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We distinctly reveal the difference in the exciton generation processes in phosphorescent organic light-emitting devices with an exciplex-type co-host and a single host. Excitons in the co-host consisting of 4,4,4-tris(N-carbazolyl)-triphenylamine and 1,3,5-tris(N-phenylbenzimidazol-2-yl)benzene are created via efficient energy transfer from the exciplex to the phosphorescent dopant. In contrast, excitons in the single host of 4,4,4-tris(N-carbazolyl)-triphenylamine are formed by the combination of holes and electrons trapped by the phosphorescent dopants. The optimized device utilizing the co-host system exhibits highly superior performance relative to the single-host device. The maximum external quantum efficiency and maximum luminance are 14.88% and 90,700 c d / m 2 for the co-host device, being 1.6 times and 3.6 times the maximum external efficiency and maximum luminance for the single-host device, respectively. Significantly, the critical current density, evaluating the device efficiency roll-off characteristic, is as high as 327.8 m A / c m 2 , which is highly superior to 120.8 m A / c m 2 for the single-host device, indicating the notable alleviation in efficiency roll-off for the co-host device. The significant improvement in device performance is attributed to eliminating the exciton quenching resulting from the captured holes and the efficient energy transfer from the exciplex-type co-host to the phosphorescent emitter incurred by the reverse intersystem crossing process.

To figure out the energy attenuation of micro/nanofibers (MNFs) more flexibly and conveniently, a backpropagation neural network (BPNN) is proposed to forecast the output intensity of rhodamine B (RhB) doped polymer microfibers (PMFs). According to the diameter, doping concentration, and propagation distance (L), we realize the L-dependence of output energy predictions for the excitation light (I E ) and fluorescence (I F ) of the doped PMFs. Hundreds of propagation distance-intensity data pairs acquired from dozens of RhB doped PMFs are used for the BPNN training. The prediction ability of the model is evaluated by the root-mean-square error (RMSE), the mean absolute percentage error (MAPE), and R 2 . The output intensity prediction performance of BPNN is compared with the traditional exponential-fitting (EF) method. The prediction results indicate that the two-hidden-layer network with one and seventeen neurons respectively provides the best performance. After training, BPNN gives a good intensity prediction for both the I E (RMSE=3.16 × 10 -2 , MAPE=7.3%, and R 2 =0.9802) and the I F (RMSE=0.91 × 10 -2 , MAPE=0.89%, and R 2 =0.9696) from the output end of the PMF with different diameters and doping concentrations. The energy losses of the two kinds of light from different doped PMFs are also calculated based on the predicted values, which are similar to the ones obtained from the EF method. The approach based on the BPNN prediction for the energy attenuation of the PMFs shows superiority in flexibility and applicability toward the traditional methods, which could promote the optimal design of the MNF devices and the practical application.

With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the helical microfiber (HMF) temperature sensor. The relative output intensities of HMF sensor at different temperatures are predicted by the BPNN with the structural parameters as the input variables. The best-forecasted performance is obtained by the BPNN with one hidden layer of ten neurons. Compared with the actual values, the root-mean-square error (RMSE) and the correlation coefficient of the predicted values are 9.7×10-3dB and 99.84%, respectively. Based on the BPNN with precise prediction, the backward stepwise elimination and the holdback input randomization methods are used to quantitatively discuss the influence of the structural parameters on the output intensity of the HMF. The importance of four geometric parameters obtained by the two methods is ranked the same. The relative importance from high to low is the helical length (~38%), microfiber diameter (~27%), helical angle (~25%), and cone angle (~10%). Quantitative analysis of structural parameters relying on the well-predicted BPNN can give basic information on the structural characteristics of the HMF sensor, which helps to optimize the structure design of the optical sensors based on micro/nanofiber and provides a powerful guarantee for its practical application.

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