Lowering the synthesis temperature of boron nitride nanotubes(BNNTs)is crucial for their development.The primary reason for adopting a high temperature is to enable the effective activation of highmelting-point solid ...Lowering the synthesis temperature of boron nitride nanotubes(BNNTs)is crucial for their development.The primary reason for adopting a high temperature is to enable the effective activation of highmelting-point solid boron.In this study,we developed a novel approach for efficiently activating boron by introducing alkali metal compounds into the conventional MgO–B system.This approach can be adopted to form various low-melting-point AM–Mg–B–O growth systems.These growth systems have improved catalytic capability and reactivity even under low-temperature conditions,facilitating the synthesis of BNNTs at temperatures as low as 850℃.In addition,molecular dynamics simulations based on density functional theory theoretically demonstrate that the systems maintain a liquid state at low temperatures and interact with N atoms to form BN chains.These findings offer novel insights into the design of boron activation and are expected to facilitate research on the low-temperature synthesis of BNNTs.展开更多
The rapid development of communication technology and high-frequency electronic devices has created a need for more advanced electromagnetic interference(EMI)shielding materials.In response to this demand,a study has ...The rapid development of communication technology and high-frequency electronic devices has created a need for more advanced electromagnetic interference(EMI)shielding materials.In response to this demand,a study has been conducted to develop multifunctional carbon nanofibers(CNFs)/polyaniline(PANI)aerogels with excellent electromagnetic interference shielding,flame retardancy,and thermal insulation performance.The process involved freeze-drying of electrospun CNFs and PANI nanoparticles followed by in situ growth PANI to coat the CNFs,creating the core-shell structured CNFs/PANI composite fiber and its hybrid aerogels(CP-3@PANI).The interaction between PANI and aniline(ANI)provides attachment sites,allowing additional ANI adsorption into the aerogel for in situ polymerization.This results in PANI uniformly covering the surface of the CNFs,creating a core-shell composite fiber with a flexible CNF core and PANI shell.This process enhances the utilization rate of the ANI monomer and increases the PANI content loaded onto the aerogel.Additionally,effective connections are established between the CNFs,forming a stable,conductive three-dimensional network structure.The prepared CP-3@PANI aerogels exhibit excellent EMI shielding efficiency(SE)of 85.4 dB and specific EMI SE(SE d^(-1))of 791.2 dB cm^(3)g^(-1)in the X-band.Due to the synergistic flame-retardant effect of CNFs,PANI,and the dopant(phytic acid),the CP-3@PANI aerogels demonstrate outstanding flame-retardant and thermal insulation properties,with a peak heat release rate(PHRR)as low as 7.8 W g^(-1)and a total heat release of only 0.58 kJ g^(-1).This study provides an effective strategy for preparing multifunctional integrated EMI shielding materials.展开更多
Mechanical excavation,blasting,adjacent rockburst and fracture slip that occur during mining excavation impose dynamic loads on the rock mass,leading to further fracture of damaged surrounding rock in three-dimensiona...Mechanical excavation,blasting,adjacent rockburst and fracture slip that occur during mining excavation impose dynamic loads on the rock mass,leading to further fracture of damaged surrounding rock in three-dimensional high-stress and even causing disasters.Therefore,a novel complex true triaxial static-dynamic combined loading method reflecting underground excavation damage and then frequent intermittent disturbance failure is proposed.True triaxial static compression and intermittent disturbance tests are carried out on monzogabbro.The effects of intermediate principal stress and amplitude on the strength characteristics,deformation characteristics,failure characteristics,and precursors of monzogabbro are analyzed,intermediate principal stress and amplitude increase monzogabbro strength and tensile fracture mechanism.Rapid increases in microseismic parameters during rock loading can be precursors for intermittent rock disturbance.Based on the experimental result,the new damage fractional elements and method with considering crack initiation stress and crack unstable stress as initiation and acceleration condition of intermittent disturbance irreversible deformation are proposed.A novel three-dimensional disturbance fractional deterioration model considering the intermediate principal stress effect and intermittent disturbance damage effect is established,and the model predicted results align well with the experimental results.The sensitivity of stress states and model parameters is further explored,and the intermittent disturbance behaviors at different f are predicted.This study provides valuable theoretical bases for the stability analysis of deep mining engineering under dynamic loads.展开更多
Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing researc...Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems.展开更多
Electrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation.To get the most energy storage out of the battery at low temperatures,improvements ...Electrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation.To get the most energy storage out of the battery at low temperatures,improvements in electrolyte chemistry need to be coupled with optimized electrode materials and tailored electrolyte/electrode interphases.Herein,this review critically outlines electrolytes’limiting factors,including reduced ionic conductivity,large de-solvation energy,sluggish charge transfer,and slow Li-ion transportation across the electrolyte/electrode interphases,which affect the low-temperature performance of Li-metal batteries.Detailed theoretical derivations that explain the explicit influence of temperature on battery performance are presented to deepen understanding.Emerging improvement strategies from the aspects of electrolyte design and electrolyte/electrode interphase engineering are summarized and rigorously compared.Perspectives on future research are proposed to guide the ongoing exploration for better low-temperature Li-metal batteries.展开更多
Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the i...Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.展开更多
In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the ...In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.展开更多
Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sp...Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sparking significant advancements in electronic devices that utilize 2D TMDs.Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance.This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor.It delves into the impacts of miniaturization,including the reduction of channel length,gate length,source/drain contact length,and dielectric thickness on transistor operation and performance.In addition,this review provides a detailed analysis of performance parameters such as source/drain contact resistance,subthreshold swing,hysteresis loop,carrier mobility,on/off ratio,and the development of p-type and single logic transistors.This review details the two logical expressions of the single 2D-TMD logic transistor,including current and voltage.It also emphasizes the role of 2D TMD-based transistors as memory devices,focusing on enhancing memory operation speed,endurance,data retention,and extinction ratio,as well as reducing energy consumption in memory devices functioning as artificial synapses.This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications.It underscores the anticipated challenges,opportunities,and potential solutions in navigating the dimension and performance boundaries of 2D transistors.展开更多
To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studie...To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.展开更多
基金supported by the National Natural Science Foundation of China(No.51972162)the Fundamental Research Funds for the Central Universities(No.2024300440).
文摘Lowering the synthesis temperature of boron nitride nanotubes(BNNTs)is crucial for their development.The primary reason for adopting a high temperature is to enable the effective activation of highmelting-point solid boron.In this study,we developed a novel approach for efficiently activating boron by introducing alkali metal compounds into the conventional MgO–B system.This approach can be adopted to form various low-melting-point AM–Mg–B–O growth systems.These growth systems have improved catalytic capability and reactivity even under low-temperature conditions,facilitating the synthesis of BNNTs at temperatures as low as 850℃.In addition,molecular dynamics simulations based on density functional theory theoretically demonstrate that the systems maintain a liquid state at low temperatures and interact with N atoms to form BN chains.These findings offer novel insights into the design of boron activation and are expected to facilitate research on the low-temperature synthesis of BNNTs.
基金the financial support from the Shenzhen Biodegradable Polymer Materials and Materials Genetic Evaluation Research Project Team (JCYJ20220818100217037)Science and Technology Program of Shenzhen (JSGG20200924171000001)+4 种基金the Key-Area Research and Development Program of Guangdong Province (2019B010941001)the Guangdong Provincial Key Laboratory of Energy Materials for Electric Power(2018B030322001)the National Key Research and Development Program of China (2018YFB0704100)Joint Laboratory of Radiation Protection and Material Genetic Engineering Applications in Nuclear Facilitiessupported by the Pico Center at SUSTech CRF which receives support from the Presidential Fund and Development and Reform Commission of Shenzhen Municipality。
文摘The rapid development of communication technology and high-frequency electronic devices has created a need for more advanced electromagnetic interference(EMI)shielding materials.In response to this demand,a study has been conducted to develop multifunctional carbon nanofibers(CNFs)/polyaniline(PANI)aerogels with excellent electromagnetic interference shielding,flame retardancy,and thermal insulation performance.The process involved freeze-drying of electrospun CNFs and PANI nanoparticles followed by in situ growth PANI to coat the CNFs,creating the core-shell structured CNFs/PANI composite fiber and its hybrid aerogels(CP-3@PANI).The interaction between PANI and aniline(ANI)provides attachment sites,allowing additional ANI adsorption into the aerogel for in situ polymerization.This results in PANI uniformly covering the surface of the CNFs,creating a core-shell composite fiber with a flexible CNF core and PANI shell.This process enhances the utilization rate of the ANI monomer and increases the PANI content loaded onto the aerogel.Additionally,effective connections are established between the CNFs,forming a stable,conductive three-dimensional network structure.The prepared CP-3@PANI aerogels exhibit excellent EMI shielding efficiency(SE)of 85.4 dB and specific EMI SE(SE d^(-1))of 791.2 dB cm^(3)g^(-1)in the X-band.Due to the synergistic flame-retardant effect of CNFs,PANI,and the dopant(phytic acid),the CP-3@PANI aerogels demonstrate outstanding flame-retardant and thermal insulation properties,with a peak heat release rate(PHRR)as low as 7.8 W g^(-1)and a total heat release of only 0.58 kJ g^(-1).This study provides an effective strategy for preparing multifunctional integrated EMI shielding materials.
基金the financial support from the National Natural Science Foundation of China(No.52109119)the Guangxi Natural Science Foundation(No.2021GXNSFBA075030)+2 种基金the Guangxi Science and Technology Project(No.Guike AD20325002)the Chinese Postdoctoral Science Fund Project(No.2022 M723408)the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin(China Institute of Water Resources and Hydropower Research)(No.IWHR-SKL-202202).
文摘Mechanical excavation,blasting,adjacent rockburst and fracture slip that occur during mining excavation impose dynamic loads on the rock mass,leading to further fracture of damaged surrounding rock in three-dimensional high-stress and even causing disasters.Therefore,a novel complex true triaxial static-dynamic combined loading method reflecting underground excavation damage and then frequent intermittent disturbance failure is proposed.True triaxial static compression and intermittent disturbance tests are carried out on monzogabbro.The effects of intermediate principal stress and amplitude on the strength characteristics,deformation characteristics,failure characteristics,and precursors of monzogabbro are analyzed,intermediate principal stress and amplitude increase monzogabbro strength and tensile fracture mechanism.Rapid increases in microseismic parameters during rock loading can be precursors for intermittent rock disturbance.Based on the experimental result,the new damage fractional elements and method with considering crack initiation stress and crack unstable stress as initiation and acceleration condition of intermittent disturbance irreversible deformation are proposed.A novel three-dimensional disturbance fractional deterioration model considering the intermediate principal stress effect and intermittent disturbance damage effect is established,and the model predicted results align well with the experimental results.The sensitivity of stress states and model parameters is further explored,and the intermittent disturbance behaviors at different f are predicted.This study provides valuable theoretical bases for the stability analysis of deep mining engineering under dynamic loads.
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSNthe Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002111 Project under Grant B08028.
文摘Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm II(NSGA-II),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method improves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target production optimization problems.
基金The work described in this paper was fully supported by a Grant from the City University of Hong Kong(Project No.9610641).
文摘Electrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation.To get the most energy storage out of the battery at low temperatures,improvements in electrolyte chemistry need to be coupled with optimized electrode materials and tailored electrolyte/electrode interphases.Herein,this review critically outlines electrolytes’limiting factors,including reduced ionic conductivity,large de-solvation energy,sluggish charge transfer,and slow Li-ion transportation across the electrolyte/electrode interphases,which affect the low-temperature performance of Li-metal batteries.Detailed theoretical derivations that explain the explicit influence of temperature on battery performance are presented to deepen understanding.Emerging improvement strategies from the aspects of electrolyte design and electrolyte/electrode interphase engineering are summarized and rigorously compared.Perspectives on future research are proposed to guide the ongoing exploration for better low-temperature Li-metal batteries.
基金funded by the Natural Science Foundation of Shandong Province (ZR2021MD061ZR2023QD025)+3 种基金China Postdoctoral Science Foundation (2022M721972)National Natural Science Foundation of China (41174098)Young Talents Foundation of Inner Mongolia University (10000-23112101/055)Qingdao Postdoctoral Science Foundation (QDBSH20230102094)。
文摘Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs.
基金supported by the National Natural Science Foundation of China under Grant 52325402, 52274057, 52074340 and 51874335the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028。
文摘In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.
基金supported by the National Key R&D Plan of China(Grant 2021YFB3600703)the National Natural Science Foundation(Grant 62204137)of China for Youth,the Open Research Fund Program of Beijing National Research Centre for Information Science and Technology(BR2023KF02009)+1 种基金the National Natural Science Foundation of china(U20A20168,61874065,and 51861145202)the Research Fund from Tsinghua University Initiative Scientific Research Program,the Center for Flexible Electronics Technology of Tsinghua University,and a grant from the Guoqiang Institute,Tsinghua University.
文摘Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sparking significant advancements in electronic devices that utilize 2D TMDs.Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance.This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor.It delves into the impacts of miniaturization,including the reduction of channel length,gate length,source/drain contact length,and dielectric thickness on transistor operation and performance.In addition,this review provides a detailed analysis of performance parameters such as source/drain contact resistance,subthreshold swing,hysteresis loop,carrier mobility,on/off ratio,and the development of p-type and single logic transistors.This review details the two logical expressions of the single 2D-TMD logic transistor,including current and voltage.It also emphasizes the role of 2D TMD-based transistors as memory devices,focusing on enhancing memory operation speed,endurance,data retention,and extinction ratio,as well as reducing energy consumption in memory devices functioning as artificial synapses.This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications.It underscores the anticipated challenges,opportunities,and potential solutions in navigating the dimension and performance boundaries of 2D transistors.
基金This work is supported by the National Natural Science Foundation of China under Grant 52274057,52074340 and 51874335the Major Scientific and Technological Projects of CNPC under Grant ZD2019-183-008+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSNthe Science and Technology Support Plan for Youth Innovation of University in Shandong Province under Grant 2019KJH002111 Project under Grant B08028.
文摘To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.
