A Novel PiGF@Diamond Color Converter with a Record Thermal Conductivity for Laser-Driven Projection Display.

High-brightness laser lighting is confronted with crucial challenges in developing laser-excitable color converting materials with effective heat dissipation and super optical performance. Herein, a novel composite of phosphor-in-glass film on transparent diamond (PiGF@diamond) is designed and fabricated via a facile low-temperature co-sintering strategy. The as-prepared La3Si6N11:Ce3+ (LSN:Ce) PiGF@diamond with well-retained optical properties of raw phosphor shows a record thermal conductivity of ≈599 W m-1 K-1, which is about 60 times higher than that of currently well-used PiGF@sapphire (≈10 W m-1 K-1). As a consequence, this color converter can bear laser power density up to 40.24 W mm-2 and a maximum luminance flux of 5602 lm without luminescence saturation due to efficient inhibition of laser-induced heat accumulation. By further supplementing red spectral component of CaAlSiN3:Eu2+ (CASN:Eu), the PiGF@diamond based white laser diode is successfully constructed, which can yield warm white light with a high color rendering index of 89.3 and find practical LD-driven applications. The findings will pave the way for realizing the commercial application of PiGF composite in laser lighting and display.

Hybrid Network Using Dynamic Graph Convolution and Temporal Self-Attention for EEG-Based Emotion Recognition.

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore both spatial information and temporal information for accurate emotion recognition. However, existing studies often focus on either spatial or temporal aspects of EEG signals, neglecting the joint consideration of both perspectives. To this end, this article proposes a hybrid network consisting of a dynamic graph convolution (DGC) module and temporal self-attention representation (TSAR) module, which concurrently incorporates the representative knowledge of spatial topology and temporal context into the EEG emotion recognition task. Specifically, the DGC module is designed to capture the spatial functional relationships within the brain by dynamically updating the adjacency matrix during the model training process. Simultaneously, the TSAR module is introduced to emphasize more valuable time segments and extract global temporal features from EEG signals. To fully exploit the interactivity between spatial and temporal information, the hierarchical cross-attention fusion (H-CAF) module is incorporated to fuse the complementary information from spatial and temporal features. Extensive experimental results on the DEAP, SEED, and SEED-IV datasets demonstrate that the proposed method outperforms other state-of-the-art methods.