh-110/Rh-6G and Rh-19/Rh-B dye pairs corresponding to acceptor concentration ranges (3.83 to 7.65) × 10-5M l-1and (3.71 to 8.34) × 10-5M l-1, respectively, were found to be in the ranges of 57.38% to 74.89%, 36.97% to 24.13%, 5.44 nm to 4.77 nm, and 77.01%. Furthermore, maximum FRET efficiencies of 85.68% and 87.63% and antenna effect efficiencies of 36.97% and 40.95% for Rh-110/Rh-6G and Rh-19/Rh-B, respectively, were also reported. Our results demonstrate the superior FRET efficiency of Rh-19/Rh-B over Rh-110/Rh-6G dye pair in sol-gel glasses, while the antenna effect efficiency of Rh-110/Rh-6G is higher than that of Rh-19/Rh-B for the same donor to acceptor (D/A) ratio. Finally, Rh-110/Rh-6G is a better energy harvester than the Rh-19/Rh-B dye pair at the common D/A ratio. These results are explained in terms of molecular structure similarity, polarity, and rigidity of donor and acceptor.
