Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while signific...Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.展开更多
Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous...Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.展开更多
The electrocaloric(EC)effect refers to the change in the polarization entropy and/or temperature of dielectric materials when an electric field is applied and removed.EC refrigeration has received increasing interest ...The electrocaloric(EC)effect refers to the change in the polarization entropy and/or temperature of dielectric materials when an electric field is applied and removed.EC refrigeration has received increasing interest as an alternative to conventional refrigeration technologies because it provides both high energy efficiency and zero global warming potential.In this review,we first introduce the thermodynamic fundamentals of the EC effect and the mechanism of EC refrigeration cycles.We then present recent advances in EC cooling technologies,from material improvements to device demonstrations,including a critical analysis of existing material and device characterization methodologies and a discussion of how to reliably measure the parameters of materials and devices.Finally,the current challenges and possible future prospects for EC cooling technology are outlined.展开更多
The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ...The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.展开更多
This review draws attention to the innovative use of arrowroot(Maranta arundinacea)fiber as a unique and underutilized biomass source for nanocrystalline cellulose(NCC)-based nanocomposites,presenting a noteworthy alt...This review draws attention to the innovative use of arrowroot(Maranta arundinacea)fiber as a unique and underutilized biomass source for nanocrystalline cellulose(NCC)-based nanocomposites,presenting a noteworthy alternative to extensively researched materials like wood pulp,bacterial cellulose,and chemically modified NCCs.In contrast to traditional sources,arrowroot possesses a naturally elevated cellulose and diminished lignin content,facilitating more effective NCC extraction requiring reduced chemical input and enabling environmentally friendly processing techniques.The review evaluates the performance of arrowroot-derived nanocomposites against systems documented in the literature,including NCC-based shape memory composites and nanoparticle-reinforced films,demonstrating enhanced tensile strength,improved moisture barrier properties,and thermal stability,as well as potential piezoelectric response.This study recognizes arrowroot as a viable option in the biomass-based nanocellulose sector,providing ecological and functional benefits while tackling significant issues such as process scalability and feedstock variability,thereby offering important insights for the advancement of sustainable materials.展开更多
Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have g...Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.展开更多
Heat exchangers play a crucial role in thermal energy systems,with their performance directly impacting efficiency,cost,and environmental impact.Apowerful technique for performance improvement can be given by passive ...Heat exchangers play a crucial role in thermal energy systems,with their performance directly impacting efficiency,cost,and environmental impact.Apowerful technique for performance improvement can be given by passive enhancement strategies,which are characterized by their dependability and minimal external power requirements.This comprehensive review critically assesses recent advancements in such passive methods to evaluate their heat transfer mechanisms,performance characteristics,and practical implementation challenges.Our methodology involves a systematic and comprehensive analysis of various heat transfer enhancement techniques,including surface modifications,extended surfaces,swirl flow devices,and tube inserts.This approach synthesizes and integrates findings from a broad spectrum of experimental investigations and numerical simulations to establish a cohesive understanding of their performance characteristics and underlyingmechanisms.Based on the findings,passive heat transfer techniques result in significant improvements in thermal performance;for instance,corrugated and roughened surfaces increase the heat transfer coefficient by 50%–200%,and advanced insert geometries,such as modified twisted tapes,can increase it by more than 300%,typically accompanied by significant pressure-drop penalties.However,an important finding is the general trade-off between enhanced heat transfer and higher frictional loss,which requires optimization depending on the applications.Finally,this review also provides recommendations that will document the gaps of various passive techniques in heat exchangers to future address.展开更多
Nowadays,higher requirements are put forward to the storage and utilization of energy,and supercapacitor is a kind of energy storage electronic devices.The resulting CA-N,with a specific surface area of 320.6 m^(2)/g ...Nowadays,higher requirements are put forward to the storage and utilization of energy,and supercapacitor is a kind of energy storage electronic devices.The resulting CA-N,with a specific surface area of 320.6 m^(2)/g and a pore volume of 0.28 cm^(3)/g,demonstrated a remarkable supercapacitance of 283.3 F/g.As a mesoporous material,CA-N offers numerous channels for the diffusion and absorption of electrolyte ions.Furthermore,it exhibited an impressive capacity retention rate of 98.48% after 5000 charge-discharge cycles.These outstanding electrochemical properties highlight the potential of CA-N for applications in energy storage.展开更多
α-Chiral amides are common in pharmaceuticals,agrochemicals,natural products,and peptides,prompting the need for new synthetic methods.Here,we introduce a nickel-catalyzed asymmetric reductive amidation method to syn...α-Chiral amides are common in pharmaceuticals,agrochemicals,natural products,and peptides,prompting the need for new synthetic methods.Here,we introduce a nickel-catalyzed asymmetric reductive amidation method to synthesizeα-chiral amides from benzyl ammonium salts and isocyanates.The key to success is using a chiral 2,2-bipyridine ligand(-)-Ph-SBpy,enabling high yield(up to 95%)and enantiomeric ratio(up to 98:2 er)under mild conditions.Addition of phenol prevents isocyanate polymerization by reversibly forming a carbamate intermediate,enhancing selectivity and efficiency.The synthetic utility is showcased through transformations of the enantioenriched amides,and the mechanism and enantioselectivity are supported by experimental and computational studies.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and li...Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and limited scalability,making it increasingly difficult to meet the evolving demands of modern society.In this context,recent advancements in machine learning technology have emerged as a promising solution for predicting and monitoring battery states,offering innovative approaches to battery management systems(BMS).By transforming raw operational data into actionable insights,machine learning has shifted the paradigm from reactive to predictive battery safety management,significantly enhancing system reliability and risk mitigation capabilities.This review delves into the implementation of machine learning in battery state prediction,including dataset selection,feature extraction,and model training.It also highlights the latest progress of these models in key applications such as state of health(SOH),state of charge(SOC),thermal runaway warning,fault detection,and remaining useful life(RUL).Finally,we critically examined the challenges and opportunities associated with leveraging machine learning to improve battery safety and performance,providing a comprehensive perspective for future research in this rapidly advancing field.展开更多
Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the e...Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the electronic structure of active sites.This optimization influences the adsorption energy of intermediates,thereby mitigating reaction energy barriers,altering paths,enhancing selectivity,and ultimately improving the catalytic efficiency of electrocatalysts.To elucidate the impact of defects on the electrocatalytic process,we comprehensively outline the roles of various point defects,their synthetic methodologies,and characterization techniques.Importantly,we consolidate insights into the relationship between point defects and catalytic activity for hydrogen/oxygen evolution and CO_(2)/O_(2)/N_(2) reduction reactions by integrating mechanisms from diverse reactions.This underscores the pivotal role of point defects in enhancing catalytic performance.At last,the principal challenges and prospects associated with point defects in current electrocatalysts are proposed,emphasizing their role in advancing the efficiency of electrochemical energy storage and conversion materials.展开更多
Lithium-sulfur batteries(LSBs)have attracted significant attention due to their high theoretical energy density and low-cost raw materials.However,LSBs still face various challenges in practical applications,particula...Lithium-sulfur batteries(LSBs)have attracted significant attention due to their high theoretical energy density and low-cost raw materials.However,LSBs still face various challenges in practical applications,particularly the shuttle effect,electrode passivation,and slow kinetics.In recent years,trisulfur radicals(TRs),important intermediates in LSBs,have emerged as a promising and beyond-traditional solution to these problems,which serves as a mediated catalyst to improve the electrochemical performance of LSBs.As a system that is inconsistent with the catalytic conversion process discussed in the traditional LSBs,this review focuses on the generation,detection,promotion,and catalytic roles of TRs,especially emphasizing the formation of TRs in solid-state lapis lazuli analogs and discussing the pros and cons of high donor number solvents and/or their co-solvents in stabilizing TRs.Strategies involving homogeneous/heterogeneous catalysts are discussed for increment of TRs and enhancing catalytic reactions in LSBs.Ultimately,given TRs’significant potential as a key factor in enhancing the performance of LSBs,future perspectives and outlooks are provided to guide the further development of TRs in LSBs.This review provides valuable insights into the design of electrolytes and catalysts for increment of TRs,paving the new practical direction and way for advanced LSBs.展开更多
Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emp...Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.展开更多
Transition metal carbides,known as MXenes,particularly Ti_(3)C_(2)T_(x),have been extensively explored as promising materials for electrochemical reactions.However,transition metal carbonitride MXenes with high nitrog...Transition metal carbides,known as MXenes,particularly Ti_(3)C_(2)T_(x),have been extensively explored as promising materials for electrochemical reactions.However,transition metal carbonitride MXenes with high nitrogen content for electrochemical reactions are rarely reported.In this work,transition metal carbonitride MXenes incorporated with Pt-based electrocatalysts,ranging from single atoms to sub-nanometer dimensions,are explored for hydrogen evolution reaction(HER).The fabricated Pt clusters/MXene catalyst exhibits superior HER performance compared to the single-atom-incorporated MXene and commercial Pt/C catalyst in both acidic and alkaline electrolytes.The optimized sample shows low overpotentials of 28,65,and 154 mV at a current densities of 10,100,and 500 m A cm^(-2),a small Tafel slope of 29 m V dec^(-1),a high mass activity of 1203 mA mgPt^(-1)and an excellent turnover frequency of 6.1 s^(-1)in the acidic electrolyte.Density functional theory calculations indicate that this high performance can be attributed to the enhanced active sites,increased surface functional groups,faster charge transfer dynamics,and stronger electronic interaction between Pt and MXene,resulting in optimized hydrogen absorption/desorption toward better HER.This work demonstrates that MXenes with a high content of nitrogen may be promising candidates for various catalytic reactions by incorporating single atoms or clusters.展开更多
Noble metal-loaded layered hydroxides exhibit high efficiency in electrocatalyzing water splitting.However,their widespread use as bifunctional electrocatalysts is hindered by low metal loading,inefficient yield,and c...Noble metal-loaded layered hydroxides exhibit high efficiency in electrocatalyzing water splitting.However,their widespread use as bifunctional electrocatalysts is hindered by low metal loading,inefficient yield,and complex synthesis processes.In this work,platinum atoms were anchored onto nickel-iron layered double hydroxide/carbon nanotube(LDH/CNT)hybrid electrocatalysts by using a straightforward milling technique with K_(2)Pt Cl_(6)·6H_(2)O as the Pt source.By adjusting the Pt-to-Fe ratio to 1/2 and 1/10,excellent electrocatalysts—Pt_(1/6)-Ni_(2/3)Fe_(1/3)-LDH/CNT and Pt_(1/30)-Ni_(2/3)Fe_(1/3)-LDH/CNT—were achieved with superior performance in hydrogen evolution reaction(HER)and oxygen evolution reaction(OER),outperforming the corresponding commercial Pt/C(20 wt%)and Ru O_(2)electrocatalysts.The enhanced electrochemical performance is attributed to the modification of Pt's electronic structure,which exhibits electron-rich states for HER and electrondeficient states for OER,significantly boosting Pt's electrochemical activity.Furthermore,the simple milling technology for controlling Pt loading offers a promising approach for scaling up the production of electrocatalysts.展开更多
This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine lear...This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine learning models,multimodal data fusion,federated learning(FL),and Karush-Kuhn-Tucker(KKT)-based resource optimization.The systemfuses data fromwearable sensors and cameras using Gramian Angular Field(GAF)encoding to capture rich spatial-temporal features.To protect sensitive data,we adopt a privacy-preserving FL setup,where model training occurs locally on client devices without transferring raw data.A custom convolutional neural network(CNN)is designed to extract robust features from the fused multimodal inputs under FL constraints.To further improve efficiency,a KKT-based optimization strategy is employed to allocate computational tasks based on device capacity.Evaluated on the UP-Fall dataset,the proposed system achieves 91%accuracy,demonstrating its effectiveness in detecting human falls while ensuring data privacy and resource efficiency.This work contributes to safer,scalable,and real-world-applicable fall detection for elderly care.展开更多
Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This ...Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.展开更多
Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse,triboelectric and piezoelectric nanogenerators(TENG&PENG)have attracted considerably increasing attention.The...Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse,triboelectric and piezoelectric nanogenerators(TENG&PENG)have attracted considerably increasing attention.Their outstanding characteristics,such as self-powered ability,high output performance,integration compatibility,cost-effectiveness,simple configurations,and versatile operation modes,could effectively expand the lifetime of vastly distributed wearable,implantable,and environmental devices,eventually achieving self-sustainable,maintenance-free,and reliable systems.However,current triboelectric/piezoelectric based active(i.e.self-powered)sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output.This work systematically summarizes and evaluates the recent research endeavors to address the above challenges,with detailed discussions on the challenge origins,designing strategies,device performance,and corresponding diverse applications.Finally,conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided,proposing the necessity of future research development in this field.展开更多
文摘Electrochemical synthesis of value-added chemicals represents a promising approach to address multidisciplinary demands.This technology establishes direct pathways for electricity-to-chemical conversion while significantly reducing the carbon footprint of chemical manufacturing.It simultaneously optimizes chemical energy storage and grid management,offering sustainable solutions for renewable energy utilization and overcoming geographical constraints in energy distribution.As a critical nexus between renewable energy and green chemistry,electrochemical synthesis serves dual roles in energy transformation and chemical production,emerging as a vital component in developing carbon-neutral circular economies.Focusing on key small molecules(H_(2)O,CO_(2),N_(2),O_(2)),this comment examines fundamental scientific challenges and practical barriers in electrocatalytic conversion processes,bridging laboratory innovations with industrial-scale implementation.
基金supported by the Deanship of Research at the King Fahd University of Petroleum&Minerals,Dhahran,31261,Saudi Arabia,under Project No.EC241001.
文摘Various factors,including weak tie-lines into the electric power system(EPS)networks,can lead to low-frequency oscillations(LFOs),which are considered an instant,non-threatening situation,but slow-acting and poisonous.Considering the challenge mentioned,this article proposes a clustering-based machine learning(ML)framework to enhance the stability of EPS networks by suppressing LFOs through real-time tuning of key power system stabilizer(PSS)parameters.To validate the proposed strategy,two distinct EPS networks are selected:the single-machine infinite-bus(SMIB)with a single-stage PSS and the unified power flow controller(UPFC)coordinated SMIB with a double-stage PSS.To generate data under various loading conditions for both networks,an efficient but offline meta-heuristic algorithm,namely the grey wolf optimizer(GWO),is used,with the loading conditions as inputs and the key PSS parameters as outputs.The generated loading conditions are then clustered using the fuzzy k-means(FKM)clustering method.Finally,the group method of data handling(GMDH)and long short-term memory(LSTM)ML models are developed for clustered data to predict PSS key parameters in real time for any loading condition.A few well-known statistical performance indices(SPI)are considered for validation and robustness of the training and testing procedure of the developed FKM-GMDH and FKM-LSTM models based on the prediction of PSS parameters.The performance of the ML models is also evaluated using three stability indices(i.e.,minimum damping ratio,eigenvalues,and time-domain simulations)after optimally tuned PSS with real-time estimated parameters under changing operating conditions.Besides,the outputs of the offline(GWO-based)metaheuristic model,proposed real-time(FKM-GMDH and FKM-LSTM)machine learning models,and previously reported literature models are compared.According to the results,the proposed methodology outperforms the others in enhancing the stability of the selected EPS networks by damping out the observed unwanted LFOs under various loading conditions.
基金supported by the National Key R&D Program of China(Grant Nos.2020YFA0711500 and 2020YFA0711503)the National Natural Science Foundation of China(Grant Nos.T2488302,T2342010,52076127)+5 种基金the Natural Science Foundation of Shanghai(Grant Nos.20ZR1471700,22JC1401800,and 24Z511405472)the State Key Laboratory of Mechanical System and Vibration(Grant Nos.MSVZD202211,MSVZD202301,and MSVZD202401)Shanghai Jiao Tong University 2030 InitiativeShanghai Jiao Tong University Si Yuan Scholar Programthe Student Innovation Center and the Instrumental Analysis Center at Shanghai Jiao Tong Universitysupport by Shanghai Jiao Tong University 2030 Initiative。
文摘The electrocaloric(EC)effect refers to the change in the polarization entropy and/or temperature of dielectric materials when an electric field is applied and removed.EC refrigeration has received increasing interest as an alternative to conventional refrigeration technologies because it provides both high energy efficiency and zero global warming potential.In this review,we first introduce the thermodynamic fundamentals of the EC effect and the mechanism of EC refrigeration cycles.We then present recent advances in EC cooling technologies,from material improvements to device demonstrations,including a critical analysis of existing material and device characterization methodologies and a discussion of how to reliably measure the parameters of materials and devices.Finally,the current challenges and possible future prospects for EC cooling technology are outlined.
基金supported by the National Natural Science Foundation of China (Grant Nos.T2325004 and 52161160330)the National Natural Science Foundation of China (Grants No.12504233)+2 种基金Advanced MaterialsNational Science and Technology Major Project (Grant No.2024ZD0606900)the Talent Hub for “AI+New Materials” Basic Researchthe Key Research and Development Program of Ningbo (Grant No.2025Z088)。
文摘The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.
基金the financial support provided by Universiti Putra Malaysiasupported by the Matching Grant(9300489).
文摘This review draws attention to the innovative use of arrowroot(Maranta arundinacea)fiber as a unique and underutilized biomass source for nanocrystalline cellulose(NCC)-based nanocomposites,presenting a noteworthy alternative to extensively researched materials like wood pulp,bacterial cellulose,and chemically modified NCCs.In contrast to traditional sources,arrowroot possesses a naturally elevated cellulose and diminished lignin content,facilitating more effective NCC extraction requiring reduced chemical input and enabling environmentally friendly processing techniques.The review evaluates the performance of arrowroot-derived nanocomposites against systems documented in the literature,including NCC-based shape memory composites and nanoparticle-reinforced films,demonstrating enhanced tensile strength,improved moisture barrier properties,and thermal stability,as well as potential piezoelectric response.This study recognizes arrowroot as a viable option in the biomass-based nanocellulose sector,providing ecological and functional benefits while tackling significant issues such as process scalability and feedstock variability,thereby offering important insights for the advancement of sustainable materials.
基金supported by National Natural Science Foundation of China(Grant Nos.52025055,52375576,52350349)Key Research and Development Program of Shaanxi(Program No.2022GXLH-01-12)+2 种基金Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B03012304)Aeronautical Science Foundation of China(No.2022004607001)the Fundamental Research Funds for the Central Universities(No.xtr072024031).
文摘Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.
文摘Heat exchangers play a crucial role in thermal energy systems,with their performance directly impacting efficiency,cost,and environmental impact.Apowerful technique for performance improvement can be given by passive enhancement strategies,which are characterized by their dependability and minimal external power requirements.This comprehensive review critically assesses recent advancements in such passive methods to evaluate their heat transfer mechanisms,performance characteristics,and practical implementation challenges.Our methodology involves a systematic and comprehensive analysis of various heat transfer enhancement techniques,including surface modifications,extended surfaces,swirl flow devices,and tube inserts.This approach synthesizes and integrates findings from a broad spectrum of experimental investigations and numerical simulations to establish a cohesive understanding of their performance characteristics and underlyingmechanisms.Based on the findings,passive heat transfer techniques result in significant improvements in thermal performance;for instance,corrugated and roughened surfaces increase the heat transfer coefficient by 50%–200%,and advanced insert geometries,such as modified twisted tapes,can increase it by more than 300%,typically accompanied by significant pressure-drop penalties.However,an important finding is the general trade-off between enhanced heat transfer and higher frictional loss,which requires optimization depending on the applications.Finally,this review also provides recommendations that will document the gaps of various passive techniques in heat exchangers to future address.
基金supported by Shenzhen Science and Technology Program(No.JCYJ20240813103608012)State Key Laboratory of New Textile Materials andAdvanced Processing Technologies(No.FZ2024019)National Natural Science Foundation of China(No.22104117).
文摘Nowadays,higher requirements are put forward to the storage and utilization of energy,and supercapacitor is a kind of energy storage electronic devices.The resulting CA-N,with a specific surface area of 320.6 m^(2)/g and a pore volume of 0.28 cm^(3)/g,demonstrated a remarkable supercapacitance of 283.3 F/g.As a mesoporous material,CA-N offers numerous channels for the diffusion and absorption of electrolyte ions.Furthermore,it exhibited an impressive capacity retention rate of 98.48% after 5000 charge-discharge cycles.These outstanding electrochemical properties highlight the potential of CA-N for applications in energy storage.
基金the National Natural Science Foundation of China(Nos.22150410339,W2432012,22301233 and 22171218)the Ministry of Science and Technology China(No.wgxz2022188)。
文摘α-Chiral amides are common in pharmaceuticals,agrochemicals,natural products,and peptides,prompting the need for new synthetic methods.Here,we introduce a nickel-catalyzed asymmetric reductive amidation method to synthesizeα-chiral amides from benzyl ammonium salts and isocyanates.The key to success is using a chiral 2,2-bipyridine ligand(-)-Ph-SBpy,enabling high yield(up to 95%)and enantiomeric ratio(up to 98:2 er)under mild conditions.Addition of phenol prevents isocyanate polymerization by reversibly forming a carbamate intermediate,enhancing selectivity and efficiency.The synthetic utility is showcased through transformations of the enantioenriched amides,and the mechanism and enantioselectivity are supported by experimental and computational studies.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported by the National Key Research and Development Program of China(No.2021YFF0500600)Natural Science Foundation of Henan Province(No.252300421176)+1 种基金National Natural Science Foundation of China(No.22478361 and No.22108256)Frontier Exploration Projects of Longmen Laboratory(No.LMQYTSKT021)。
文摘Batteries play a crucial role in the storage and application of sustainable energy,yet their inherent safety risks are non-negligible.Traditional monitoring methods often suffer from high costs,time consumption,and limited scalability,making it increasingly difficult to meet the evolving demands of modern society.In this context,recent advancements in machine learning technology have emerged as a promising solution for predicting and monitoring battery states,offering innovative approaches to battery management systems(BMS).By transforming raw operational data into actionable insights,machine learning has shifted the paradigm from reactive to predictive battery safety management,significantly enhancing system reliability and risk mitigation capabilities.This review delves into the implementation of machine learning in battery state prediction,including dataset selection,feature extraction,and model training.It also highlights the latest progress of these models in key applications such as state of health(SOH),state of charge(SOC),thermal runaway warning,fault detection,and remaining useful life(RUL).Finally,we critically examined the challenges and opportunities associated with leveraging machine learning to improve battery safety and performance,providing a comprehensive perspective for future research in this rapidly advancing field.
基金supported by the National Natural Science Foundation of China(U21A20281)the Special Fund for Young Teachers from Zhengzhou University(JC23557030,JC23257011)+1 种基金the Key Research Projects of Higher Education Institutions of Henan Province(24A530009)the Project of Zhongyuan Critical Metals Laboratory(GJJSGFYQ202336).
文摘Point defect engineering endows catalysts with novel physical and chemical properties,elevating their electrocatalytic efficiency.The introduction of defects emerges as a promising strategy,effectively modifying the electronic structure of active sites.This optimization influences the adsorption energy of intermediates,thereby mitigating reaction energy barriers,altering paths,enhancing selectivity,and ultimately improving the catalytic efficiency of electrocatalysts.To elucidate the impact of defects on the electrocatalytic process,we comprehensively outline the roles of various point defects,their synthetic methodologies,and characterization techniques.Importantly,we consolidate insights into the relationship between point defects and catalytic activity for hydrogen/oxygen evolution and CO_(2)/O_(2)/N_(2) reduction reactions by integrating mechanisms from diverse reactions.This underscores the pivotal role of point defects in enhancing catalytic performance.At last,the principal challenges and prospects associated with point defects in current electrocatalysts are proposed,emphasizing their role in advancing the efficiency of electrochemical energy storage and conversion materials.
基金supported by the National Key Research and Development Program of China(No.2021YFF0500600)National Natural Science Foundation of China(No.22309165),Natural Science Foundation of Henan Province(No.232300420296)Key Scientific Research Project Plan of Henan Provincial Higher Education Institutions(No.25B430006).
文摘Lithium-sulfur batteries(LSBs)have attracted significant attention due to their high theoretical energy density and low-cost raw materials.However,LSBs still face various challenges in practical applications,particularly the shuttle effect,electrode passivation,and slow kinetics.In recent years,trisulfur radicals(TRs),important intermediates in LSBs,have emerged as a promising and beyond-traditional solution to these problems,which serves as a mediated catalyst to improve the electrochemical performance of LSBs.As a system that is inconsistent with the catalytic conversion process discussed in the traditional LSBs,this review focuses on the generation,detection,promotion,and catalytic roles of TRs,especially emphasizing the formation of TRs in solid-state lapis lazuli analogs and discussing the pros and cons of high donor number solvents and/or their co-solvents in stabilizing TRs.Strategies involving homogeneous/heterogeneous catalysts are discussed for increment of TRs and enhancing catalytic reactions in LSBs.Ultimately,given TRs’significant potential as a key factor in enhancing the performance of LSBs,future perspectives and outlooks are provided to guide the further development of TRs in LSBs.This review provides valuable insights into the design of electrolytes and catalysts for increment of TRs,paving the new practical direction and way for advanced LSBs.
文摘Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.
基金the final support of ARC DP220103045the startup support of KFUPMPrince Sultan University for their support。
文摘Transition metal carbides,known as MXenes,particularly Ti_(3)C_(2)T_(x),have been extensively explored as promising materials for electrochemical reactions.However,transition metal carbonitride MXenes with high nitrogen content for electrochemical reactions are rarely reported.In this work,transition metal carbonitride MXenes incorporated with Pt-based electrocatalysts,ranging from single atoms to sub-nanometer dimensions,are explored for hydrogen evolution reaction(HER).The fabricated Pt clusters/MXene catalyst exhibits superior HER performance compared to the single-atom-incorporated MXene and commercial Pt/C catalyst in both acidic and alkaline electrolytes.The optimized sample shows low overpotentials of 28,65,and 154 mV at a current densities of 10,100,and 500 m A cm^(-2),a small Tafel slope of 29 m V dec^(-1),a high mass activity of 1203 mA mgPt^(-1)and an excellent turnover frequency of 6.1 s^(-1)in the acidic electrolyte.Density functional theory calculations indicate that this high performance can be attributed to the enhanced active sites,increased surface functional groups,faster charge transfer dynamics,and stronger electronic interaction between Pt and MXene,resulting in optimized hydrogen absorption/desorption toward better HER.This work demonstrates that MXenes with a high content of nitrogen may be promising candidates for various catalytic reactions by incorporating single atoms or clusters.
基金supported by the Natural Science Foundation of Henan(242300421230)the Young Teacher Fundamental Research Cultivation Program of Zhengzhou University(JC23557030)the National Natural Science Foundation of China(U21A20281 and 22208322)。
文摘Noble metal-loaded layered hydroxides exhibit high efficiency in electrocatalyzing water splitting.However,their widespread use as bifunctional electrocatalysts is hindered by low metal loading,inefficient yield,and complex synthesis processes.In this work,platinum atoms were anchored onto nickel-iron layered double hydroxide/carbon nanotube(LDH/CNT)hybrid electrocatalysts by using a straightforward milling technique with K_(2)Pt Cl_(6)·6H_(2)O as the Pt source.By adjusting the Pt-to-Fe ratio to 1/2 and 1/10,excellent electrocatalysts—Pt_(1/6)-Ni_(2/3)Fe_(1/3)-LDH/CNT and Pt_(1/30)-Ni_(2/3)Fe_(1/3)-LDH/CNT—were achieved with superior performance in hydrogen evolution reaction(HER)and oxygen evolution reaction(OER),outperforming the corresponding commercial Pt/C(20 wt%)and Ru O_(2)electrocatalysts.The enhanced electrochemical performance is attributed to the modification of Pt's electronic structure,which exhibits electron-rich states for HER and electrondeficient states for OER,significantly boosting Pt's electrochemical activity.Furthermore,the simple milling technology for controlling Pt loading offers a promising approach for scaling up the production of electrocatalysts.
基金supported by King Fahd University of Petroleum&Minerals,Dhahran,31261,SaudiArabiaTheauthors at KFUP Macknowledge the Interdisciplinary Research Center for Intelligent Secure Systems(IRC-ISS)for the support received under Grant No.INSS2516.
文摘This article presents a human fall detection system that addresses two critical challenges:privacy preservation and detection accuracy.We propose a comprehensive framework that integrates state-of-the-art machine learning models,multimodal data fusion,federated learning(FL),and Karush-Kuhn-Tucker(KKT)-based resource optimization.The systemfuses data fromwearable sensors and cameras using Gramian Angular Field(GAF)encoding to capture rich spatial-temporal features.To protect sensitive data,we adopt a privacy-preserving FL setup,where model training occurs locally on client devices without transferring raw data.A custom convolutional neural network(CNN)is designed to extract robust features from the fused multimodal inputs under FL constraints.To further improve efficiency,a KKT-based optimization strategy is employed to allocate computational tasks based on device capacity.Evaluated on the UP-Fall dataset,the proposed system achieves 91%accuracy,demonstrating its effectiveness in detecting human falls while ensuring data privacy and resource efficiency.This work contributes to safer,scalable,and real-world-applicable fall detection for elderly care.
基金funded by King Fahd University of Petroleum&Minerals,Saudi Arabia under IRC-SES grant#INRE 2217.
文摘Wind energy has emerged as a potential replacement for fossil fuel-based energy sources.To harness maximum wind energy,a crucial decision in the development of an efficient wind farm is the optimal layout design.This layout defines the specific locations of the turbines within the wind farm.The process of finding the optimal locations of turbines,in the presence of various technical and technological constraints,makes the wind farm layout design problem a complex optimization problem.This problem has traditionally been solved with nature-inspired algorithms with promising results.The performance and convergence of nature-inspired algorithms depend on several parameters,among which the algorithm termination criterion plays a crucial role.Timely convergence is an important aspect of efficient algorithm design because an inefficient algorithm results in wasted computational resources,unwarranted electricity consumption,and hardware stress.This study provides an in-depth analysis of several termination criteria while using the genetic algorithm as a test bench,with its application to the wind farm layout design problem while considering various wind scenarios.The performance of six termination criteria is empirically evaluated with respect to the quality of solutions produced and the execution time involved.Due to the conflicting nature of these two attributes,fuzzy logic-based multi-attribute decision-making is employed in the decision process.Results for the fuzzy decision approach indicate that among the various criteria tested,the criterion Phi achieves an improvement in the range of 2.44%to 32.93%for wind scenario 1.For scenario 2,Best-worst termination criterion performed well compared to the other criteria evaluated,with an improvement in the range of 1.2%to 9.64%.For scenario 3,Hitting bound was the best performer with an improvement of 1.16%to 20.93%.
基金supported by the National Key R&D Program of China(Grant Nos.2022YFB3603403,2021YFB3600502)the National Natural Science Foundation of China(Grant Nos.62075040,62301150)+3 种基金the Southeast University Interdisciplinary Research Program for Young Scholars(2024FGC1007)the Start-up Research Fund of Southeast University(RF1028623164)the Nanjing Science and Technology Innovation Project for Returned Overseas Talent(4206002302)the Fundamental Research Funds for the Central Universities(2242024K40015).
文摘Benefiting from the widespread potential applications in the era of the Internet of Thing and metaverse,triboelectric and piezoelectric nanogenerators(TENG&PENG)have attracted considerably increasing attention.Their outstanding characteristics,such as self-powered ability,high output performance,integration compatibility,cost-effectiveness,simple configurations,and versatile operation modes,could effectively expand the lifetime of vastly distributed wearable,implantable,and environmental devices,eventually achieving self-sustainable,maintenance-free,and reliable systems.However,current triboelectric/piezoelectric based active(i.e.self-powered)sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output.This work systematically summarizes and evaluates the recent research endeavors to address the above challenges,with detailed discussions on the challenge origins,designing strategies,device performance,and corresponding diverse applications.Finally,conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided,proposing the necessity of future research development in this field.
