Low-cost and high-safety aqueous Zn-I_(2) batteries attract extensive attention for large-scale energy storage systems.However,polyiodide shuttling and sluggish iodine conversion reactions lead to inferior rate capabi...Low-cost and high-safety aqueous Zn-I_(2) batteries attract extensive attention for large-scale energy storage systems.However,polyiodide shuttling and sluggish iodine conversion reactions lead to inferior rate capability and severe capacity decay.Herein,a three-dimensional polyaniline is wrapped by carboxylcarbon nanotubes(denoted as C-PANI)which is designed as a catalytic cathode to effectively boost iodine conversion with suppressed polyiodide shuttling,thereby improving Zn-I_(2) batteries.Specifically,carboxyl-carbon nanotubes serve as a proton reservoir for more protonated-NH+=sites in PANI chains,achieving a direct I0/I−reaction for suppressed polyiodide generation and Zn corrosion.Attributing to this“proton-iodine”regulation,catalytic protonated C-PANI strongly fixes electrolytic iodine species and stores proton ions simultaneously through reversible-N=/-NH^(+)-reaction.Therefore,the electrolytic Zn-I_(2) battery with C-PANI cathode exhibits an impressive capacity of 420 mAh g^(−1) and ultra-long lifespan over 40,000 cycles.Additionally,a 60 mAh pouch cell was assembled with excellent cycling stability after 100 cycles,providing new insights into exploring effective organocatalysts for superb Zn-halogen batteries.展开更多
Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficie...Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.展开更多
基金supported by the National Natural Science Foundation of China(22209006,21935001)the Natural Science Foundation of Shandong Province(ZR2022QE009)+1 种基金Fundamental Research Funds for the Central Universities(buctrc202307)the Beijing Natural Science Foundation(Z210016).
文摘Low-cost and high-safety aqueous Zn-I_(2) batteries attract extensive attention for large-scale energy storage systems.However,polyiodide shuttling and sluggish iodine conversion reactions lead to inferior rate capability and severe capacity decay.Herein,a three-dimensional polyaniline is wrapped by carboxylcarbon nanotubes(denoted as C-PANI)which is designed as a catalytic cathode to effectively boost iodine conversion with suppressed polyiodide shuttling,thereby improving Zn-I_(2) batteries.Specifically,carboxyl-carbon nanotubes serve as a proton reservoir for more protonated-NH+=sites in PANI chains,achieving a direct I0/I−reaction for suppressed polyiodide generation and Zn corrosion.Attributing to this“proton-iodine”regulation,catalytic protonated C-PANI strongly fixes electrolytic iodine species and stores proton ions simultaneously through reversible-N=/-NH^(+)-reaction.Therefore,the electrolytic Zn-I_(2) battery with C-PANI cathode exhibits an impressive capacity of 420 mAh g^(−1) and ultra-long lifespan over 40,000 cycles.Additionally,a 60 mAh pouch cell was assembled with excellent cycling stability after 100 cycles,providing new insights into exploring effective organocatalysts for superb Zn-halogen batteries.
基金partly supported by the Youth Foundation of the Inner Mongolia Natural Science Foundation[grant number 2024QN06017 and 2025MS06022]the Basic Scientific Research Business Fee Project for Universities in Inner Mongolia[grant numbers 2023XKJX019 and 2023XKJX024]the Central Guidance on Local Science and Technology Development Fund through[grant number 2024ZY0084].
文摘Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems(ITS),playing a pivotal role in mitigating congestion,enhancing route optimization,and improving the utilization efficiency of roadway infrastructure.However,existingmethods struggle in complex traffic scenarios due to static spatio-temporal embedding,restricted multi-scale temporal modeling,and weak representation of local spatial interactions.This study proposes Bi-STAT+,an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions:(1)an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations;(2)frequency-domain analysis in the temporal dimension for simultaneous high-frequency details and low-frequency trend extraction;and(3)an agent attention mechanism in the spatial dimension that enhances local feature extraction through dynamic weight allocation.Extensive experiments were performed on four distinct datasets,including two publicly benchmark datasets(PEMS04 and PEMS08)and two private datasets collected from Baotou and Chengdu,China.The results demonstrate that Bi-STAT+consistently outperforms existing methods in terms of MAE,RMSE,and MAPE,while maintaining strong robustness against missing data and noise.Furthermore,the results highlight that prediction accuracy improves significantly with higher sampling rates,providing crucial insights for optimizing real-world deployment scenarios.
