Renewable energy driven N_(2) electroreduction with air as nitrogen source holds great promise for realizing scalable green ammonia production.However,relevant out-lab research is still in its infancy.Herein,a novel S...Renewable energy driven N_(2) electroreduction with air as nitrogen source holds great promise for realizing scalable green ammonia production.However,relevant out-lab research is still in its infancy.Herein,a novel Sn-based MXene/MAX hybrid with abundant Sn vacancies,Sn@Ti_(2)CTX/Ti_(2)SnC–V,was synthesized by controlled etching Sn@Ti_(2)SnC MAX phase and demonstrated as an efficient electrocatalyst for electrocatalytic N2 reduction.Due to the synergistic effect of MXene/MAX heterostructure,the existence of Sn vacancies and the highly dispersed Sn active sites,the obtained Sn@Ti2CTX/Ti_(2)SnC–V exhibits an optimal NH_(3) yield of 28.4μg h^(−1) mg_(cat)^(−1) with an excellent FE of 15.57% at−0.4 V versus reversible hydrogen electrode in 0.1 M Na_(2)SO_(4),as well as an ultra-long durability.Noticeably,this catalyst represents a satisfactory NH3 yield rate of 10.53μg h^(−1) mg^(−1) in the home-made simulation device,where commercial electrochemical photovoltaic cell was employed as power source,air and ultrapure water as feed stock.The as-proposed strategy represents great potential toward ammonia production in terms of financial cost according to the systematic technical economic analysis.This work is of significance for large-scale green ammonia production.展开更多
Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detec...Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.展开更多
1 Introduction L exical semantic resource plays an important role in natural language processing.So far,many lexical semantic resources have been developed by the world-wide linguists,such as WordNet[1],ConceptNet[2]i...1 Introduction L exical semantic resource plays an important role in natural language processing.So far,many lexical semantic resources have been developed by the world-wide linguists,such as WordNet[1],ConceptNet[2]in English,HowNet[3],Chinese Concept Dictionary(CCD)[4],and Tongyici-Cilin(Cilin)[5]in Chinese,Diferent recourses usually have different focuses and structures,while some of them are also closely rclated and could be complementary to each other.As a result,the integration of several resources may be more useful than only using one of them for a certain purpose.展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.22308139,52071171,52202248)Natural Science Foundation of Liaoning Province(2023-MS-140)+11 种基金Liaoning BaiQianWan Talents Program(LNBQW2018B0048)Shenyang Science and Technology Project(21-108-9-04)Young Scientific and Technological Talents Project of the Department of Education of Liaoning Province(LQN202008)Key Research Project of Department of Education of Liaoning Province(LJKZZ20220015)Foundation of State Key Laboratory of Clean and Efficient Coal Utilization,Taiyuan University of Technology(MJNYSKL202301)Foundation of State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering(KF2023006)Anhui Province Key Laboratory of Coal Clean Conversion and High Valued Utilization,Anhui University of Technology(CHV22-05)Australian Research Council(ARC)through Future Fellowship(FT210100298,FT210100806)Discovery Project(DP220100603)Linkage Project(LP210100467,LP210200504,LP210200345,LP220100088)Industrial Transformation Training Centre(IC180100005)schemesthe Australian Government through the Cooperative Research Centres Projects(CRCPXIII000077).
文摘Renewable energy driven N_(2) electroreduction with air as nitrogen source holds great promise for realizing scalable green ammonia production.However,relevant out-lab research is still in its infancy.Herein,a novel Sn-based MXene/MAX hybrid with abundant Sn vacancies,Sn@Ti_(2)CTX/Ti_(2)SnC–V,was synthesized by controlled etching Sn@Ti_(2)SnC MAX phase and demonstrated as an efficient electrocatalyst for electrocatalytic N2 reduction.Due to the synergistic effect of MXene/MAX heterostructure,the existence of Sn vacancies and the highly dispersed Sn active sites,the obtained Sn@Ti2CTX/Ti_(2)SnC–V exhibits an optimal NH_(3) yield of 28.4μg h^(−1) mg_(cat)^(−1) with an excellent FE of 15.57% at−0.4 V versus reversible hydrogen electrode in 0.1 M Na_(2)SO_(4),as well as an ultra-long durability.Noticeably,this catalyst represents a satisfactory NH3 yield rate of 10.53μg h^(−1) mg^(−1) in the home-made simulation device,where commercial electrochemical photovoltaic cell was employed as power source,air and ultrapure water as feed stock.The as-proposed strategy represents great potential toward ammonia production in terms of financial cost according to the systematic technical economic analysis.This work is of significance for large-scale green ammonia production.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.61976114 and 61936012)the National Key R&D Program of China(2018YFB1005102).
文摘Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a document.It generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and causes.Existing works adopt pipelined approaches or multi-task learning to address the ECPE task.However,the pipelined approaches easily suffer from error propagation in real-world scenarios.Typical multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction results.To address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging task.Unlike prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging task.Through this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause pairs.To validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark dataset.The experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.U1836221,61772261)the Jiangsu Provincial Research Foundation for Basic Research(BK20170074).
文摘1 Introduction L exical semantic resource plays an important role in natural language processing.So far,many lexical semantic resources have been developed by the world-wide linguists,such as WordNet[1],ConceptNet[2]in English,HowNet[3],Chinese Concept Dictionary(CCD)[4],and Tongyici-Cilin(Cilin)[5]in Chinese,Diferent recourses usually have different focuses and structures,while some of them are also closely rclated and could be complementary to each other.As a result,the integration of several resources may be more useful than only using one of them for a certain purpose.
