Quasi-Shell-Growth Strategy Achieves Stable and Efficient Green InP Quantum Dot Light-Emitting Diodes.

Indium phosphide (InP) based quantum dots (QDs) have been known as an ideal alternative to heavy metals including QDs light emitters, such as cadmium selenium (CdSe) QDs, and show great promise in the next-generation solid-state lighting and displays. However, the electroluminescence performance of green InP QDs is still inferior to their red counterparts, due to the higher density of surface defects and the wider particle size distribution. Here, a quasi-shell-growth strategy for the growth of highly luminescent green InP/ZnSe/ZnS QDs is reported, in which the zinc and selenium monomers are added at the initial nucleation of InP stage to adsorb on the surface of InP cores that create a quasi-ZnSe shell rather than a bulk ZnSe shell. The quasi-ZnSe shell reduces the surface defects of InP core by passivating In-terminated vacancies, and suppresses the Ostwald ripening of InP core at high temperatures, leading to a photoluminescence quantum yield of 91% with a narrow emission linewidth of 36 nm for the synthesized InP/ZnSe/ZnS QDs. Consequently, the light-emitting diodes based on the green QDs realize a maximum luminance of 15606 cd m-2 , a peak external quantum efficiency of 10.6%, and a long half lifetime of > 5000 h.

Suppressing the Cation Exchange at the Core/Shell Interface of InP Quantum Dots by a Selenium Shielding Layer Enables Efficient Green Light-Emitting Diodes.

Indium phosphide (InP) quantum dots (QDs) have demonstrated great potential for light-emitting diode (LED) application because of their excellent optical properties and nontoxicity. However, the over performance of InP QDs still lags behind that of CdSe QDs, and one of main reasons is that the Zn traps in InP lattices can be formed through the cation exchange in the ZnSe shell growth process. Herein, we realized highly luminescent InP/ZnSe/ZnS QDs by constructing Se-rich shielding layers on the surfaces of InP cores, which simultaneously protect the InP cores from the invasion of Zn2+ into InP lattices and facilitate the ZnSe shell growth via the reaction between Zn2+ precursors and Se2- shielding layers. The as-synthesized green InP/ZnSe/ZnS QDs had a high photoluminescence quantum yield (PLQY) of up to 87%. The fabricated QLEDs present a peak external quantum efficiency of 6.2% with an improved efficiency roll-off at high luminance, which is 2 times higher than that of control devices.

Epileptic Seizure Detection Based on Partial Directed Coherence Analysis.

Long-term video EEG epilepsy monitoring can help doctors diagnose and cure epilepsy. The workload of doctors to read the EEG signals of epilepsy patients can be effectively reduced by automatic seizure detection. The application of partial directed coherence (PDC) analysis as mechanism for feature extraction in the scalp EEG recordings for seizure detection could reflect the physiological changes of brain activity before and after seizure onsets. In this study, a new approach on the basis of PDC was proposed to detect the seizure intervals of epilepsy patients. First of all, the multivariate autoregressive model was established for a moving window and the direction and intensity of information flow based on PDC analysis was calculated. Then, the outflow information related to certain EEG channel could be obtained by summing up the intensity of information flow propagated to other EEG channels in order to reduce the feature dimensionality. At last, according to the pathological features of epileptic seizures, the outflow information was regarded as the input vectors to a support vector machine classifier for discriminating interictal periods and ictal periods of EEG signals. The proposed method had achieved a good performance with the correct rate of 98.3%, the selectivity rate of 67.88%, the sensitivity rate of 91.44%, the specificity rate of 99.34%, and the average detection rate of 95.39%, which demonstrated that this method was suitable for detecting the seizure intervals of epilepsy patients. By comparing with other existing techniques, the proposed method based on PDC analysis achieved significant improvement in terms of seizure detection.

The analysis of hand movement distinction based on relative frequency band energy method.

For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.