Achieving Strong Circularly Polarized Luminescence through Cascade Cationic Insertion in Lead-free Hybrid Metal Halides.

Generating circularly polarized luminescence (CPL) with simultaneous high photoluminescence quantum yield (PLQY) and dissymmetry factor (glum) is difficult due to usually unmatched electric transition dipole moment (µ) and magnetic transition dipole moment (m) of materials. Herein we tackle this issue by playing a "cascade cationic insertion" trick to achieve strong CPL (with PLQY of ~100 %) in lead-free metal halides with high glum values reaching -2.3×10-2 without using any chiral inducers. Achiral solvents of hydrochloric acid (HCl) and N, N-dimethylformamide (DMF) infiltrate the crystal lattice via asymmetric hydrogen bonding, distorting the perovskite structure to induce the "intrinsic" chirality. Surprisingly, additional insertion of Cs+ cation to substitute partial (CH3)2NH2 + transforms the chiral space group to achiral but the crystal maintains chiroptical activity. Further doping of Sb3+ stimulates strong photoluminescence as a result of self-trapped excitons (STEs) formation without disturbing the crystal framework. The chiral perovskites of indium-antimony chlorides embedded on LEDs chips demonstrate promising potential as CPL emitters. Our work presents rare cases of chiroptical activity of highly luminescent perovskites from only achiral building blocks via spontaneous resolution as a result of symmetry breaking.

EEGGAN-Net: enhancing EEG signal classification through data augmentation.

Background: Emerging brain-computer interface (BCI) technology holds promising potential to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained accuracy of electroencephalography (EEG) signal classification poses numerous hurdles in real-world applications. Methods: In response to this predicament, we introduce a novel EEG signal classification model termed EEGGAN-Net, leveraging a data augmentation framework. By incorporating Conditional Generative Adversarial Network (CGAN) data augmentation, a cropped training strategy and a Squeeze-and-Excitation (SE) attention mechanism, EEGGAN-Net adeptly assimilates crucial features from the data, consequently enhancing classification efficacy across diverse BCI tasks. Results: The EEGGAN-Net model exhibits notable performance metrics on the BCI Competition IV-2a and IV-2b datasets. Specifically, it achieves a classification accuracy of 81.3% with a kappa value of 0.751 on the IV-2a dataset, and a classification accuracy of 90.3% with a kappa value of 0.79 on the IV-2b dataset. Remarkably, these results surpass those of four other CNN-based decoding models. Conclusions: In conclusion, the amalgamation of data augmentation and attention mechanisms proves instrumental in acquiring generalized features from EEG signals, ultimately elevating the overall proficiency of EEG signal classification.