Transition metal-nitrogen-carbon(M-N-C)as a promising substitute for the conventional noble metalbased catalyst still suffers from low activity and durability for oxygen reduction reaction(ORR)in proton exchange membr...Transition metal-nitrogen-carbon(M-N-C)as a promising substitute for the conventional noble metalbased catalyst still suffers from low activity and durability for oxygen reduction reaction(ORR)in proton exchange membrane fuel cells(PEMFCs).To tackle the issue,herein,a new type of sulfur-doped ironnitrogen-hard carbon(S-Fe-N-HC)nanosheets with high activity and durability in acid media were developed by using a newly synthesized precursor of amide-based polymer with Fe ions based on copolymerizing two monomers of 2,5-thiophene dicarboxylic acid(TDA)as S source and 1,8-diaminonaphthalene(DAN)as N source via an amination reaction.The as-synthesized S-Fe-N-HC features highly dispersed atomic Fe Nxmoieties embedded into rich thiophene-S doped hard carbon nanosheets filled with highly twisted graphite-like microcrystals,which is distinguished from the majority of M-N-C with soft or graphitic carbon structures.These unique characteristics endow S-Fe-N-HC with high ORR activity and outstanding durability in 0.5 M H_(2)SO_(4).Its initial half-wave potential is 0.80 V and the corresponding loss is only 21 m V after 30,000 cycles.Meanwhile,its practical PEMFC performance is a maximum power output of 628.0 mW cm^(-2)and a slight power density loss is 83.0 m W cm^(-2)after 200-cycle practical operation.Additionally,theoretical calculation shows that the activity of Fe Nxmoieties on ORR can be further enhanced by sulfur doping at meta-site near FeN_(4)C.These results evidently demonstrate that the dual effect of hard carbon substrate and S doping derived from the precursor platform of amid-polymers can effectively enhance the activity and durability of Fe-N-C catalysts,providing a new guidance for developing advanced M-N-C catalysts for ORR.展开更多
Thin-walled cylindrical workpiece is easy to deform during machining and clamping processes due to the insufficient rigidi.Moreover,it’s also difficult to ensure the perpendicularity of flange holes during drilling p...Thin-walled cylindrical workpiece is easy to deform during machining and clamping processes due to the insufficient rigidi.Moreover,it’s also difficult to ensure the perpendicularity of flange holes during drilling process.In this paper,the element birth and death technique is used to obtain the axial deformation of the hole through finite element simulation.The measured value of the perpendicularity of the hole was compared with the simulated value to verify then the rationality of the simulation model.To reduce the perpendicularity error of the hole in the drilling process,the theory of inventive principle solution(TRIZ)was used to analyze the drilling process of thin-walled cylinder,and the corresponding fixture was developed to adjust the supporting surface height adaptively.Three different fixture supporting layout schemes were used for numerical simulation of drilling process,and the maximum,average and standard deviation of the axial deformation of the flange holes and their maximum hole perpendicularity errors were comparatively analyzed,and the optimal arrangement was optimized.The results show that the proposed deformation control strategy can effectively improve the drilling deformation of thin-walled cylindrical workpiece,thereby significantly improving the machining quality of the parts.展开更多
Aiming at mercury and dioxin in fire coal gas as research objects,nonthermal plasma(NTP)catalytic technology was used to investigate the degradation effect of operating condition parameters on mixed pollutants in mixe...Aiming at mercury and dioxin in fire coal gas as research objects,nonthermal plasma(NTP)catalytic technology was used to investigate the degradation effect of operating condition parameters on mixed pollutants in mixed flue gas condition,and to explore the synergistic degradation of Hg0and TCB(1,2,3-trichlorobenzene,TCB)under mixed flue gas conditions.The research results showed that the conversion efficiency of mercury and TCB increased with the additional output of voltage,and decreased with the increase of the gas flow rate.Under optimal reaction conditions:voltage=17 k V,frequency=300 Hz,gas flow rate=21 min^(-1),the conversion efficiency of Hg^(0)and TCB reached the highest 91.4%and 84.98%,respectively.In the NTP catalytic system,active free radicals played an important role in the synergistic conversion of mercury and TCB,which have a competitive effect,to make the conversion efficiency of mixed pollutants lower than a single substance.In the mixed flue gas condition,the mixed gas has an inhibitory effect on the synergistic conversion of mercury and TCB.Kinetic modeling of NTP catalytic synergistic reaction was established.Under three conditions of TCB,mercury and TCB,mixed simulated flue gas,the NTP catalytic technology showed a quasi-firstorder kinetic reaction for the degradation of TCB.According to the synergistic effect of NTP and composites,the transformation and degradation of TCB mainly included two processes:TCB and ring opening,and Hg^(0)was finally oxidized to Hg^(2+).展开更多
Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways....Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approach–Relying on the support vector machine(SVM)-based classification model,the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face,and the identification calculation was carried out for the five test tunnels.Then,the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.Findings–The results show that compared with the two classification models based on neural networks,the SVM-based classification model has a higher classification accuracy when the sample size is small,and the average accuracy can reach 87.9%.After the samples are replaced,the SVM-based classification model can still reach the same accuracy,whose generalization ability is stronger.Originality/value–By applying the identification method described in this paper,the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified,and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting,and can provide a basis for local optimization of support parameters.展开更多
Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience...Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.展开更多
基金finically supported by the National Natural Science Foundation of China(22075055)the Guangxi Science and Technology Project(AB16380030)。
文摘Transition metal-nitrogen-carbon(M-N-C)as a promising substitute for the conventional noble metalbased catalyst still suffers from low activity and durability for oxygen reduction reaction(ORR)in proton exchange membrane fuel cells(PEMFCs).To tackle the issue,herein,a new type of sulfur-doped ironnitrogen-hard carbon(S-Fe-N-HC)nanosheets with high activity and durability in acid media were developed by using a newly synthesized precursor of amide-based polymer with Fe ions based on copolymerizing two monomers of 2,5-thiophene dicarboxylic acid(TDA)as S source and 1,8-diaminonaphthalene(DAN)as N source via an amination reaction.The as-synthesized S-Fe-N-HC features highly dispersed atomic Fe Nxmoieties embedded into rich thiophene-S doped hard carbon nanosheets filled with highly twisted graphite-like microcrystals,which is distinguished from the majority of M-N-C with soft or graphitic carbon structures.These unique characteristics endow S-Fe-N-HC with high ORR activity and outstanding durability in 0.5 M H_(2)SO_(4).Its initial half-wave potential is 0.80 V and the corresponding loss is only 21 m V after 30,000 cycles.Meanwhile,its practical PEMFC performance is a maximum power output of 628.0 mW cm^(-2)and a slight power density loss is 83.0 m W cm^(-2)after 200-cycle practical operation.Additionally,theoretical calculation shows that the activity of Fe Nxmoieties on ORR can be further enhanced by sulfur doping at meta-site near FeN_(4)C.These results evidently demonstrate that the dual effect of hard carbon substrate and S doping derived from the precursor platform of amid-polymers can effectively enhance the activity and durability of Fe-N-C catalysts,providing a new guidance for developing advanced M-N-C catalysts for ORR.
文摘Thin-walled cylindrical workpiece is easy to deform during machining and clamping processes due to the insufficient rigidi.Moreover,it’s also difficult to ensure the perpendicularity of flange holes during drilling process.In this paper,the element birth and death technique is used to obtain the axial deformation of the hole through finite element simulation.The measured value of the perpendicularity of the hole was compared with the simulated value to verify then the rationality of the simulation model.To reduce the perpendicularity error of the hole in the drilling process,the theory of inventive principle solution(TRIZ)was used to analyze the drilling process of thin-walled cylinder,and the corresponding fixture was developed to adjust the supporting surface height adaptively.Three different fixture supporting layout schemes were used for numerical simulation of drilling process,and the maximum,average and standard deviation of the axial deformation of the flange holes and their maximum hole perpendicularity errors were comparatively analyzed,and the optimal arrangement was optimized.The results show that the proposed deformation control strategy can effectively improve the drilling deformation of thin-walled cylindrical workpiece,thereby significantly improving the machining quality of the parts.
基金supported by National Natural Science Foundation of China(No.52270114)。
文摘Aiming at mercury and dioxin in fire coal gas as research objects,nonthermal plasma(NTP)catalytic technology was used to investigate the degradation effect of operating condition parameters on mixed pollutants in mixed flue gas condition,and to explore the synergistic degradation of Hg0and TCB(1,2,3-trichlorobenzene,TCB)under mixed flue gas conditions.The research results showed that the conversion efficiency of mercury and TCB increased with the additional output of voltage,and decreased with the increase of the gas flow rate.Under optimal reaction conditions:voltage=17 k V,frequency=300 Hz,gas flow rate=21 min^(-1),the conversion efficiency of Hg^(0)and TCB reached the highest 91.4%and 84.98%,respectively.In the NTP catalytic system,active free radicals played an important role in the synergistic conversion of mercury and TCB,which have a competitive effect,to make the conversion efficiency of mixed pollutants lower than a single substance.In the mixed flue gas condition,the mixed gas has an inhibitory effect on the synergistic conversion of mercury and TCB.Kinetic modeling of NTP catalytic synergistic reaction was established.Under three conditions of TCB,mercury and TCB,mixed simulated flue gas,the NTP catalytic technology showed a quasi-firstorder kinetic reaction for the degradation of TCB.According to the synergistic effect of NTP and composites,the transformation and degradation of TCB mainly included two processes:TCB and ring opening,and Hg^(0)was finally oxidized to Hg^(2+).
基金supported by the Science and Technology Research and Development Program of CHINA RAILWAY(Grant No.K2018G014,K2020G035)the National Natural Science Foundation of China(Grant No.51878567,51878568).
文摘Purpose–The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.Design/methodology/approach–Relying on the support vector machine(SVM)-based classification model,the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face,and the identification calculation was carried out for the five test tunnels.Then,the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.Findings–The results show that compared with the two classification models based on neural networks,the SVM-based classification model has a higher classification accuracy when the sample size is small,and the average accuracy can reach 87.9%.After the samples are replaced,the SVM-based classification model can still reach the same accuracy,whose generalization ability is stronger.Originality/value–By applying the identification method described in this paper,the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified,and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting,and can provide a basis for local optimization of support parameters.
基金supported by the National Natural Science Foundation of China(NSFC)[Grant Nos.51578458,and 51878568]the China Railway Corporation Science and Technology Research and Development Program[Grant Nos.2017G007-H,2017G007-F,P2018G007,K2018G014,and K2018G014-01].
文摘Classification of surrounding rock is the cornerstone of tunnel design and construction.The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience.To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human participation.An intelligent classification technique based on information technology and artificial intelligence could overcome these issues.In this regard,using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou-Wanzhou high-speed railway in China,an intelligent-classification surrounding-rock database is constructed in this study.Based on a machine learning algorithm,an intelligent classification model is then developed,which has an overall accuracy of 91.9%.Finally,using the core of the model,the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated,and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock.This approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.
