In this paper, we report and discuss our successful synthesis of monodispersed, polystyrene-coated gold core-shell nanoparticles (Au@PS NPs) for use in highly efficient, air-stable, organic light-emitting diodes (OLEDs) and organic photovoltaics (OPVs). These core-shell NPs retain the dual functions of (1) the plasmonic effect of the Au core and (2) the stability and solvent resistance of the cross-linked PS shell. The monodispersed Au@PS NPs were incorporated into a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) film that was located between the ITO substrate and the emitting layer (or active layer) in the devices. The incorporation of the Au@PS NPs provided remarkable improvements in the performances of both OLEDs and OPVs, which benefitted from the plasmonic effect of the Au@PS NPs. The OLED device with the Au@PS NPs achieved an enhancement of the current efficiency that was 42% greater than that of the control device. In addition, the power conversion efficiency was increased from 7.6% to 8.4% in PTB7:PC71BM-based OPVs when the Au@PS NPs were embedded. Direct evidence of the plasmonic effect on optical enhancement of the device was provided by near-field scanning optical microscopy measurements. More importantly, the Au@PS NPs induced a remarkable and simultaneous improvement in the stabilities of the OLED and OPV devices by reducing the acidic and hygroscopic properties of the PEDOT:PSS layer.

Background Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction. Objective The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction. Methods The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction. Results The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set. Conclusion The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.