Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To sp...Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.展开更多
Laser micro-nano processing technologies have been developed to address challenges that are otherwise difficult to solve in industrial applications and diverse scientific fields.These technologies offer designable pat...Laser micro-nano processing technologies have been developed to address challenges that are otherwise difficult to solve in industrial applications and diverse scientific fields.These technologies offer designable patterning,arraying capabilities,three-dimensional(3D)processing,and high precision.Recent advancements in laser technologies have demonstrated their effectiveness as powerful tools for micro-nano processing of optoelectronic materials.By utilizing various laser techniques—such as laser-induced polymerization,laser ablation,laser-induced transfer,laser-directed assembly,and laser-assisted crystallization—broad applications in image sensors,displays,solar cells,lasers,anti-counterfeiting,and information encryption have been enabled.This review comprehensively summarizes recent progress in the laser micro-nano processing of optoelectronic materials,including the technologies used for preparation,patterning,arraying,and modification.These laser fabrication methods uniquely provide capabilities such as annealing,phase transitions,and ion exchange in optoelectronic materials.We also discuss the perspectives and challenges for future developments,including the advantages,disadvantages,and potential applications of different laser micro-nano processing technologies.With the rapid advancements in laser micro-nanofabrication,we foresee significant growth in advanced,high-performance optoelectronic applications.This review aims to provide researchers with insights into the current state and future prospects of laser-based micro-nano processing,encouraging further exploration and innovation in this promising field.展开更多
Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and w...Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.展开更多
Relaxation processes in quantum systems coupled to external environments represent one of the most fundamental nonequilibrium phenomena in condensed matter physics.The Lindblad master equation provides a powerful fram...Relaxation processes in quantum systems coupled to external environments represent one of the most fundamental nonequilibrium phenomena in condensed matter physics.The Lindblad master equation provides a powerful framework for characterizing such open quantum dynamics.In this work,we systematically investigate how different types of quantum jump operators and system geometries influence the Liouvillian gap and the properties of the nonequilibrium steady state(NESS)in finite-size systems.We demonstrate that,due to the intricate structure of the Liouvillian superoperator,multiple NESSs with unphysical characteristics can emerge.The physically meaningful steady state must instead be understood as a superposition of these NESSs that collectively satisfy the required physical constraints.Furthermore,we find that the Liouvillian gap does not necessarily increase monotonically with the system-environment coupling strength.Instead,it can exhibit a nontrivial peak structure,corresponding to a minimum in the relaxation time.The magnitude of this peak is closely related to the symmetry properties of the system.Our results provide a deeper understanding of nonequilibrium behavior in finite quantum systems and offer new insights into the design and control of open quantum dynamics.展开更多
Integrated land and resource planning is critical for achieving global sustainability goals,yet a persistent chasm separates policy ambition from on-the-ground outcomes.The review article undertakes a comparative eval...Integrated land and resource planning is critical for achieving global sustainability goals,yet a persistent chasm separates policy ambition from on-the-ground outcomes.The review article undertakes a comparative evaluation across the world to diagnose the systemic gaps of the policy that is leading to this implementation failure.We come up with a general typology of 5 categories of gaps that are interconnected:spatial-temporal mismatches,institutional fragmentation,the knowledge-action divide,lack of equity and justice,and broken monitoring and feedback loops.In a comparative study of the High-Income Countries,Rapidly Developing Economies,and Low-Income Countries,we show how these universal gaps are reflected in specific contextual syndromes,which are defined by the political economy,state capacity,and global integration.As can be seen in the analysis,these failures are not stand-alone but exist in a vicious,self-perpetuating cycle that is based on power asymmetries,institutional path dependency,and scale mismatches.In order to break this cycle,we suggest a revolutionary structure of action,which is structured around integration,adaptive management,and justice.The framework identifies the specific operation strategies,such as developing meta-governance formations and establishing community tenure to implement participatory monitoring,and aligning a multi-scale agenda.We infer that the implementation gap must be bridged by going beyond technical solutions to ensure a virtuous circle of legitimate learning-oriented governance that can address the complexity of socio-ecological conditions of the Anthropocene.展开更多
The rise of AI speech synthesis,while achieving impressive naturalness,has revealed a profound educational challenge:its failure to convey complex human emotions and contextual nuance-termed the“affective gap”-threa...The rise of AI speech synthesis,while achieving impressive naturalness,has revealed a profound educational challenge:its failure to convey complex human emotions and contextual nuance-termed the“affective gap”-threatens to undermine the ecology of voice artistry and societal aesthetic discernment.This paper first diagnoses this gap by examining its key manifestations(compound emotion flattening,contextual deafness,the prosodic uncanny valley)and tracing its root cause to the epistemological divide between AI’s data-driven pattern recognition and human embodied experience.It then analyzes the consequent structural disruption to the voice-acting industry’s traditional“pyramid”training model and the broader risk of cultural aesthetic deskilling.In response,the paper’s central contribution is to propose a novel pedagogical framework designed to bridge this gap.This framework advocates a decisive shift in voice education from skill transmission towards critical voice artistry,centered on cultivating students’capacities for deep textual/contextual analysis,empathetic and embodied sensemaking,and the critical evaluation and direction of AI-generated speech.The paper argues that by integrating this critical pedagogical approach with strategic technology use,educators can empower future artists to navigate and shape a hybrid human-AI creative landscape.Ultimately,this work provides a theoretically grounded and actionable roadmap for innovating performing arts education in the AI era,positioning educational technology as vital steward of uniquely human expressive intelligence.展开更多
The structures of even-even Gd and Dy isotopes around N=100 were investigated using a fully self-consistent microscopic model.The systematics of the exited 2_(1)^(+)and 4_(1)^(+)energies reveal a peak-like structure a...The structures of even-even Gd and Dy isotopes around N=100 were investigated using a fully self-consistent microscopic model.The systematics of the exited 2_(1)^(+)and 4_(1)^(+)energies reveal a peak-like structure at N=100 along the Gd(Z=64)and Dy(Z=66)isotopic chains.This supports the evidence for a subshell gap near N=100.The nuclear structure properties studied are important to understand the r-process elemental abundance peak at A~160.展开更多
Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that ad...Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.展开更多
As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency...As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency of exciton utilization and the overall performance of organic light-emit-ting devices are closely linked to the singlet-triplet energy gap(ΔE_(ST))of MR-TADF emitters.Identifying an economic and accu-rate theoretical approach to predictΔE_(ST)would be beneficial for high-throughput screening and facilitate the inverse design of MR-TADF molecules.In this study,we evaluated the S_(1)state energy(E(S_(1))),T_(1)state ener-gy(E(T_(1))),andΔE_(ST)using three different physical interpretations:adiabatic excitation ener-gy,vertical absorption energy,and vertical emission energy.We employed the time-depen-dent density functional theory(TDDFT)and delta self-consistent field(ΔSCF)methods to calculate E(S_(1)),E(T_(1)),andΔE_(ST)for 20 MR-TADF molecules reported in the literature.We compared these calculated values with experimental data obtained from fluorescence spec-troscopy at room-temperature(or 77 K)and phosphorescence spectroscopy conducted at 77 K.Our findings indicate that the vertical absorption energy at the S0 state minimum,deter-mined by theΔSCF method,accurately predicts the S_(1)state energy.Similarly,the vertical absorption energy at the S0 state minimum,calculated using the TDDFT method,effectively predicts the T_(1)state energy.TheΔE_(ST)derived from the difference between these two excita-tion energies exhibited the smallest mean absolute error of only 0.039 eV compared to the ex-perimental values.This combination represents the most accurate and cost-effective method reported to date for predicting theΔE_(ST)of MR-TADF molecules,and can be integrated into AI-driven inverse design workflows for new emitters.展开更多
The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges be...The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.展开更多
Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and ...Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and unstable,making high-quality single-crystal growth,characterization,and measurements difficult,and most do not exhibit superconductivity at ambient pressure.In contrast,La_(3) In stands out for its ambient-pressure superconductivity(T_(C)∼9.4 K)and the availability of high-quality single crystals.Here,we investigate its low-energy electronic structure using angle-resolved photoemission spectroscopy and first-principles calculations.The bands near the Fermi energy(E_(F))are mainly derived from La 5d and In 5p orbitals.A saddle point is directly observed at the Brillouin zone(BZ)boundary,while a three-dimensional Van Hove singularity crosses E_(F) at the BZ corner.First-principles calculations further reveal topological Dirac surface states within the bulk energy gap above E_(F).The coexistence of a high density of states and in-gap topological surface states near𝐸F suggests that La3In offers a promising platform for tuning superconductivity and exploring possible topological superconducting phases through doping or external pressure.展开更多
文摘Titanium-based semiconductors are known for their high chemical stability and suitable band gap widths.However,the conventional experimental screening methods are inefficient due to the wide variety of materials.To speed up the selection process,this work focuses on interpretable feature learning and band gap prediction for titanium-based semiconductors.First,titanium compounds were selected from the Materials Project database by machine learning,and elemental features were extracted using the Magpie descriptors.Then,principal component analysis(PCA)was applied to reduce the data dimensionality,creating a representative dataset.Meantime,heatmaps and SHAP(SHapley Additive exPlanations)methods were used to demonstrate the influence of key features such as electronegativity,covalent radius,period number,and unit cell volume on the bandgap,understanding the relationship between the material’s properties and performance.After comparing different machine learning models,including Random Forest(RF),Support Vector Machines(SVM),Linear Regression(LR),and Gradient Boosting Regression(GBR),the RF was found to be the most accurate for band gap prediction.Finally,the model performance was improved through parameter tuning,showing high accuracy.These findings provide strong data support and design guidance for the development of materials in fields like photocatalysis and solar cells.
基金supported by the National Key Research and Development Program of ChinaNational Natural Science Foundation of China(NSFC)Jilin Province Science and Technology Development Plan Project under Grants 2020YFA0715000,62075081,and 20220402011GH。
文摘Laser micro-nano processing technologies have been developed to address challenges that are otherwise difficult to solve in industrial applications and diverse scientific fields.These technologies offer designable patterning,arraying capabilities,three-dimensional(3D)processing,and high precision.Recent advancements in laser technologies have demonstrated their effectiveness as powerful tools for micro-nano processing of optoelectronic materials.By utilizing various laser techniques—such as laser-induced polymerization,laser ablation,laser-induced transfer,laser-directed assembly,and laser-assisted crystallization—broad applications in image sensors,displays,solar cells,lasers,anti-counterfeiting,and information encryption have been enabled.This review comprehensively summarizes recent progress in the laser micro-nano processing of optoelectronic materials,including the technologies used for preparation,patterning,arraying,and modification.These laser fabrication methods uniquely provide capabilities such as annealing,phase transitions,and ion exchange in optoelectronic materials.We also discuss the perspectives and challenges for future developments,including the advantages,disadvantages,and potential applications of different laser micro-nano processing technologies.With the rapid advancements in laser micro-nanofabrication,we foresee significant growth in advanced,high-performance optoelectronic applications.This review aims to provide researchers with insights into the current state and future prospects of laser-based micro-nano processing,encouraging further exploration and innovation in this promising field.
基金supported in part by National Natural Science Foundation of China under Grant 72401055in part by National Natural Science Foundation of China under Grant 52277083in part by the joint founding of Guangdong,and Dongguan under Grant 2023A1515110939.
文摘Existing load forecasting methods typically assume that recent load data are available for prediction.This is not in conformity with reality since there is a time gap between the flow date(when power is consumed)and when measurement values are obtained.To this end,this letter proposes an online learning-based probabilistic load forecasting method considering the impact of the data gap.Specifically,an adaptive ensemble backpropagation-enabled online quantile regression algorithm is developed to optimize the parameters of the attention network recursively using the newly obtained load observations.To further improve the reliability and sharpness of prediction intervals under significant data gaps,we introduce an online interval calibration technique.The proposed online learning method allows us to adaptively capture the dynamic changes in load patterns and alleviate the information lags caused by data gaps.Comparative tests utilizing real-world datasets reveal the superiority of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant Nos.12275193 and11975166)。
文摘Relaxation processes in quantum systems coupled to external environments represent one of the most fundamental nonequilibrium phenomena in condensed matter physics.The Lindblad master equation provides a powerful framework for characterizing such open quantum dynamics.In this work,we systematically investigate how different types of quantum jump operators and system geometries influence the Liouvillian gap and the properties of the nonequilibrium steady state(NESS)in finite-size systems.We demonstrate that,due to the intricate structure of the Liouvillian superoperator,multiple NESSs with unphysical characteristics can emerge.The physically meaningful steady state must instead be understood as a superposition of these NESSs that collectively satisfy the required physical constraints.Furthermore,we find that the Liouvillian gap does not necessarily increase monotonically with the system-environment coupling strength.Instead,it can exhibit a nontrivial peak structure,corresponding to a minimum in the relaxation time.The magnitude of this peak is closely related to the symmetry properties of the system.Our results provide a deeper understanding of nonequilibrium behavior in finite quantum systems and offer new insights into the design and control of open quantum dynamics.
文摘Integrated land and resource planning is critical for achieving global sustainability goals,yet a persistent chasm separates policy ambition from on-the-ground outcomes.The review article undertakes a comparative evaluation across the world to diagnose the systemic gaps of the policy that is leading to this implementation failure.We come up with a general typology of 5 categories of gaps that are interconnected:spatial-temporal mismatches,institutional fragmentation,the knowledge-action divide,lack of equity and justice,and broken monitoring and feedback loops.In a comparative study of the High-Income Countries,Rapidly Developing Economies,and Low-Income Countries,we show how these universal gaps are reflected in specific contextual syndromes,which are defined by the political economy,state capacity,and global integration.As can be seen in the analysis,these failures are not stand-alone but exist in a vicious,self-perpetuating cycle that is based on power asymmetries,institutional path dependency,and scale mismatches.In order to break this cycle,we suggest a revolutionary structure of action,which is structured around integration,adaptive management,and justice.The framework identifies the specific operation strategies,such as developing meta-governance formations and establishing community tenure to implement participatory monitoring,and aligning a multi-scale agenda.We infer that the implementation gap must be bridged by going beyond technical solutions to ensure a virtuous circle of legitimate learning-oriented governance that can address the complexity of socio-ecological conditions of the Anthropocene.
文摘The rise of AI speech synthesis,while achieving impressive naturalness,has revealed a profound educational challenge:its failure to convey complex human emotions and contextual nuance-termed the“affective gap”-threatens to undermine the ecology of voice artistry and societal aesthetic discernment.This paper first diagnoses this gap by examining its key manifestations(compound emotion flattening,contextual deafness,the prosodic uncanny valley)and tracing its root cause to the epistemological divide between AI’s data-driven pattern recognition and human embodied experience.It then analyzes the consequent structural disruption to the voice-acting industry’s traditional“pyramid”training model and the broader risk of cultural aesthetic deskilling.In response,the paper’s central contribution is to propose a novel pedagogical framework designed to bridge this gap.This framework advocates a decisive shift in voice education from skill transmission towards critical voice artistry,centered on cultivating students’capacities for deep textual/contextual analysis,empathetic and embodied sensemaking,and the critical evaluation and direction of AI-generated speech.The paper argues that by integrating this critical pedagogical approach with strategic technology use,educators can empower future artists to navigate and shape a hybrid human-AI creative landscape.Ultimately,this work provides a theoretically grounded and actionable roadmap for innovating performing arts education in the AI era,positioning educational technology as vital steward of uniquely human expressive intelligence.
文摘The structures of even-even Gd and Dy isotopes around N=100 were investigated using a fully self-consistent microscopic model.The systematics of the exited 2_(1)^(+)and 4_(1)^(+)energies reveal a peak-like structure at N=100 along the Gd(Z=64)and Dy(Z=66)isotopic chains.This supports the evidence for a subshell gap near N=100.The nuclear structure properties studied are important to understand the r-process elemental abundance peak at A~160.
文摘Further investigation is warranted into the collaborative function of carbon capture and electrolysis-to-gas conversion technologies within integrated electro-gas energy systems,as well as optimized scheduling that addresses the variability of wind and solar energy,to promote multi-energy complementarity and energy decarbonization while enhancing the capacity to absorb new energy.This work presents an optimized scheduling model for electro-gas integrated energy systems that include hydrogen storage,utilizing information gap decision theory(IGDT).A model is constructed that integrates the synergistic functions of carbon capture and storage(CCS),power-to-gas(P2G),and gas turbine units through electrical coupling.A carbon ladder trading mechanism is implemented to mitigate carbon emissions inside the system.A day-ahead optimization scheduling model is subsequently built to maximize system operational profit and ensure hydrogen storage safety,while considering economic viability,low-carbon performance,and safety.Secondly,the trinitrotoluene(TNT)equivalent approach and the half-lethal range were employed to quantify the safety concerns associated with hydrogen storage tanks,offering the model optimization guidance and conservative management.Ultimately,the CCS-P2G integrated operation accounted for the unpredictability in wind and solar energy production through the application of information gap decision theory.The model was solved using the GUROBI solver.The findings indicate that the proposed approach diminishes system carbon emissions by 66%,attains complete integration of wind and solar energy,and eliminates hazardous working time for hydrogen storage tanks,reducing it from 10 h to zero.It ensures system safety while guaranteeing profits of at least 90%of the anticipated value,accounting for changes in wind and solar output within±14%.This confirms the model’s efficacy in improving renewable energy integration rates,facilitating low-carbon,cost-effective,and secure system operation,while mitigating the unpredictability of renewable energy production.
基金support provided by the National Natural Science Foundation of China(No.22273043).
文摘As a novel class of purely organic fluores-cent materials,multiple resonance thermal-ly activated delayed fluorescence(MR-TADF)compounds hold significant promise for next-generation display technologies.The efficiency of exciton utilization and the overall performance of organic light-emit-ting devices are closely linked to the singlet-triplet energy gap(ΔE_(ST))of MR-TADF emitters.Identifying an economic and accu-rate theoretical approach to predictΔE_(ST)would be beneficial for high-throughput screening and facilitate the inverse design of MR-TADF molecules.In this study,we evaluated the S_(1)state energy(E(S_(1))),T_(1)state ener-gy(E(T_(1))),andΔE_(ST)using three different physical interpretations:adiabatic excitation ener-gy,vertical absorption energy,and vertical emission energy.We employed the time-depen-dent density functional theory(TDDFT)and delta self-consistent field(ΔSCF)methods to calculate E(S_(1)),E(T_(1)),andΔE_(ST)for 20 MR-TADF molecules reported in the literature.We compared these calculated values with experimental data obtained from fluorescence spec-troscopy at room-temperature(or 77 K)and phosphorescence spectroscopy conducted at 77 K.Our findings indicate that the vertical absorption energy at the S0 state minimum,deter-mined by theΔSCF method,accurately predicts the S_(1)state energy.Similarly,the vertical absorption energy at the S0 state minimum,calculated using the TDDFT method,effectively predicts the T_(1)state energy.TheΔE_(ST)derived from the difference between these two excita-tion energies exhibited the smallest mean absolute error of only 0.039 eV compared to the ex-perimental values.This combination represents the most accurate and cost-effective method reported to date for predicting theΔE_(ST)of MR-TADF molecules,and can be integrated into AI-driven inverse design workflows for new emitters.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos.XDB28000000 and XDB0460000)the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2021ZD0302600)the National Key Research and Development Program of China(Grant No.2024YFA1409002)。
文摘The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.
基金supported by the National Natural Science Foundation of China(Grant Nos.12222413,12174443,12274459,and 12404266)the National Key R&D Program of China(Grant Nos.2023YFA1406500,2022YFA1403800,and 2022YFA1403103)+3 种基金the Natural Science Foundation of Shanghai (Grant No.23ZR1482200)the Natural Science Foundation of Ningbo (Grant No.2024J019)the Science Research Project of Hebei Education Department (Grant No.BJ2025060)the funding of Ningbo Yongjiang Talent Program。
文摘Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and unstable,making high-quality single-crystal growth,characterization,and measurements difficult,and most do not exhibit superconductivity at ambient pressure.In contrast,La_(3) In stands out for its ambient-pressure superconductivity(T_(C)∼9.4 K)and the availability of high-quality single crystals.Here,we investigate its low-energy electronic structure using angle-resolved photoemission spectroscopy and first-principles calculations.The bands near the Fermi energy(E_(F))are mainly derived from La 5d and In 5p orbitals.A saddle point is directly observed at the Brillouin zone(BZ)boundary,while a three-dimensional Van Hove singularity crosses E_(F) at the BZ corner.First-principles calculations further reveal topological Dirac surface states within the bulk energy gap above E_(F).The coexistence of a high density of states and in-gap topological surface states near𝐸F suggests that La3In offers a promising platform for tuning superconductivity and exploring possible topological superconducting phases through doping or external pressure.
