Fluorescent probes based on intramolecular charge transfer(ICT) have obvious advantages for accurate quantitative analysis.To obtain high-performance ratiometric probes requires distinct photophysical properties durin...Fluorescent probes based on intramolecular charge transfer(ICT) have obvious advantages for accurate quantitative analysis.To obtain high-performance ratiometric probes requires distinct photophysical properties during recognition reaction process,which is closely related to their ICT characteristics.1,8-Naphthalimide is known as a typical fluorophore with desirable ICT property when functionalized with an electron-donating moiety at the para-position of the naphthalene chromophore.Although the photophysical properties of para-substituted 1,8-naphthalimide have been well studied,its meta-substituted counterpart has not been fully evaluated since the meta-position is conventionally thought to be weakly conjugated.Herein,combined experimental and theoretical studies are performed which consistently indicate that stronger charge transfer(CT) is exhibited by the meta-amino substituted 1,8-naphthalimide(m-NH_(2)) compared to the para-amino substituted one(p-NH_(2)).The ratiometric response of fluorescence with significant changes in wavelength and intensity upon acetylation(m-NAc and p-NAc) can be attributed to the larger ICT and stronger-NH_(2) vibrations.This observation is further demonstrated by deuterium oxide experiments,viscosity experiments and quantum chemical calculations.The practical application of meta-amino-1,8-naphthalimide ICT-based probes is also confirmed.This research is expected to bring an in-depth understanding of π-conjugated systems with ICT characteristics,and facilitates the design of sensitive ICT fluorescent probes with meta-amino substitution.展开更多
Realizing efficient and controlled state transfers is necessary for implementing a wide range of classical and quantum information protocols.Recent studies have demonstrated that both asymmetric and symmetric state tr...Realizing efficient and controlled state transfers is necessary for implementing a wide range of classical and quantum information protocols.Recent studies have demonstrated that both asymmetric and symmetric state transfers can be achieved by encircling an exceptional point(EP)in non-Hermitian(NH)systems.However,the application of this phenomenon has been restricted to scenarios where an EP exists in single-qubit systems and is associated with a specific type of dissipation.In this work,we demonstrate efficient and controlled symmetric and asymmetric Bell-state transfers by modulating system parameters within a Jaynes-Cummings model while accounting for atomic spontaneous emission and cavity decay.The effective suppression of nonadiabatic transitions enables a symmetric exchange of Bell states irrespective of the encircling direction.Furthermore,we report a counterintuitive finding:the presence of an EP is not indispensable for implementing asymmetric state transfers in NH systems.We achieve perfect asymmetric Bell-state transfers even in the absence of an EP by dynamically orbiting around an approximate EP.Our work presents an approach to effectively and reliably manipulate entangled states with both symmetric and asymmetric characteristics,through dissipation engineering in NH systems.展开更多
Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supp...Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supported palladium catalyst(Pd-TiO_(2)-Ov)for efficient photocatalytic water-donating transfer hydrogenation of anethole towards 4-n-propylanisole in a high yield of 99.9%,which is significantly higher compared to the pristine TiO_(2)-supported palladium catalyst(Pd-TiO_(2),74%).The enhanced performance is ascribed to the presence of oxygen vacancies,which facilitate light absorption and suppress the recombination of photogenerated electron-hole pairs.Furthermore,the Pd-TiO_(2)-Ov is versatile in hydrogenating various alkene substrates including those with hydroxyl,ether,fluoride,and chloride functional groups in full conversion,thus offering a green method for transfer hydrogenation of alkenes.This study provides new insights and advances in current hydrogenation technology with water as the proton source.展开更多
Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now av...Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.展开更多
This study investigates the enhancement of convective heat transfer in a serpentine pipe using ferrofluid flow influenced by dual non-uniform magnetic sources.The primary objective is to improve thermal performance in...This study investigates the enhancement of convective heat transfer in a serpentine pipe using ferrofluid flow influenced by dual non-uniform magnetic sources.The primary objective is to improve thermal performance in compact cooling systems,such as those used in heat exchangers.A two-dimensional,steady-state Computational Fluid Dynamic(CFD)model is developed in ANSYS Fluent to simulate the behavior of an incompressible ferrofluid under applied constant heat flux and magnetic fields.The magnetic force is modeled using the Kelvin force,which acts on magnetized nanoparticles in response to spatially varying electromagnetic fields generated by two strategically positioned current-carrying wires.The effects of magnetic field strength,quantified by the magnetic number(Mn),on flow behavior and temperature distribution are thoroughly analyzed.The results indicate that increasing Mn leads to higher Nusselt numbers,demonstrating enhanced convective heat transfer.Secondary vortices induced by magnetic forcing improve fluid mixing,particularly in curved regions of the pipe.A mesh-independence study and model validation with benchmark data support the reliability of the numerical framework.This work highlights the potential of magnetic-field-assisted thermal control in energy-efficient cooling applications and provides a foundation for the further development of advanced ferrofluid-based heat transfer systems.展开更多
In this study,we meticulously designed a layered carbon-based catalytic material to induce the degradation of a series of organic pollutants by activating peroxymonosulfate(PMS) in the PMS-based advanced oxidation pro...In this study,we meticulously designed a layered carbon-based catalytic material to induce the degradation of a series of organic pollutants by activating peroxymonosulfate(PMS) in the PMS-based advanced oxidation processes(AOPs).Results indicated that the silicon and oxygen elements from the montmorillonite were incorporated into the catalyst matrix to form the Si-O-C structure.It was notable that the layered carbonaceous material with Si-O-C structure exhibited an outstanding catalytic effect on the synthesized layered catalytic material array,achieving over 90 % removal rate of most pollutants within 60 min.It was notable that the layered carbonaceous material with Si-O-C structure exhibited an outstanding catalytic effect on the synthesized layered catalytic material array.The salt bridge system confirmed that pollutants can provide electrons to the Si-O-C/PMS system,and we verified that the electron transfer process(ETP) mechanism was the main pathway for the degradation of pollutants in the Si-O-C/PMS system via the open-circuit potential analysis.In combination with the structural properties of different pollutants,we discovered that electron-donating pollutants can supply more electrons to the Si-O-C/PMS system,thereby enhancing the ETP process.The findings of this study are anticipated to advance the development and practical application of layered carbonaceous materials-based catalysts and support the design and implementation of nanoconfined catalysts in the field of AOPs.展开更多
The development of catalytic multicomponent reactions for constructing complex organic scaffolds from readily accessible commodity chemicals is a key pursuit in contemporary synthetic chemistry.Current methods for syn...The development of catalytic multicomponent reactions for constructing complex organic scaffolds from readily accessible commodity chemicals is a key pursuit in contemporary synthetic chemistry.Current methods for synthesizing thioesters primarily rely on the acylation of thiols,which produces substantial waste and requires malodorous,unstable sulfur sources.In this work,we introduce a photocatalyzed hydrogen transfer strategy that enables a three-component synthesis of thioesters using abundant primary alcohols,easily available alkenes and elemental sulfur under mild conditions.This protocol demonstrates broad applicability and high chemo-and regioselectivity for both primary alcohols and alkenes,highlighting the advantage and potential of photo-mediated hydrogen transfer in facilitating multicomponent reactions using primary alcohol and elemental sulfur feedstocks.展开更多
The severe shuttle effect and sluggish reaction kinetics in room-temperature sodium-sulfur(RT Na-S)batteries have been major bottlenecks hindering their practical application.To overcome these challenges,a straightfor...The severe shuttle effect and sluggish reaction kinetics in room-temperature sodium-sulfur(RT Na-S)batteries have been major bottlenecks hindering their practical application.To overcome these challenges,a straightforward reduction approach was employed to design three bimetallic alloy nanoparticles(FeNi,FeCo,and NiCo)supported on multistage porous carbon substrates.Experimental and theoretical calculations reveal that the charge transfer within the alloy catalyst influences the position of its d-band center and its degree of hybridization with sodium polysulfides(NaPSs).An increased charge transfer leads to a shift of the alloy’s d-band center closer to the Fermi energy level,thereby enhancing its adsorption and catalytic capabilities.Among the three alloy compositions,the FeNi alloy exhibits the highest charge transfer.Consequently,the FeNi alloy demonstrates the superior electrochemical performance,achieving a high reversible specific capacity of 848.2 mA h g^(−1),with an average capacity degradation rate of only 0.037%per cycle over 1000 cycles at 1.2 C.The S/FeNi/NC cathode exhibits a low electrolyte-to-sulfur(E/S)ratio of 6.6µL mg^(−1),while maintaining a high reversible specific capacity of 568.1 mA h g^(−1).This offers valuable insights for the application of alloy catalysts in the S/FeNi/NC cathode of RT Na-S batteries.展开更多
This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This eva...This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.展开更多
In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free...In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.展开更多
Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-base...Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-based machine learning(ML)technique combined with long-time ML-parameterized molecular dynamics simulations,we have systematically investigated charge transfer and friction at the interfaces between h-BN and water.The introduction of defects(including Stone-Wales,B-vacancy,N-vacancy,and B-vacancy/N-vacancy defects)into h-BN significantly enhances heterogeneous charge polarization and distribution at h-BN layers,as well as increases the friction coefficients at water/h-BN interfaces compared to perfect h-BN.The observed increase in interfacial friction of defected h-BN can be attributed to stronger charge transfer and higher charge density at the defected h-BN layers induced by interactions with water molecules.Our results offer deeper insights into the role of defects in modulating charge exchange and transfer between water and h-BN,as well as their impact on interfacial friction.展开更多
Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively...Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively annotated datasets.This research presents a novel,efficient approach that leveragesmulti-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group(VGG)—to overcome this limitation.Ourmethod significantly reduces dependency on vast labeled data while achieving high accuracy.Evaluated on the Aerial Image Dataset(AID)dataset,the model attained a validation accuracy of 93.6%with a loss of 0.35,demonstrating robust performance with minimal training data.These results underscore the viability of our approach for real-time,data-efficient scene recognition,offering a practical and cost-effective advancement for the field.展开更多
Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly r...Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.展开更多
Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong d...Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.展开更多
In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalizatio...In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data,especially in small-sample scenarios.To address these chal-lenges,transfer learning has emerged as an effective strategy to leverage knowledge from related domains,thereby enhancing model per-formance with limited target data.This review systematically summarizes the fundamental concepts,methodologies,and representative applications of transfer learning in the prediction of metallic materials'properties.Transfer learning can be categorized into feature-based,instance-based,parameter-based,and knowledge-based methods.This work discusses their respective mechanisms,advantages,and limit-ations.Case studies demonstrate that transfer learning can significantly improve prediction accuracy,data efficiency,and model inter-pretability in tasks such as mechanical property prediction and alloy design.Furthermore,this work highlights emerging trends including hybrid,multi-task,meta,and adaptive transfer learning,which further expand the applicability of these techniques.Finally,this work out-lines future research directions,emphasizing the need for data standardization,algorithmic innovation,multimodal data fusion,and the in-tegration of physical principles to achieve robust,interpretable,and generalizable models.The perspectives presented aim to advance the intelligent design and discovery of metallic materials,promoting efficient knowledge transfer and collaborative innovation in materials science.展开更多
The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to...The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.展开更多
Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numer...Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numerically investigate the reaction process of hydrocarbon-containing VOCs in RCO using computational fluid dynamics(CFD)simulation.To obtain the conversion characteristics of multi-component hydrocarbons,the effects of intake load,equivalence ratio,and the composition of multi-component hydrocarbons on the flow,heat transfer,and conversion rate of the reactor were analyzed.A feasibility study plan targeting the hard-to-convert components was also proposed.The results indicated that as the load increases,the conversion rates of the various components decrease,while the reaction rates increase.Moreover,increasing the flow velocity intensifies turbulence and enhances the collision frequency between the gas and the wall surfaces.This,in turn,amplifies the resistance effect of the porous medium.As the equivalence ratio of VOCs to oxygen increases,the oxygen-deficient condition leads to a decrease in the molecular weight of the hydrocarbons involved in the reaction.The reaction temperature also shows a downward trend.A comparative analysis of the catalytic combustion characteristics of multi-component VOCs and single-component gases reveals that adding ethane and propane can facilitate methane oxidation.展开更多
Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral mole...Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.展开更多
Lithium-rich layered oxides(LRLOs)are promising cathode materials due to their high specific capacity,energy density,and operating voltage.However,their performance is hindered by the limited redox activity of transit...Lithium-rich layered oxides(LRLOs)are promising cathode materials due to their high specific capacity,energy density,and operating voltage.However,their performance is hindered by the limited redox activity of transition metals,leading to oxygen redox instability,oxygen release,and capacity degradation.To address these issues,we propose an innovative lattice-oxygen modulation(LOM)strategy that incorporates Mn^(3+)and Ti^(4+)into the Li_(1.2)Cr_(0.3)Mn_(0.4)Ti_(0.1)O_(2) system,effectively mitigating Cr migration,stabilizing oxygen redox reactions,and reinforcing structural integrity.This results in improved electrochemical performance,as demonstrated by a 56.5 mAh g^(−1) increase in initial discharge capacity to 364.2 mAh g^(−1),with 71.3%capacity retention after 30 cycles,reflecting a 20.2%improvement in cycling stability.Density functional theory(DFT)calculations confirm enhanced Cr redox reversibility and reduced oxygen evolution,further strengthening structural stability.These synergistic effects highlight the pivotal role of the LOM strategy in optimizing both electrochemical performance and structural integrity,offering a scalable pathway to improve capacity and cycling stability in lithium-rich cathodes.展开更多
Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learni...Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.展开更多
基金financially supported by National Key Research and Development Programs (Nos.2022YFD1700403 and 2023YFD1700303)National Natural Science Foundation of China (Nos.12274128 and 12250003)+2 种基金Shanghai Rising-Star Program (No.21QA1402600)the support of NYU-ECNU Center for Computational Chemistry at NYU Shanghaithe University of Bath and the Open Research Fund of the School of Chemistry and Chemical Engineering,Henan Normal University (No.2020ZD01) for support。
文摘Fluorescent probes based on intramolecular charge transfer(ICT) have obvious advantages for accurate quantitative analysis.To obtain high-performance ratiometric probes requires distinct photophysical properties during recognition reaction process,which is closely related to their ICT characteristics.1,8-Naphthalimide is known as a typical fluorophore with desirable ICT property when functionalized with an electron-donating moiety at the para-position of the naphthalene chromophore.Although the photophysical properties of para-substituted 1,8-naphthalimide have been well studied,its meta-substituted counterpart has not been fully evaluated since the meta-position is conventionally thought to be weakly conjugated.Herein,combined experimental and theoretical studies are performed which consistently indicate that stronger charge transfer(CT) is exhibited by the meta-amino substituted 1,8-naphthalimide(m-NH_(2)) compared to the para-amino substituted one(p-NH_(2)).The ratiometric response of fluorescence with significant changes in wavelength and intensity upon acetylation(m-NAc and p-NAc) can be attributed to the larger ICT and stronger-NH_(2) vibrations.This observation is further demonstrated by deuterium oxide experiments,viscosity experiments and quantum chemical calculations.The practical application of meta-amino-1,8-naphthalimide ICT-based probes is also confirmed.This research is expected to bring an in-depth understanding of π-conjugated systems with ICT characteristics,and facilitates the design of sensitive ICT fluorescent probes with meta-amino substitution.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFA1408900)the National Natural Science Foundation of China(Grant Nos.12264040,12374333,and U21A20436)+2 种基金the Jiangxi Natural Science Foundation(Grant Nos.20232BCJ23022 and 20252BAC240119)the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301705)the Jiangxi Province Key Laboratory of Applied Optical Technology(Grant No.2024SSY03051)。
文摘Realizing efficient and controlled state transfers is necessary for implementing a wide range of classical and quantum information protocols.Recent studies have demonstrated that both asymmetric and symmetric state transfers can be achieved by encircling an exceptional point(EP)in non-Hermitian(NH)systems.However,the application of this phenomenon has been restricted to scenarios where an EP exists in single-qubit systems and is associated with a specific type of dissipation.In this work,we demonstrate efficient and controlled symmetric and asymmetric Bell-state transfers by modulating system parameters within a Jaynes-Cummings model while accounting for atomic spontaneous emission and cavity decay.The effective suppression of nonadiabatic transitions enables a symmetric exchange of Bell states irrespective of the encircling direction.Furthermore,we report a counterintuitive finding:the presence of an EP is not indispensable for implementing asymmetric state transfers in NH systems.We achieve perfect asymmetric Bell-state transfers even in the absence of an EP by dynamically orbiting around an approximate EP.Our work presents an approach to effectively and reliably manipulate entangled states with both symmetric and asymmetric characteristics,through dissipation engineering in NH systems.
基金supported by the National Key Research and Development Program of China(2023YFD2200505)National Natural Science Foundation of China(22202105),Natural Science Foundation of Jiangsu Higher Education Institutions of China(21KJA150003)the Innovation and Entrepreneurship Team Program of Jiangsu Province(JSSCTD202345).
文摘Photocatalytic transfer hydrogenation using water as the proton source has emerged as an attractive and green approach for the catalytic reduction of unsaturated bonds.Herein,we report an oxygen-defective TiO_(2)-supported palladium catalyst(Pd-TiO_(2)-Ov)for efficient photocatalytic water-donating transfer hydrogenation of anethole towards 4-n-propylanisole in a high yield of 99.9%,which is significantly higher compared to the pristine TiO_(2)-supported palladium catalyst(Pd-TiO_(2),74%).The enhanced performance is ascribed to the presence of oxygen vacancies,which facilitate light absorption and suppress the recombination of photogenerated electron-hole pairs.Furthermore,the Pd-TiO_(2)-Ov is versatile in hydrogenating various alkene substrates including those with hydroxyl,ether,fluoride,and chloride functional groups in full conversion,thus offering a green method for transfer hydrogenation of alkenes.This study provides new insights and advances in current hydrogenation technology with water as the proton source.
文摘Conventionally,foundations have been classified as shallow or deep in routine civil engineering practice.However,due to recent developments,two other approaches,semi-deep and ground modification foundations,are now available,complicating foundation categorization.Accordingly,a new concept for foundation categorization is introduced in this paper based on insights into the theory of structure analysis.Based on the form aspect,foundation systems can be categorized as one-dimensional(linear),two-dimensional(planar),and threedimensional(volumetric).Based on the load transfer aspect,foundations can also be categorized as vector-acting(piles),section or surface-acting(rafts and shells),and block-acting(piled rafts).As a step toward implementing this new categorization scheme,a database of 22 cases has been compiled,symbolizing novel introduced foundation systems.This compilation involves structures such as offshore jackets,high-rise buildings,towers and storages,and diverse geomaterials.Among them,a few have been selected for detailed evaluation,emphasizing influential factors in foundation selection,comprising superstructure,subsoil condition,foundation system,circumferential conditions,and supplementary considerations,that is,constructional and sustainability-based issues.Lessons learned from experience and these knowledge-based cases have described for foundation selection and implementation.Geotechnical and practical aspects with critical components have been realized as major performance assessment and comparison factors.Foundation systems have been compared and ranked using the improved analytic hierarchy process approach.Finally,four categories of buildings,from low-rise to towers and four prevailing levels of soil strength,from soft to very hard,have been considered to propose a perspective for building substructure implementation,adapted via relevant cases.Overall,the introduced categorization is recognized as an efficient algorithm for the experimentation of appropriate foundations for specific structures and subsoil conditions.
文摘This study investigates the enhancement of convective heat transfer in a serpentine pipe using ferrofluid flow influenced by dual non-uniform magnetic sources.The primary objective is to improve thermal performance in compact cooling systems,such as those used in heat exchangers.A two-dimensional,steady-state Computational Fluid Dynamic(CFD)model is developed in ANSYS Fluent to simulate the behavior of an incompressible ferrofluid under applied constant heat flux and magnetic fields.The magnetic force is modeled using the Kelvin force,which acts on magnetized nanoparticles in response to spatially varying electromagnetic fields generated by two strategically positioned current-carrying wires.The effects of magnetic field strength,quantified by the magnetic number(Mn),on flow behavior and temperature distribution are thoroughly analyzed.The results indicate that increasing Mn leads to higher Nusselt numbers,demonstrating enhanced convective heat transfer.Secondary vortices induced by magnetic forcing improve fluid mixing,particularly in curved regions of the pipe.A mesh-independence study and model validation with benchmark data support the reliability of the numerical framework.This work highlights the potential of magnetic-field-assisted thermal control in energy-efficient cooling applications and provides a foundation for the further development of advanced ferrofluid-based heat transfer systems.
基金supported by National Natural Science Foundation of China (Nos.52170086,22476116,52074176)Natural Science Foundation of Shandong Province (Nos.ZR2021ME013,ZR2024ME156,ZR2022QB250)。
文摘In this study,we meticulously designed a layered carbon-based catalytic material to induce the degradation of a series of organic pollutants by activating peroxymonosulfate(PMS) in the PMS-based advanced oxidation processes(AOPs).Results indicated that the silicon and oxygen elements from the montmorillonite were incorporated into the catalyst matrix to form the Si-O-C structure.It was notable that the layered carbonaceous material with Si-O-C structure exhibited an outstanding catalytic effect on the synthesized layered catalytic material array,achieving over 90 % removal rate of most pollutants within 60 min.It was notable that the layered carbonaceous material with Si-O-C structure exhibited an outstanding catalytic effect on the synthesized layered catalytic material array.The salt bridge system confirmed that pollutants can provide electrons to the Si-O-C/PMS system,and we verified that the electron transfer process(ETP) mechanism was the main pathway for the degradation of pollutants in the Si-O-C/PMS system via the open-circuit potential analysis.In combination with the structural properties of different pollutants,we discovered that electron-donating pollutants can supply more electrons to the Si-O-C/PMS system,thereby enhancing the ETP process.The findings of this study are anticipated to advance the development and practical application of layered carbonaceous materials-based catalysts and support the design and implementation of nanoconfined catalysts in the field of AOPs.
基金National Natural Science Foundation of China (Nos.22071185 and 22271224)the Fundamental Research Funds for the Central Universities (No.2042019kf0008)Wuhan University startup funding for financial support。
文摘The development of catalytic multicomponent reactions for constructing complex organic scaffolds from readily accessible commodity chemicals is a key pursuit in contemporary synthetic chemistry.Current methods for synthesizing thioesters primarily rely on the acylation of thiols,which produces substantial waste and requires malodorous,unstable sulfur sources.In this work,we introduce a photocatalyzed hydrogen transfer strategy that enables a three-component synthesis of thioesters using abundant primary alcohols,easily available alkenes and elemental sulfur under mild conditions.This protocol demonstrates broad applicability and high chemo-and regioselectivity for both primary alcohols and alkenes,highlighting the advantage and potential of photo-mediated hydrogen transfer in facilitating multicomponent reactions using primary alcohol and elemental sulfur feedstocks.
基金supported by Shaanxi Fundamental Science Research Project for Chemistry and Biology(23JHQ011)Natural Science Foundation of Shaanxi(2024JC-YBMS-115)Natural Science Basic Research Plan in Shaanxi Province of China(2025JC-YBMS-141)。
文摘The severe shuttle effect and sluggish reaction kinetics in room-temperature sodium-sulfur(RT Na-S)batteries have been major bottlenecks hindering their practical application.To overcome these challenges,a straightforward reduction approach was employed to design three bimetallic alloy nanoparticles(FeNi,FeCo,and NiCo)supported on multistage porous carbon substrates.Experimental and theoretical calculations reveal that the charge transfer within the alloy catalyst influences the position of its d-band center and its degree of hybridization with sodium polysulfides(NaPSs).An increased charge transfer leads to a shift of the alloy’s d-band center closer to the Fermi energy level,thereby enhancing its adsorption and catalytic capabilities.Among the three alloy compositions,the FeNi alloy exhibits the highest charge transfer.Consequently,the FeNi alloy demonstrates the superior electrochemical performance,achieving a high reversible specific capacity of 848.2 mA h g^(−1),with an average capacity degradation rate of only 0.037%per cycle over 1000 cycles at 1.2 C.The S/FeNi/NC cathode exhibits a low electrolyte-to-sulfur(E/S)ratio of 6.6µL mg^(−1),while maintaining a high reversible specific capacity of 568.1 mA h g^(−1).This offers valuable insights for the application of alloy catalysts in the S/FeNi/NC cathode of RT Na-S batteries.
基金supported by the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Educationsupported by the National Natural Scientific Foundation of China under Grant No.NSFC12131006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Science under Grant No.CI2023C063YLL。
文摘This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.
文摘In recent years,with the rapid advancement of artificial intelligence,object detection algorithms have made significant strides in accuracy and computational efficiency.Notably,research and applications of Anchor-Free models have opened new avenues for real-time target detection in optical remote sensing images(ORSIs).However,in the realmof adversarial attacks,developing adversarial techniques tailored to Anchor-Freemodels remains challenging.Adversarial examples generated based on Anchor-Based models often exhibit poor transferability to these new model architectures.Furthermore,the growing diversity of Anchor-Free models poses additional hurdles to achieving robust transferability of adversarial attacks.This study presents an improved cross-conv-block feature fusion You Only Look Once(YOLO)architecture,meticulously engineered to facilitate the extraction ofmore comprehensive semantic features during the backpropagation process.To address the asymmetry between densely distributed objects in ORSIs and the corresponding detector outputs,a novel dense bounding box attack strategy is proposed.This approach leverages dense target bounding boxes loss in the calculation of adversarial loss functions.Furthermore,by integrating translation-invariant(TI)and momentum-iteration(MI)adversarial methodologies,the proposed framework significantly improves the transferability of adversarial attacks.Experimental results demonstrate that our method achieves superior adversarial attack performance,with adversarial transferability rates(ATR)of 67.53%on the NWPU VHR-10 dataset and 90.71%on the HRSC2016 dataset.Compared to ensemble adversarial attack and cascaded adversarial attack approaches,our method generates adversarial examples in an average of 0.64 s,representing an approximately 14.5%improvement in efficiency under equivalent conditions.
基金supported by the National Natural Science Foundation of China(12472105)the Western Light Project of CAS(xbzg-zdsys-202118)+1 种基金the Fundamental Research Funds for the Central Universities(NO.NJ2024001)a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Understanding the friction behavior between hexagonal boron nitride(h-BN)and water is critical for the potential applications of h-BN in liquid-related functional devices.By using a density-functional-theory(DFT)-based machine learning(ML)technique combined with long-time ML-parameterized molecular dynamics simulations,we have systematically investigated charge transfer and friction at the interfaces between h-BN and water.The introduction of defects(including Stone-Wales,B-vacancy,N-vacancy,and B-vacancy/N-vacancy defects)into h-BN significantly enhances heterogeneous charge polarization and distribution at h-BN layers,as well as increases the friction coefficients at water/h-BN interfaces compared to perfect h-BN.The observed increase in interfacial friction of defected h-BN can be attributed to stronger charge transfer and higher charge density at the defected h-BN layers induced by interactions with water molecules.Our results offer deeper insights into the role of defects in modulating charge exchange and transfer between water and h-BN,as well as their impact on interfacial friction.
基金funded by the Deanship of Scientific Research and Libraries,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No.(RPFAP-23-1445).
文摘Scene recognition is a critical component of computer vision,powering applications from autonomous vehicles to surveillance systems.However,its development is often constrained by a heavy reliance on large,expensively annotated datasets.This research presents a novel,efficient approach that leveragesmulti-model transfer learning from pre-trained deep neural networks—specifically DenseNet201 and Visual Geometry Group(VGG)—to overcome this limitation.Ourmethod significantly reduces dependency on vast labeled data while achieving high accuracy.Evaluated on the Aerial Image Dataset(AID)dataset,the model attained a validation accuracy of 93.6%with a loss of 0.35,demonstrating robust performance with minimal training data.These results underscore the viability of our approach for real-time,data-efficient scene recognition,offering a practical and cost-effective advancement for the field.
基金supported by the Research Program of State Key Laboratory of Heavy Ion Science and Technology,Institute of Modern Physics,Chinese Academy of Sciences(No.HIST2025CS06)the National Natural Science Foundation of China(Nos.12405402,12475106,12105327,and 12405337)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023B1515120067)。
文摘Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.
文摘Accurate assessment of seismic landslide susceptibility is crucial for disaster prevention and emergency decision-making.Although machine learning methods have been widely applied in this field,they exhibit a strong dependence on large quantities of highquality samples,resulting in significantly low prediction accuracy of existing studies under data-scarce or crossregional prediction scenarios,which fail to meet practical application requirements.To address this issue,this study proposes an intelligent prediction model integrating transfer learning and a sampling optimization strategy,aiming to enhance the accuracy and applicability of seismic landslide susceptibility assessment.The model first improves the sample collection method through the sampling optimization strategy to enhance the precision and representativeness of training samples.This not only ensures the accuracy of origin area training but also further strengthens the model's predictive ability in the target area.Subsequently,it incorporates Transfer Component Analysis(TCA)to overcome the differences in environmental characteristics between the origin area and target area,and couples TCA with the Light GBM algorithm to construct the TCA-Light GBM model,realizing the assessment of seismic landslide susceptibility in sample-free areas.Validated through case studies of the Jiuzhaigou and Luding earthquakes,the results demonstrate that the proposed TCALight GBM transfer learning method exhibits excellent applicability in seismic landslide susceptibility prediction.After optimization with the TCA algorithm,the model's prediction performance in the target domain is significantly improved,with the AUC value increasing from 0.719 to 0.827,representing an increase of approximately 15.02%.This indicates that TCA technology can effectively alleviate the feature distribution discrepancy between the source domain and target domain,enhancing the model's generalization ability.The method is particularly suitable for scenarios with data scarcity and cross-regional prediction and can provide reliable technical support for the emergency response and risk prevention and control of seismic hazards.
基金supported by the National NaturalScience Foundation of China(Nos.52301029 and 52274359)the Fundamental Research Funds for the CentralUniversities,China(No.06500165)+2 种基金the Guangdong Basicand Applied Basic Research Foundation,China(No.2022A1515140006)Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)Beijing Young Elite Scientists Sponsorship Program by BMES,China.
文摘In the era of materials genome engineering,data-driven machine learning has become a powerful tool for accelerating the re-search and development of metallic materials.However,the predictive accuracy and generalization ability of traditional machine learning models are often limited by the scarcity and heterogeneity of available data,especially in small-sample scenarios.To address these chal-lenges,transfer learning has emerged as an effective strategy to leverage knowledge from related domains,thereby enhancing model per-formance with limited target data.This review systematically summarizes the fundamental concepts,methodologies,and representative applications of transfer learning in the prediction of metallic materials'properties.Transfer learning can be categorized into feature-based,instance-based,parameter-based,and knowledge-based methods.This work discusses their respective mechanisms,advantages,and limit-ations.Case studies demonstrate that transfer learning can significantly improve prediction accuracy,data efficiency,and model inter-pretability in tasks such as mechanical property prediction and alloy design.Furthermore,this work highlights emerging trends including hybrid,multi-task,meta,and adaptive transfer learning,which further expand the applicability of these techniques.Finally,this work out-lines future research directions,emphasizing the need for data standardization,algorithmic innovation,multimodal data fusion,and the in-tegration of physical principles to achieve robust,interpretable,and generalizable models.The perspectives presented aim to advance the intelligent design and discovery of metallic materials,promoting efficient knowledge transfer and collaborative innovation in materials science.
基金supported by the Tianjin Education Commission Research Program Project(No.2024KJ105)。
文摘The buoyancy-induced flow constitutes a core scientific issue for thermal management of electronic devices and thermal design of energy systems,where accurate characterization of flow and heat transfer is essential to improve thermal efficiency.In this work,buoyancy-induced flow above two heating elements flush-mounted at the bottom of a square enclosure containing air is numerically investigated over a range of Rayleigh numbers(0<Ra≤1.5×10^(8)),with a focus on equal and unequal heat flux conditions under a constraint of constant total thermal energy input.Distinct flow transitions are observed in both cases,leading to the identification of three flow regimes:Steady,periodic unsteady,and chaotic unsteady.Two types of periodic flows are distinguished,in which the first is a periodic flow dominated by a fundamental frequency(FF)and its integer-multiple frequencies(INTMF),while the second is a more complex periodic flow featuring FF,INTMF,and their sub-harmonics.The transitions between these regimes are affected by the relative heat flux of the two heaters.When the heat flux of the two heaters is unequal,the range of Rayleigh numbers corresponding to periodic flow is suppressed.It is also found that the time-averaged maximum temperature of the strong heater increases more rapidly with Ra,while that of the weak heater increases more slowly,reflecting the interaction between buoyancy-driven flow dynamics and asymmetric heat input.Analysis of the time-averaged Nusselt number demonstrates that heat dissipation from the isothermal walls remains roughly equivalent,even when the heat flux of the two heaters differs by a factor of two.These findings highlight the critical roles of Rayleigh number,the number of heaters,and the heat flux ratio of the heaters in determining heat transfer and flow characteristics for buoyancy-driven convection systems,providing important theoretical support and design references for engineering scenarios such as electronic devices and design of new energy systems.
基金supported by National Key Research&Development Program of China(2022YFB4101500).
文摘Regenerative catalytic oxidizers(RCO)are widely used to remove volatile organic compounds(VOCs)due to their energy-saving and stability.In this study,a multi-component catalytic reaction model was constructed to numerically investigate the reaction process of hydrocarbon-containing VOCs in RCO using computational fluid dynamics(CFD)simulation.To obtain the conversion characteristics of multi-component hydrocarbons,the effects of intake load,equivalence ratio,and the composition of multi-component hydrocarbons on the flow,heat transfer,and conversion rate of the reactor were analyzed.A feasibility study plan targeting the hard-to-convert components was also proposed.The results indicated that as the load increases,the conversion rates of the various components decrease,while the reaction rates increase.Moreover,increasing the flow velocity intensifies turbulence and enhances the collision frequency between the gas and the wall surfaces.This,in turn,amplifies the resistance effect of the porous medium.As the equivalence ratio of VOCs to oxygen increases,the oxygen-deficient condition leads to a decrease in the molecular weight of the hydrocarbons involved in the reaction.The reaction temperature also shows a downward trend.A comparative analysis of the catalytic combustion characteristics of multi-component VOCs and single-component gases reveals that adding ethane and propane can facilitate methane oxidation.
基金financially supported by the National Natural Science Foundation of China (Nos.22171165 and 22371170)Natural Science Foundation of Shandong Province (No.ZR2022MB080)Scientific and Technological Frontiers in Project of Henan Province(No.242102110192)。
文摘Co-assembling chiral molecules with achiral compounds via non-covalent interactions like areneperfluoroarene(AP) interactions offers an effective approach for fabricating chiral functional materials.Herein,chiral molecules L/D-PF1 and L/D-PF2 with pyrene groups were synthesized and its chiroptical properties upon co-assembly with achiral compound octafluoronaphthalene(OFN) through AP interaction were systemically studied.The co-assembly of L/D-PF1/OFN and L/D-PF2/OFN exhibited distinct chiroptical properties such as circular dichroism(CD) and circularly polarized luminescence(CPL) signals.Chirality transfer from the chirality center of L/D-PF1 and L/D-PF2 to the achiral OFN and chiral amplification were successfully achieved.Besides,no significant CPL signal was observed in the self-assembly of L/DPF1 or L/D-PF2 while co-assembly with OFN exhibited obvious CPL amplification induced by AP interaction.Notably,a reversal CD signal and CPL signal could be observed in L/D-PF2/OFN when the molar ratio changed from 1:1 to 1:2 while not found in L/D-PF1/OFN,indicating that that minor structural changes of molecules could cause large changes in assembly.In addition,a series of computational calculations were conducted to verify the AP interaction between L-PF1/L-PF2 and OFN.This work demonstrated that arene-perfluoroarene interaction could drive chiral transfer,chiral amplification and chiral inversion and provided a new method for the preparation of chiroptical materials.
基金support from National Key R&D Program of China(2022YFB3807200)Science and Technology Commission of Shanghai Municipality(25CL2902100).
文摘Lithium-rich layered oxides(LRLOs)are promising cathode materials due to their high specific capacity,energy density,and operating voltage.However,their performance is hindered by the limited redox activity of transition metals,leading to oxygen redox instability,oxygen release,and capacity degradation.To address these issues,we propose an innovative lattice-oxygen modulation(LOM)strategy that incorporates Mn^(3+)and Ti^(4+)into the Li_(1.2)Cr_(0.3)Mn_(0.4)Ti_(0.1)O_(2) system,effectively mitigating Cr migration,stabilizing oxygen redox reactions,and reinforcing structural integrity.This results in improved electrochemical performance,as demonstrated by a 56.5 mAh g^(−1) increase in initial discharge capacity to 364.2 mAh g^(−1),with 71.3%capacity retention after 30 cycles,reflecting a 20.2%improvement in cycling stability.Density functional theory(DFT)calculations confirm enhanced Cr redox reversibility and reduced oxygen evolution,further strengthening structural stability.These synergistic effects highlight the pivotal role of the LOM strategy in optimizing both electrochemical performance and structural integrity,offering a scalable pathway to improve capacity and cycling stability in lithium-rich cathodes.
基金supported in part by the National Natural Science Foundation of China(62472146)the Key Technologies Research Development Joint Foundation of Henan Province of China(225101610001)。
文摘Deep transfer learning has achieved significant success in anomaly detection over the past decade,but data acquisition challenges in practical engineering hinder high-quality feature representation for few-shot learning tasks.To address this issue,a novel time-frequency-assisted deep feature enhancement(TFE)mechanism is proposed.Unlike traditional methods that integrate time-frequency analysis with deep neural networks,TFE employs a wavelet scattering transform to establish a parallel time-frequency feature space,where a dual interaction strategy facilitates collaboration between deep feature and time-frequency spaces through two operations:1)Enhancement,where a frequency-importance-driven contrastive learning(FICL)network transfers physically-aware information from wavelet scattering features to deep features,and 2)Feedback,which uses a detection rule adaptation module to minimize bias in wavelet scattering features based on deep feature performance.TFE is applied to a domain-adversarial anomaly detection framework and,through alternating training,significantly enhances both deep feature discriminative power and few-shot anomaly detection.Theoretical analysis confirms that the proposed dual interaction strategy reduces the upper bound of classification error.Experiments on benchmark datasets and a real-world industrial dataset from a large steel factory demonstrate TFE's superior performance and highlight the importance of frequency saliency in transfer learning.Thus,collaboration is shown to outperform integration for few-shot transfer learning in anomaly detection.
