CaAl-LDH-Derived High-Temperature CO Capture Materials with Stable Cyclic Performance.

The urgent need to mitigate rising global CO emissions demands the development of efficient carbon capture technologies. This study addresses the persistent challenge of sintering-induced performance degradation in CaO-based sorbents during high-temperature CO capture. A novel solvent/nonsolvent synthetic strategy to fabricate CaO/CaAl-layered double oxide (LDO) composites was developed, where CaAl-LDO serves as a nanostructural stabilizer.

Optimization of deep learning architecture based on multi-path convolutional neural network algorithm.

Current multi-stream convolutional neural network (MSCNN) exhibits notable limitations in path cooperation, feature fusion, and resource utilization when handling complex tasks. To enhance MSCNN's feature extraction ability, computational efficiency, and model robustness, this study conducts an in-depth investigation of these architectural deficiencies and proposes corresponding improvements. At present, there are some problems in multi-path architecture, such as isolated information among paths, low efficiency of feature fusion mechanism, and high computational complexity.