Flexible damping technology considering aseismic materials and aseismic structures seems be a good solution for engineering structures.In this study,a constrained damping structure for underground tunnel lining,using ...Flexible damping technology considering aseismic materials and aseismic structures seems be a good solution for engineering structures.In this study,a constrained damping structure for underground tunnel lining,using a rubber-sand-concrete(RSC)as the aseismic material,is proposed.The aseismic performances of constrained damping structure were investigated by a series of hammer impact tests.The damping layer thickness and shape effects on the aseismic performance such as effective duration and acceleration amplitude of time-domain analysis,composite loss factor and damping ratio of the transfer function analysis,and total vibration level of octave spectrum analysis were discussed.The hammer impact tests revealed that the relationship between the aseismic performance and damping layer thickness was not linear,and that the hollow damping layer had a better aseismic performance than the flat damping layer one.The aseismic performances of constrained damping structure under different seismicity magnitudes and geological conditions were investigated.The effects of the peak ground acceleration(PGA)and tunnel overburden depth on the aseismic performances such as the maximum principal stress and equivalent plastic strain(PEEQ)were discussed.The numerical results show the constrained damping structure proposed in this paper has a good aseismic performance,with PGA in the range(0.2-1.2)g and tunnel overburden depth in the range of 0-300 m.展开更多
Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safet...Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.展开更多
Constructing unique and highly stable structures with plenty of electroactive sites in sodium storage materials is a key factor for achieving improved electrochemical properties through favorable sodium ion di usion k...Constructing unique and highly stable structures with plenty of electroactive sites in sodium storage materials is a key factor for achieving improved electrochemical properties through favorable sodium ion di usion kinetics. An SnS_2@carbon hollow nanospheres(SnS_2@C) has been designed and fabricated via a facile solvothermal route, followed by an annealing treatment. The SnS_2@C hybrid possesses an ideal hollow structure, rich active sites, a large electrode/electrolyte interface, a shortened ion transport pathway, and, importantly, a bu er space for volume change, generated from the repeated insertion/extraction of sodium ions. These merits lead to the significant reinforcement of structural integrity during electrochemical reactions and the improvement in sodium storage properties, with a high specific reversible capacity of 626.8 mAh g^(-1) after 200 cycles at a current density of 0.2 A g^(-1) and superior high-rate performance(304.4 mAh g^(-1) at 5 A g^(-1)).展开更多
A novel method of measuring the positive-sequence capacitance of T-connection transmission lines is proposed. The mathematical model of the new method is explained in detail. In order to obtain enough independent equa...A novel method of measuring the positive-sequence capacitance of T-connection transmission lines is proposed. The mathematical model of the new method is explained in detail. In order to obtain enough independent equations, three independent operation modes of T-connection transmission lines during the line measurement are introduced. The digital simulation results and field measurement results are shown. The simulation and measurement results have validated that the new method can meet the needs of measuring the positive-sequence capacitance of T-connection transmission lines. This method has been implemented in the newly developed measurement instrument.展开更多
A live line measurement method for the zero sequence parameters of transmission lines with mutual inductance is introduced. The mathematical models of the measurement method are given. Global Positioning System (GPS) ...A live line measurement method for the zero sequence parameters of transmission lines with mutual inductance is introduced. The mathematical models of the measurement method are given. Global Positioning System (GPS) is used as the synchronous signal for the measurement carried out at different substations simultaneously. The measurement system and digital simulation results are given. Finally, the live line measurement results of two 220 kV transmission lines with mutual inductance in Hainangrid are given. Results from both simulation and on-site measurement show that the live line measurement method is feasible, and its measurement accuracy can satisfactorily meet the requirements of engineering measurement.展开更多
After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the...After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the key basis for roadway stability discrimination and support structure design,and it is of great engineering significance to accurately predict the thickness of EDZ.Considering the advantages of machine learning(ML)in dealing with high-dimensional,nonlinear problems,a hybrid prediction model based on the random forest(RF)algorithm is developed in this paper.The model used the dragonfly algorithm(DA)to optimize two hyperparameters in RF,namely mtry and ntree,and used mean absolute error(MAE),rootmean square error(RMSE),determination coefficient(R^(2)),and variance accounted for(VAF)to evaluatemodel prediction performance.A database containing 217 sets of data was collected,with embedding depth(ED),drift span(DS),surrounding rock mass strength(RMS),joint index(JI)as input variables,and the excavation damaged zone thickness(EDZT)as output variable.In addition,four classic models,back propagation neural network(BPNN),extreme learning machine(ELM),radial basis function network(RBF),and RF were compared with the DA-RF model.The results showed that the DARF mold had the best prediction performance(training set:MAE=0.1036,RMSE=0.1514,R^(2)=0.9577,VAF=94.2645;test set:MAE=0.1115,RMSE=0.1417,R^(2)=0.9423,VAF=94.0836).The results of the sensitivity analysis showed that the relative importance of each input variable was DS,ED,RMS,and JI from low to high.展开更多
This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the data...This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network(CNN)-based models,i.e.,U-Net(University of Freiburg,Germany)and DeepLabV3+(Google,USA)models.Chain code technology,polyline approximation algorithm,and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces,including trace length,dip angle,density,and intensity.The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise,especially the DeepLabV3+model.Furthermore,the quantization results further prove the reliability of extracting the fracture trace.As a result,a case study with two types of traces(i.e.,on flat and uneven surfaces)demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.展开更多
A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six ...A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.展开更多
Recently,defect architectured photocatalysis is proved to be the most versatile choice for high solar to chemical energy conversion processes.Defect engineering strategies are of great demand to effectively tune the e...Recently,defect architectured photocatalysis is proved to be the most versatile choice for high solar to chemical energy conversion processes.Defect engineering strategies are of great demand to effectively tune the electronic microstructure and surface morphologies of semiconductors to boost charge carrier concentration and extend light harvesting capability.This review provides a comprehensive insight to various kinds of defects along with their synthesis procedures and controlling mechanism to uplift photocatalytic activity.In addition,the contribution made by defects and material optimization techniques toward electronic band structure of the photocatalyst,the optimal concentration of defects,the key adsorption processes,charge distribution,and transfer dynamics have been explained in detail.Further,to clarify the relationship between photocatalytic activity and defect states in real,a comprehensive outlook to the versatile photocatalytic applications has been presented to highlight current challenges and future applications.Defect engineering therefore stands as the next step toward advancement in the design and configuration of modern photocatalysts for high efficiency photocatalysis.展开更多
The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)pandemic has posed significant challenges in early viral diagnosis.Hence,it is urgently desirable to develop a rapid,inexpensive,and s...The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)pandemic has posed significant challenges in early viral diagnosis.Hence,it is urgently desirable to develop a rapid,inexpensive,and sensitive method to aid point-of-care SARS-CoV-2 detection.In this work,we report a highly sequence-specific biosensor based on nanocomposites with aggregationinduced emission luminogens(AIEgen)-labeled oligonucleotide probes on graphene oxide nanosheets(AIEgen@GO)for one step-detection of SARS-CoV-2-specific nucleic acid sequences(Orf1ab or N genes).A dual“turn-on”mechanism based on AIEgen@GO was established for viral nucleic acids detection.Here,the first-stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid,and the second-stage enhancement of AIEbased fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen.Furthermore,the feasibility of our platform for diagnostic application was demonstrated by detecting SARS-CoV-2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification.Our platform shows great promise in assisting the initial rapid detection of the SARS-CoV-2 nucleic acid sequence before utilizing quantitative reverse transcription-polymerase chain reaction for second confirmation.展开更多
Volatile organic compounds(VOCs)are major contributors to air pollution.Based on the emission characteristics of 99 VOCs that daily measured at 10 am in winter from 15 December 2015 to 17 January 2016 and in summer fr...Volatile organic compounds(VOCs)are major contributors to air pollution.Based on the emission characteristics of 99 VOCs that daily measured at 10 am in winter from 15 December 2015 to 17 January 2016 and in summer from 21 July to 25 August 2016 in Beijing,the environmental impact and health risk of VOC were assessed.In the winter polluted days,the secondary organic aerosol formation potential(SOAP)of VOC(199.70±15.05 mg/m^3)was significantly higher than that on other days.And aromatics were the primary contributor(98.03%)to the SOAP during the observation period.Additionally,the result of the ozone formation potential(OFP)showed that ethylene contributed the most to OFP in winter(26.00%and 27.64%on the normal and polluted days).In summer,however,acetaldehyde was the primary contributor to OFP(22.00%and 21.61%on the normal and polluted days).Simultaneously,study showed that hazard ratios and lifetime cancer risk values of acrolein,chloroform,benzene,1,2-dichloroethane,acetaldehyde and 1,3-butadiene exceeded the thresholds established by USEPA,thereby presenting a health risk to the residents.Besides,the ratio of toluene-to-benzene indicated that vehicle exhausts were the main source of VOC pollution in Beijing.The ratio of m-/p-xylene-toethylbenzene demonstrated that there were more prominent atmospheric photochemical reactions in summer than that in winter.Finally,according to the potential source contribution function(PSCF)results,compared with local pollution sources,the spread of pollution from long-distance VOCs had a greater impact on Beijing.展开更多
基金supported by the National Natural Science Foundation of China(No.52079133)CRSRI Open Research Program(Program SN:CKWV2019746/KY)+1 种基金the project of Key Laboratory of Water Grid Project and Regulation of Ministry of Water Resources(QTKS0034W23291)the Youth Innovation Promotion Association CAS.
文摘Flexible damping technology considering aseismic materials and aseismic structures seems be a good solution for engineering structures.In this study,a constrained damping structure for underground tunnel lining,using a rubber-sand-concrete(RSC)as the aseismic material,is proposed.The aseismic performances of constrained damping structure were investigated by a series of hammer impact tests.The damping layer thickness and shape effects on the aseismic performance such as effective duration and acceleration amplitude of time-domain analysis,composite loss factor and damping ratio of the transfer function analysis,and total vibration level of octave spectrum analysis were discussed.The hammer impact tests revealed that the relationship between the aseismic performance and damping layer thickness was not linear,and that the hollow damping layer had a better aseismic performance than the flat damping layer one.The aseismic performances of constrained damping structure under different seismicity magnitudes and geological conditions were investigated.The effects of the peak ground acceleration(PGA)and tunnel overburden depth on the aseismic performances such as the maximum principal stress and equivalent plastic strain(PEEQ)were discussed.The numerical results show the constrained damping structure proposed in this paper has a good aseismic performance,with PGA in the range(0.2-1.2)g and tunnel overburden depth in the range of 0-300 m.
基金supported by the National Natural Science Foundation Project of China(Nos.72088101 and 42177164)the Distinguished Youth Science Foundation of Hunan Province of China(No.2022JJ10073)The first author was funded by China Scholarship Council(No.202106370038).
文摘Hard rock pillar is one of the important structures in engineering design and excavation in underground mines.Accurate and convenient prediction of pillar stability is of great significance for underground space safety.This paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar stability.An integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM models.Five parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input parameters.Two global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’performance.The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices.Nevertheless,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published works.The validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.
基金the National Natural Science Foundation of China (Grant No. 21701144)the China Postdoctoral Science Foundation (Grant Nos. 2016M592303 and 2017T100536)
文摘Constructing unique and highly stable structures with plenty of electroactive sites in sodium storage materials is a key factor for achieving improved electrochemical properties through favorable sodium ion di usion kinetics. An SnS_2@carbon hollow nanospheres(SnS_2@C) has been designed and fabricated via a facile solvothermal route, followed by an annealing treatment. The SnS_2@C hybrid possesses an ideal hollow structure, rich active sites, a large electrode/electrolyte interface, a shortened ion transport pathway, and, importantly, a bu er space for volume change, generated from the repeated insertion/extraction of sodium ions. These merits lead to the significant reinforcement of structural integrity during electrochemical reactions and the improvement in sodium storage properties, with a high specific reversible capacity of 626.8 mAh g^(-1) after 200 cycles at a current density of 0.2 A g^(-1) and superior high-rate performance(304.4 mAh g^(-1) at 5 A g^(-1)).
文摘A novel method of measuring the positive-sequence capacitance of T-connection transmission lines is proposed. The mathematical model of the new method is explained in detail. In order to obtain enough independent equations, three independent operation modes of T-connection transmission lines during the line measurement are introduced. The digital simulation results and field measurement results are shown. The simulation and measurement results have validated that the new method can meet the needs of measuring the positive-sequence capacitance of T-connection transmission lines. This method has been implemented in the newly developed measurement instrument.
文摘A live line measurement method for the zero sequence parameters of transmission lines with mutual inductance is introduced. The mathematical models of the measurement method are given. Global Positioning System (GPS) is used as the synchronous signal for the measurement carried out at different substations simultaneously. The measurement system and digital simulation results are given. Finally, the live line measurement results of two 220 kV transmission lines with mutual inductance in Hainangrid are given. Results from both simulation and on-site measurement show that the live line measurement method is feasible, and its measurement accuracy can satisfactorily meet the requirements of engineering measurement.
基金funded by the National Science Foundation of China(42177164)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073)the Innovation-Driven Project of Central South University(2020CX040).
文摘After the excavation of the roadway,the original stress balance is destroyed,resulting in the redistribution of stress and the formation of an excavation damaged zone(EDZ)around the roadway.The thickness of EDZ is the key basis for roadway stability discrimination and support structure design,and it is of great engineering significance to accurately predict the thickness of EDZ.Considering the advantages of machine learning(ML)in dealing with high-dimensional,nonlinear problems,a hybrid prediction model based on the random forest(RF)algorithm is developed in this paper.The model used the dragonfly algorithm(DA)to optimize two hyperparameters in RF,namely mtry and ntree,and used mean absolute error(MAE),rootmean square error(RMSE),determination coefficient(R^(2)),and variance accounted for(VAF)to evaluatemodel prediction performance.A database containing 217 sets of data was collected,with embedding depth(ED),drift span(DS),surrounding rock mass strength(RMS),joint index(JI)as input variables,and the excavation damaged zone thickness(EDZT)as output variable.In addition,four classic models,back propagation neural network(BPNN),extreme learning machine(ELM),radial basis function network(RBF),and RF were compared with the DA-RF model.The results showed that the DARF mold had the best prediction performance(training set:MAE=0.1036,RMSE=0.1514,R^(2)=0.9577,VAF=94.2645;test set:MAE=0.1115,RMSE=0.1417,R^(2)=0.9423,VAF=94.0836).The results of the sensitivity analysis showed that the relative importance of each input variable was DS,ED,RMS,and JI from low to high.
基金This research is partially supported by the National Natural Science Foundation Project of China(Grant No.42177164)the Outstanding Youth Project of Hunan Provincial Department of Education(Grant No.23B0008)the Distinguished Youth Science Foundation of Hunan Province of China(2022JJ10073).
文摘This study utilizes a semantic-level computer vision-based detection to characterize fracture traces of hard rock pillars in underground space.The trace images captured by photogrammetry are used to establish the database for training two convolutional neural network(CNN)-based models,i.e.,U-Net(University of Freiburg,Germany)and DeepLabV3+(Google,USA)models.Chain code technology,polyline approximation algorithm,and the circular window scanning approach are combined to quantify the main characteristics of fracture traces on flat and uneven surfaces,including trace length,dip angle,density,and intensity.The extraction results indicate that the CNN-based models have better performances than the edge detection methods-based Canny and Sobel operators for extracting the trace and reducing noise,especially the DeepLabV3+model.Furthermore,the quantization results further prove the reliability of extracting the fracture trace.As a result,a case study with two types of traces(i.e.,on flat and uneven surfaces)demonstrates that the applied semantic-level computer vision detection is an accurate and efficient approach for characterizing the fracture trace of hard rock pillars.
基金funded by the National Science Foundation of China(41807259)the Innovation-Driven Project of Central South University(No.2020CX040)the Shenghua Lieying Program of Central South University(Principle Investigator:Dr.Jian Zhou)。
文摘A reliable and accurate prediction of the tunnel boring machine(TBM)performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB)which are optimized by gray wolf optimization(GWO),particle swarm optimization(PSO),social spider optimization(SSO),sine cosine algorithm(SCA),multi verse optimization(MVO)and moth flame optimization(MFO),for estimation of the TBM penetration rate(PR).To do this,a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation,the rock mass rating,Brazilian tensile strength(BTS),rock mass weathering,the uniaxial compressive strength(UCS),revolution per minute and trust force per cutter(TFC),were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models,four single models i.e.,artificial neural network,random forest regression,XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then,their performance capacities were assessed through the use of root mean square error,coefficient of determination,mean absolute percentage error,and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453,and 0.1325),R^(2) of(0.951,and 0.951),mean absolute percentage error(4.0689,and 3.8115),and a10-index of(0.9348,and 0.9496)in training and testing phases,respectively.The developed hybrid PSO-XGB can be introduced as an accurate,powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis,it was found that UCS,BTS and TFC have the deepest impacts on the TBM PR.
基金financialy supported by National Natural Science Foundation of China (22002142)China Postdoctoral Science Foundation (2019M652570, 2019M650172 and 2020T130605)+1 种基金Support Plan for College Science and Technology Innovation Team of Henan Province (No. 16IRTSTHN001)the Science & Technology Innovation Talent Plan of Henan Province (No. 174200510018)
文摘Recently,defect architectured photocatalysis is proved to be the most versatile choice for high solar to chemical energy conversion processes.Defect engineering strategies are of great demand to effectively tune the electronic microstructure and surface morphologies of semiconductors to boost charge carrier concentration and extend light harvesting capability.This review provides a comprehensive insight to various kinds of defects along with their synthesis procedures and controlling mechanism to uplift photocatalytic activity.In addition,the contribution made by defects and material optimization techniques toward electronic band structure of the photocatalyst,the optimal concentration of defects,the key adsorption processes,charge distribution,and transfer dynamics have been explained in detail.Further,to clarify the relationship between photocatalytic activity and defect states in real,a comprehensive outlook to the versatile photocatalytic applications has been presented to highlight current challenges and future applications.Defect engineering therefore stands as the next step toward advancement in the design and configuration of modern photocatalysts for high efficiency photocatalysis.
基金Shenzhen-Hong Kong-Macao Science and Technology Plan Project,Grant/Award Number:SGDX2020110309260000Research Grants Council(RGC)Collaborative Research Fund,Grant/Award Number:C5110-20GF+2 种基金Research Grants Council(RGC)General Research Fund,Grant/Award Numbers:PolyU 15214619,PolyU 15210818Hong Kong Polytechnic University Internal Fund,Grant/Award Numbers:1-ZE1E,1-ZVVQNational Natural Science Foundation of China,Grant/Award Number:31771077。
文摘The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)pandemic has posed significant challenges in early viral diagnosis.Hence,it is urgently desirable to develop a rapid,inexpensive,and sensitive method to aid point-of-care SARS-CoV-2 detection.In this work,we report a highly sequence-specific biosensor based on nanocomposites with aggregationinduced emission luminogens(AIEgen)-labeled oligonucleotide probes on graphene oxide nanosheets(AIEgen@GO)for one step-detection of SARS-CoV-2-specific nucleic acid sequences(Orf1ab or N genes).A dual“turn-on”mechanism based on AIEgen@GO was established for viral nucleic acids detection.Here,the first-stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid,and the second-stage enhancement of AIEbased fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen.Furthermore,the feasibility of our platform for diagnostic application was demonstrated by detecting SARS-CoV-2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification.Our platform shows great promise in assisting the initial rapid detection of the SARS-CoV-2 nucleic acid sequence before utilizing quantitative reverse transcription-polymerase chain reaction for second confirmation.
基金supported by the National Key R&D Program of China(No.2016YFC0202500)the National Natural Science Foundation of China(Nos.21677163 and 21876193)+1 种基金the Chengdu Science and Technology Project(No.2018-ZM01-00019-SN)the Youth Innovation Promotion Association CAS。
文摘Volatile organic compounds(VOCs)are major contributors to air pollution.Based on the emission characteristics of 99 VOCs that daily measured at 10 am in winter from 15 December 2015 to 17 January 2016 and in summer from 21 July to 25 August 2016 in Beijing,the environmental impact and health risk of VOC were assessed.In the winter polluted days,the secondary organic aerosol formation potential(SOAP)of VOC(199.70±15.05 mg/m^3)was significantly higher than that on other days.And aromatics were the primary contributor(98.03%)to the SOAP during the observation period.Additionally,the result of the ozone formation potential(OFP)showed that ethylene contributed the most to OFP in winter(26.00%and 27.64%on the normal and polluted days).In summer,however,acetaldehyde was the primary contributor to OFP(22.00%and 21.61%on the normal and polluted days).Simultaneously,study showed that hazard ratios and lifetime cancer risk values of acrolein,chloroform,benzene,1,2-dichloroethane,acetaldehyde and 1,3-butadiene exceeded the thresholds established by USEPA,thereby presenting a health risk to the residents.Besides,the ratio of toluene-to-benzene indicated that vehicle exhausts were the main source of VOC pollution in Beijing.The ratio of m-/p-xylene-toethylbenzene demonstrated that there were more prominent atmospheric photochemical reactions in summer than that in winter.Finally,according to the potential source contribution function(PSCF)results,compared with local pollution sources,the spread of pollution from long-distance VOCs had a greater impact on Beijing.
