The osteochondral(OC)interface exhibits a mineral gradient,varying in thickness by several hundred micrometers across different species.Disruptions in this interface damage OC tissues,leading to osteoarthritis.The nat...The osteochondral(OC)interface exhibits a mineral gradient,varying in thickness by several hundred micrometers across different species.Disruptions in this interface damage OC tissues,leading to osteoarthritis.The natural architecture and composition of native OC interfaces can be replicated using biomaterial scaffolds via regenerative engineering approaches.A novel one-step bioextrusion process was employed to fabricate a unitary synthetic graft(USG),which mimics the native OC interface’s mineral concentration gradient.This novel USG is composed of an agarose-based cartilage layer and a bone layer,consisting of agarose enriched with 20%(200 g/L)hydroxyapatite.The USG features a gradient interface with mineral concentrations transitioning from 0%to 20%(mass fraction),mimicking the transition between the cartilage and bone.Thermogravimetric analysis revealed that the gradient transition lengths of the graft and native OC tissue harvested from bovine knees were similar((647±21)vs.(633±124)μm).The linear viscoelastic properties of the grafts,which were evaluated using strain sweep and frequency sweep tests with oscillatory shear,indicated a dominant storage modulus over loss modulus similar to that of native OC tissues.The compressive and stress relaxation behaviors of the USGs demonstrated that the graft maintained structural integrity under mechanical stress.Viability assays performed after bioextrusion showed that chondrocytes and human fetal osteoblast cells successfully integrated and survived within their designated regions of the graft.The novel USGs exhibit properties similar to native OC tissue and are promising candidates for regenerating OC defects and restoring knee joint functionality.展开更多
Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properti...Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properties.A further aspect of LN’s versatility stems from the ability to engineer ferroelectric domains with micro and even nano-scale precision in LN,which provides an additional degree of freedom to design acoustic and optical devices with improved performance and is only possible in a handful of other materials.In this review paper,we provide an overview of the domain engineering techniques developed for LN,their principles,and the typical domain size and pattern uniformity they provide,which is important for devices that require high-resolution domain patterns with good reproducibility.It also highlights each technique's benefits,limitations,and adaptability for an application,along with possible improvements and future advancement prospects.Further,the review provides a brief overview of domain visualization methods,which is crucial to gain insights into domain quality/shape and explores the adaptability of the proposed domain engineering methodologies for the emerging thin-film lithium niobate on an insulator platform,which creates opportunities for developing the next generation of compact and scalable photonic integrated circuits and high frequency acoustic devices.展开更多
Rechargeable aqueous zinc-ion batteries(AZIBs)exhibit appreciable potential in the domain of electrochemical energy storage.However,there are serious challenges for AZIBs,for instance zinc dendrite growth,hydrogen evo...Rechargeable aqueous zinc-ion batteries(AZIBs)exhibit appreciable potential in the domain of electrochemical energy storage.However,there are serious challenges for AZIBs,for instance zinc dendrite growth,hydrogen evolution reaction(HER),and corrosion side reactions.Herein,we propose a surface engineering modification strategy for coating the montmorillonite(MMT)layer onto the surface of the Zn anode to tackle these issues,thereby achieving high cycling stability for rechargeable AZIBs.The results reveal that the MMT layer on the surface of the Zn anode is able to provide ordered zincophilic channels for zinc ions migration,facilitating the reaction kinetics of zinc ions.Density functional theory(DFT)calculations and water contact angle(CA)tests prove that MMT@Zn anode exhibits superior adsorption capacity for Zn^(2+)and better hydrophobicity than the bare Zn anode,thereby achieving excellent cycling stability.Moreover,the MMT@Zn||MMT@Zn symmetric cell holds the stable cycling over 5600 h at 0.5 mA cm^(-2)and 0.125 m A h cm^(-2),even exceeding 1800 h long cycling under harsh conditions of 5 m A cm^(-2)and 1.25 m A h cm^(-2).The MMT@Zn||V_(2)O_(5)full cell reaches over 3000 cycles at 2 A g^(-1)with excellent rate capability.Therefore,this surface engineering modification strategy for enhancing the electrochemical performance of AZIBs represents a promising application.展开更多
This study focused on the production of polypropylene(PP)/silver(Ag)composites via additive manufacturing.This study aimed to enhance the quality of medical-grade PP in material extrusion(MEX)three-dimensional printin...This study focused on the production of polypropylene(PP)/silver(Ag)composites via additive manufacturing.This study aimed to enhance the quality of medical-grade PP in material extrusion(MEX)three-dimensional printing(3DP)by improving its mechanical properties while simultaneously adding antibacterial properties.The latter can find extremely important and versatile properties that are applicable in defense and security domains.PP/Ag nanocomposites were prepared using a novel method based on a reaction occurring while mixing appropriate quantities of the starting polymers and additives,namely polyvinylpyrrolidone(PVP)as the matrix material and silver nitrate(AgNO_(3))as the filler.This process produced three-dimensional(3D)printed filaments,which were then used to create specimens for a series of standardized tests.It was found that the mechanical properties of the nanocomposites were enhanced in relation to pristine PP,especially for the PP matrix with various loadings of AgNO_(3)and PVP,such as 5.0 wt%and 2.5 wt%,respectively.The voids,inclusions,and actual-to-nominal dimensions also showed improved results.The 3DP specimens exhibited a more effective biocidal performance against Staphylococcus aureus than Escherichia coli,which developed an inhibition zone only in the case of PP with filler loading percentages of AgNO_(3)and PVP at 10.0 wt%and 5.0 wt%,respectively Compounds possessing such properties can be beneficial for various applications requiring increased mechanical properties and biocidal capabilities,such as in the Defence or medical industries.展开更多
This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene...This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.展开更多
Self-Centering Piston-Based Braced Frames(SC-PBBFs)are designed to curtail structural damage under severe ground motions.The self-centering mechanism in this bracing mitigates structural damage during an earthquake,th...Self-Centering Piston-Based Braced Frames(SC-PBBFs)are designed to curtail structural damage under severe ground motions.The self-centering mechanism in this bracing mitigates structural damage during an earthquake,thereby reducing post-earthquake repair costs and contributing to seismic resilience.However,non-structural components,particularly those sensitive to floor acceleration,remain vulnerable,resulting in prolonged func-tional recovery times.This paper aims to address this limitation by introducing a novel structural archetype,the Self-Centering Viscous-Based Braced Frame(SC-VBBF),which integrates superelastic shape memory alloy(SMA)bars,viscous dampers(VDs),and friction springs(FSs).A streamlined analytical approach relies on the strength decoupling of VD from other components using aλfactor to design SC-VBBFs.To evaluate the effectiveness of the hybrid brace,a set of 4-,8-,and 12-story archetypes equipped with SC-PBBs and SC-VBBFs are simulated in OpenSees and analyzed under various earthquake types,including crustal,subcrustal,and subduction events.The results demonstrate the superior performance of the SC-VBBF withλ≤0.5 system compared to SC-PBBFs in mitigating floor accelerations under design-level earthquakes and improving seismic resilience.展开更多
The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technol...The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technolo-gies have been unable to eliminate these pollutants,resulting in their ongoing release into aquatic ecosystems.This study focuses on cloperastine(CPS),a cough suppressant and antihistamine medication.The environmental impact of CPS usage has become a concern,mainly due to its increased detection during the COVID-19 pandemic.CPS has been found in wastewater treatment facilities,effluents from senior living residences,river waters,and sewage sludge.However,the photosensitivity of CPS and its photodegradation profile remain largely unknown.This study investigates the photodegradation process of CPS under simulated tertiary treatment conditions using UV photolysis,a method commonly applied in some wastewater treatment plants.Several transformation prod-ucts were identified,evaluating their kinetic profiles using chemometric approaches(i.e.,curve fitting and the hard-soft multivariate curve resolution-alternating least squares(HS-MCR-ALS)algorithm)and calculating the reaction quantum yield.As a result,three different transformation products have been detected and correctly identified.In addition,a comprehensive description of the kinetic pathway involved in the photodegradation process of the CPS drug has been provided,including observed kinetic rate constants.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
The dual-probe heat pulse(DPHP)is a well-established method for estimating soil moisture(θ)using soil thermal conductivity(λ)and volumetric heat capacity(C_(v)).Recently,monitoringθhas been improved by integrating ...The dual-probe heat pulse(DPHP)is a well-established method for estimating soil moisture(θ)using soil thermal conductivity(λ)and volumetric heat capacity(C_(v)).Recently,monitoringθhas been improved by integrating the DPHP method with distributed temperature sensing(DTS)technology.In the DPHP-DTS approach,a single fiber optic(FO)cable with embedded metallic constituents functions as a heating element,while a parallel cable serves to monitor the temperature.Despite ongoing advancements,challenges such as the difficulty in positioning heating and sensing cables and high energy requirements hinder the widespread adoption of the DPHP-DTS method.As alternative heating materials are seldom used,this study evaluated the feasibility of employing a resistive metallic alloy as the heating element in a laboratory DPHP-DTS application.Overall,higher errors were observed when assessing C_(v)andλat higherθvalues(>0.2),but using C_(v)data produced more accurateθestimates(with the root mean square error(RMSE)≤0.06).Based on C_(v)values,a low-power,long-duration heat pulse(8.07 W/m for 300 s)yielded more consistentθestimates(RMSE=0.04)than a high-power,shortduration pulse(15.93 W/m for 180 s,with RMSE=0.06).The findings of this study also indicated that variations in heating uniformity and electrical power fluctuations potentially affected measurement accuracy.Nevertheless,the resistive alloy proved advantageous for DPHP-DTS due to its independent power connection,ability to maintain linear positioning within the soil,and potential for energy savings,all while providing reliableθestimates.展开更多
Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of mul...Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.展开更多
The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulne...The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks,with potentially severe consequences.To mitigate this threat,we present a machine learning-driven framework for real-time GPS spoofing detection,designed with a balance of detection accuracy and computational efficiency.Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data,enabling robust and generalizable model development.A comprehensive evaluation ofmultiple classification algorithms identifies XGBoost as the superior performer,achieving 93.07% accuracy alongside outstanding precision,recall,and F1-scores.Beyond standard classification metrics,our assessment encompasses ROC-AUC,detection latency,and false positive rate,providing a comprehensive assessment of performance.This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks,supported by a detailed methodology,a comprehensive evaluation including inference-time latency,and a publicly available dataset.展开更多
The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonm...The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.展开更多
As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processin...As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.展开更多
Sandwich functionally graded(FG)auxetic beams are extensively utilized in aerospace,automotive,and biomedical industries due to their excellent strength-toweight ratio,impact resistance,and tunable mechanical properti...Sandwich functionally graded(FG)auxetic beams are extensively utilized in aerospace,automotive,and biomedical industries due to their excellent strength-toweight ratio,impact resistance,and tunable mechanical properties.The integration of FG materials with auxetic structures enhances their adaptability in advanced engineering applications.However,understanding their dynamic behavior under external excitations is essential for optimal design and structural reliability.Nonlinear interactions in such structures pose significant challenges in vibration analysis,necessitating robust analytical methods.This study presents a closed-form solution for the nonlinear forced vibration analysis of sandwich FG auxetic beams,offering an accurate and efficient method for predicting their dynamic response.The beam consists of two FG face sheets with material properties varying through the thickness and a re-entrant honeycomb auxetic core with an adjustable Poisson's ratio.The governing nonlinear equations of motion are derived using the first-order shear deformation theory(FSDT),the modified Gibson model,and the von Kármán relations,formulated through Hamilton's principle.A closed-form solution is obtained via the Galerkin method and multiple-scale technique.The results demonstrate that FG layers enable control of the overweight and dynamic response amplitude,with positive power law indexes reducing weight.Comparisons with finite element results confirm the accuracy of the proposed formulation.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.B...Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.Based on the Joule effect,the solid carbon sources can be rapidly heated to ultra-high temperatures(>3000 K)through instantaneous high-energy current pulses during FJH,thus driving the rapid rearrangement and graphitization of carbon atoms.This technology demonstrates numerous advantages,such as solvent-and catalyst-free features,high energy conversion efficiency,and a short process cycle.In this review,we have systematically summarized the technology principle and equipment design for FJH,as well as its raw materials selection and pretreatment strategies.The research progress in the FJH synthesis of flash graphene,carbon nanotubes,graphene fibers,and anode hard carbon,as well as its by-products,is also presented.FJH can precisely optimize the microstructures of carbon materials(e.g.,interlayer spacing of turbostratic graphene,defect concentration,and heteroatom doping)by regulating its operation parameters like flash voltage and flash time,thereby enhancing their performances in various applications,such as composite reinforcement,metal-ion battery electrodes,supercapacitors,and electrocatalysts.However,this technology is still challenged by low process yield,macroscopic material uniformity,and green power supply system construction.More research efforts are also required to promote the transition of FJH from laboratory to industrial-scale applications,thus providing innovative solutions for advanced carbon materials manufacturing and waste management toward carbon neutrality.展开更多
The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study intro...The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study introduces a molten salt electrochemical strategy for converting photovoltaic wSi into NiSi_(2)-silicon nanorods(NiSi_(2)-SiNRs)as high-performance anode materials for lithium-ion batteries.A stable oxidized passivation layer is formed on the wSi surface via controlled oxidation,and further in situ generated highly active NiSi_(2) droplets.The molten salt electric field modulates the surface energy of silicon,while particle integration drives localized directional growth,enabling the self-assembly of NiSi_(2)-SiNRs composites.These NiSi_(2)-SiNRs anodes exhibit rapid ion transport and effective strain buffering.The high aspect ratio of SiNRs and the presence of retained NiSi_(2) facilitate both longitudinal and transverse Li^(+) diffusion.Owing to their robust structural design,the NiSi_(2)-SiNRs anode achieves an excellent initial Coulombic efficiency of 91.61%and retains 72.99%of its capacity after 800 cycles at 2 A·g^(−1).This study establishes a model system for investigating silicide/silicon interfaces in molten salt electrochemical synthesis and provides an effective strategy for upcycling photovoltaic wSi into high-performance lithium-ion battery anodes.展开更多
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
TiB_(2)coatings can significantly enhance the high-temperature oxidation resistance of molybdenum,which would broaden the application range of molybdenum and alloys thereof.However,traditional methods for preparing Ti...TiB_(2)coatings can significantly enhance the high-temperature oxidation resistance of molybdenum,which would broaden the application range of molybdenum and alloys thereof.However,traditional methods for preparing TiB_(2)coatings have disadvantages such as high equipment costs,complicated processes,and highly toxic gas emissions.This paper proposes an environmentally friendly method,which requires inexpensive equipment and simple processing,for preparing TiB_(2)coating on molybdenum via electrophoretic deposition within Na3AlF6-based molten salts.The produced TiB_(2)layer had an approximate thickness of 60μm and exhibited high density,outstanding hardness(38.2 GPa)and robust adhesion strength(51 N).Additionally,high-temperature oxidation experiments revealed that,at900℃,the TiB_(2)coating provided effective protection to the molybdenum substrate against oxidation for 3 h.This result indicates that the TiB_(2)coating prepared on molybdenum using molten salt electrophoretic deposition possesses good high-temperature oxidation resistance.展开更多
The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects....The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.展开更多
基金supported by the School of Engineering and Digital Sciences of Nazarbayev University,Astana,Kazakhstan(to CE)。
文摘The osteochondral(OC)interface exhibits a mineral gradient,varying in thickness by several hundred micrometers across different species.Disruptions in this interface damage OC tissues,leading to osteoarthritis.The natural architecture and composition of native OC interfaces can be replicated using biomaterial scaffolds via regenerative engineering approaches.A novel one-step bioextrusion process was employed to fabricate a unitary synthetic graft(USG),which mimics the native OC interface’s mineral concentration gradient.This novel USG is composed of an agarose-based cartilage layer and a bone layer,consisting of agarose enriched with 20%(200 g/L)hydroxyapatite.The USG features a gradient interface with mineral concentrations transitioning from 0%to 20%(mass fraction),mimicking the transition between the cartilage and bone.Thermogravimetric analysis revealed that the gradient transition lengths of the graft and native OC tissue harvested from bovine knees were similar((647±21)vs.(633±124)μm).The linear viscoelastic properties of the grafts,which were evaluated using strain sweep and frequency sweep tests with oscillatory shear,indicated a dominant storage modulus over loss modulus similar to that of native OC tissues.The compressive and stress relaxation behaviors of the USGs demonstrated that the graft maintained structural integrity under mechanical stress.Viability assays performed after bioextrusion showed that chondrocytes and human fetal osteoblast cells successfully integrated and survived within their designated regions of the graft.The novel USGs exhibit properties similar to native OC tissue and are promising candidates for regenerating OC defects and restoring knee joint functionality.
基金supported by the Australian Research Council Centre of Excellence in Optical Microcombs for Breakthrough Science COMBS(CE230100006)the Australian Research Council grants DP220100488 and DE230100964funded by the Australian Government.
文摘Lithium niobate(LN)has remained at the forefront of academic research and industrial applications due to its rich material properties,which include second-order nonlinear optic,electro-optic,and piezoelectric properties.A further aspect of LN’s versatility stems from the ability to engineer ferroelectric domains with micro and even nano-scale precision in LN,which provides an additional degree of freedom to design acoustic and optical devices with improved performance and is only possible in a handful of other materials.In this review paper,we provide an overview of the domain engineering techniques developed for LN,their principles,and the typical domain size and pattern uniformity they provide,which is important for devices that require high-resolution domain patterns with good reproducibility.It also highlights each technique's benefits,limitations,and adaptability for an application,along with possible improvements and future advancement prospects.Further,the review provides a brief overview of domain visualization methods,which is crucial to gain insights into domain quality/shape and explores the adaptability of the proposed domain engineering methodologies for the emerging thin-film lithium niobate on an insulator platform,which creates opportunities for developing the next generation of compact and scalable photonic integrated circuits and high frequency acoustic devices.
基金National Natural Science Foundation of China(Grant No.22005318,22379152)Western Young Scholars Foundations of Chinese Academy of Sciences+4 种基金Lanzhou Youth Science and Technology Talent Innovation Project(Grant No.2023-NQ-86,No.2023-QN-96)Lanzhou Chengguan District Science and Technology Plan Project(Grant No.2023-rc-4,2022-rc-4)Collaborative Innovation Alliance Fund for Young Science and Technology Worker(Grant No.HZJJ23-7)National Nature Science Foundations of Gansu Province(Grant No.21JR11RA020)Fundamental Research Funds for the Central Universities(Grant No.31920220073,31920230128)。
文摘Rechargeable aqueous zinc-ion batteries(AZIBs)exhibit appreciable potential in the domain of electrochemical energy storage.However,there are serious challenges for AZIBs,for instance zinc dendrite growth,hydrogen evolution reaction(HER),and corrosion side reactions.Herein,we propose a surface engineering modification strategy for coating the montmorillonite(MMT)layer onto the surface of the Zn anode to tackle these issues,thereby achieving high cycling stability for rechargeable AZIBs.The results reveal that the MMT layer on the surface of the Zn anode is able to provide ordered zincophilic channels for zinc ions migration,facilitating the reaction kinetics of zinc ions.Density functional theory(DFT)calculations and water contact angle(CA)tests prove that MMT@Zn anode exhibits superior adsorption capacity for Zn^(2+)and better hydrophobicity than the bare Zn anode,thereby achieving excellent cycling stability.Moreover,the MMT@Zn||MMT@Zn symmetric cell holds the stable cycling over 5600 h at 0.5 mA cm^(-2)and 0.125 m A h cm^(-2),even exceeding 1800 h long cycling under harsh conditions of 5 m A cm^(-2)and 1.25 m A h cm^(-2).The MMT@Zn||V_(2)O_(5)full cell reaches over 3000 cycles at 2 A g^(-1)with excellent rate capability.Therefore,this surface engineering modification strategy for enhancing the electrochemical performance of AZIBs represents a promising application.
文摘This study focused on the production of polypropylene(PP)/silver(Ag)composites via additive manufacturing.This study aimed to enhance the quality of medical-grade PP in material extrusion(MEX)three-dimensional printing(3DP)by improving its mechanical properties while simultaneously adding antibacterial properties.The latter can find extremely important and versatile properties that are applicable in defense and security domains.PP/Ag nanocomposites were prepared using a novel method based on a reaction occurring while mixing appropriate quantities of the starting polymers and additives,namely polyvinylpyrrolidone(PVP)as the matrix material and silver nitrate(AgNO_(3))as the filler.This process produced three-dimensional(3D)printed filaments,which were then used to create specimens for a series of standardized tests.It was found that the mechanical properties of the nanocomposites were enhanced in relation to pristine PP,especially for the PP matrix with various loadings of AgNO_(3)and PVP,such as 5.0 wt%and 2.5 wt%,respectively.The voids,inclusions,and actual-to-nominal dimensions also showed improved results.The 3DP specimens exhibited a more effective biocidal performance against Staphylococcus aureus than Escherichia coli,which developed an inhibition zone only in the case of PP with filler loading percentages of AgNO_(3)and PVP at 10.0 wt%and 5.0 wt%,respectively Compounds possessing such properties can be beneficial for various applications requiring increased mechanical properties and biocidal capabilities,such as in the Defence or medical industries.
基金the Engineering and Physical Sciences Research Council(EPSRC)for funding the researchUK India Education Research Initiative(UKIERI)for funding support.
文摘This review provides an insightful and comprehensive exploration of the emerging 2D material borophene,both pristine and modified,emphasizing its unique attributes and potential for sustainable applications.Borophene’s distinctive properties include its anisotropic crystal structures that contribute to its exceptional mechanical and electronic properties.The material exhibits superior electrical and thermal conductivity,surpassing many other 2D materials.Borophene’s unique atomic spin arrangements further diversify its potential application for magnetism.Surface and interface engineering,through doping,functionalization,and synthesis of hybridized and nanocomposite borophene-based systems,is crucial for tailoring borophene’s properties to specific applications.This review aims to address this knowledge gap through a comprehensive and critical analysis of different synthetic and functionalisation methods,to enhance surface reactivity by increasing active sites through doping and surface modifications.These approaches optimize diffusion pathways improving accessibility for catalytic reactions,and tailor the electronic density to tune the optical and electronic behavior.Key applications explored include energy systems(batteries,supercapacitors,and hydrogen storage),catalysis for hydrogen and oxygen evolution reactions,sensors,and optoelectronics for advanced photonic devices.The key to all these applications relies on strategies to introduce heteroatoms for tuning electronic and catalytic properties,employ chemical modifications to enhance stability and leverage borophene’s conductivity and reactivity for advanced photonics.Finally,the review addresses challenges and proposes solutions such as encapsulation,functionalization,and integration with composites to mitigate oxidation sensitivity and overcome scalability barriers,enabling sustainable,commercial-scale applications.
文摘Self-Centering Piston-Based Braced Frames(SC-PBBFs)are designed to curtail structural damage under severe ground motions.The self-centering mechanism in this bracing mitigates structural damage during an earthquake,thereby reducing post-earthquake repair costs and contributing to seismic resilience.However,non-structural components,particularly those sensitive to floor acceleration,remain vulnerable,resulting in prolonged func-tional recovery times.This paper aims to address this limitation by introducing a novel structural archetype,the Self-Centering Viscous-Based Braced Frame(SC-VBBF),which integrates superelastic shape memory alloy(SMA)bars,viscous dampers(VDs),and friction springs(FSs).A streamlined analytical approach relies on the strength decoupling of VD from other components using aλfactor to design SC-VBBFs.To evaluate the effectiveness of the hybrid brace,a set of 4-,8-,and 12-story archetypes equipped with SC-PBBs and SC-VBBFs are simulated in OpenSees and analyzed under various earthquake types,including crustal,subcrustal,and subduction events.The results demonstrate the superior performance of the SC-VBBF withλ≤0.5 system compared to SC-PBBFs in mitigating floor accelerations under design-level earthquakes and improving seismic resilience.
基金supported by the grants PID2020-113371RA-C22 and TED2021-130845A-C32,funded by MCIN/AEI/10.13039/501100011033.M.Marín-García,R.González-OlmosC.Gómez-Canela are members of the GESPA group(Grup d’Enginyeria i Simulacióde Processos Ambientals)at IQS-URL,which has been acknowledged as a Consolidated Research Group by the Government of Catalonia(No.2021-SGR-00321)+1 种基金In addition,M.Marín-García has been awarded a public grant for the Investigo Programme,aimed at hiring young job seekers to undertake research and innovation projects under the Recovery,Transformation,and Resilience Plan(PRTR),European Union Next Generation,for the year 2022,through the Government of Catalonia and the Spanish Ministry for Work and Social Economy(No.100045ID16)Ana Belén Cuenca for her support and expertise,which helped to confirm the proposed reaction mechanism involved in the UV photolysis of cloperastine.
文摘The increasing production and release of synthetic organic chemicals,including pharmaceuticals,into our envi-ronment has allowed these substances to accumulate in our surface water systems.Current purification technolo-gies have been unable to eliminate these pollutants,resulting in their ongoing release into aquatic ecosystems.This study focuses on cloperastine(CPS),a cough suppressant and antihistamine medication.The environmental impact of CPS usage has become a concern,mainly due to its increased detection during the COVID-19 pandemic.CPS has been found in wastewater treatment facilities,effluents from senior living residences,river waters,and sewage sludge.However,the photosensitivity of CPS and its photodegradation profile remain largely unknown.This study investigates the photodegradation process of CPS under simulated tertiary treatment conditions using UV photolysis,a method commonly applied in some wastewater treatment plants.Several transformation prod-ucts were identified,evaluating their kinetic profiles using chemometric approaches(i.e.,curve fitting and the hard-soft multivariate curve resolution-alternating least squares(HS-MCR-ALS)algorithm)and calculating the reaction quantum yield.As a result,three different transformation products have been detected and correctly identified.In addition,a comprehensive description of the kinetic pathway involved in the photodegradation process of the CPS drug has been provided,including observed kinetic rate constants.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
基金funded in part by the Coordination for the Improvement of Higher Education Personnel(CAPES,Finance Code 001)in part by the Brazilian National Council for Scientific and Technological Development(CNPq,Grant No.131511/2020-3)/Ministry of Science,Technology and Innovation(MCTI)in part by the State of São Paulo Research Foundation(FAPESP)(Grant Nos.2015/03806-1 and 2023/08756-9).
文摘The dual-probe heat pulse(DPHP)is a well-established method for estimating soil moisture(θ)using soil thermal conductivity(λ)and volumetric heat capacity(C_(v)).Recently,monitoringθhas been improved by integrating the DPHP method with distributed temperature sensing(DTS)technology.In the DPHP-DTS approach,a single fiber optic(FO)cable with embedded metallic constituents functions as a heating element,while a parallel cable serves to monitor the temperature.Despite ongoing advancements,challenges such as the difficulty in positioning heating and sensing cables and high energy requirements hinder the widespread adoption of the DPHP-DTS method.As alternative heating materials are seldom used,this study evaluated the feasibility of employing a resistive metallic alloy as the heating element in a laboratory DPHP-DTS application.Overall,higher errors were observed when assessing C_(v)andλat higherθvalues(>0.2),but using C_(v)data produced more accurateθestimates(with the root mean square error(RMSE)≤0.06).Based on C_(v)values,a low-power,long-duration heat pulse(8.07 W/m for 300 s)yielded more consistentθestimates(RMSE=0.04)than a high-power,shortduration pulse(15.93 W/m for 180 s,with RMSE=0.06).The findings of this study also indicated that variations in heating uniformity and electrical power fluctuations potentially affected measurement accuracy.Nevertheless,the resistive alloy proved advantageous for DPHP-DTS due to its independent power connection,ability to maintain linear positioning within the soil,and potential for energy savings,all while providing reliableθestimates.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia(Grant No.13292-psu-2023-PSNU-R-3-1-EF-).
文摘Colorectal cancer is the third most diagnosed cancer worldwide,and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups.This study performed a comprehensive analysis of multi-omics data from The Cancer Genome Atlas colorectal adenocarcinoma cohort(TCGA-COADREAD),accessed through cBioPortal,to develop machine learning models for predicting progression-free survival(PFS)following immunotherapy.The dataset included clinical variables,genomic alterations in Kirsten Rat Sarcoma Viral Oncogene Homolog(KRAS),B-Raf Proto-Oncogene(BRAF),and Neuroblastoma RAS Viral Oncogene Homolog(NRAS),microsatellite instability(MSI)status,tumor mutation burden(TMB),and expression of immune checkpoint genes.Kaplan–Meier analysis showed that KRAS mutations were significantly associated with reduced PFS,while BRAF and NRAS mutations had no significant impact.MSI-high tumors exhibited elevated TMB and increased immune checkpoint expression,reflecting their immunologically active phenotype.We developed both survival and classification models,with the Extra Trees classifier achieving the best performance(accuracy=0.86,precision=0.67,recall=0.70,F1-score=0.68,AUC=0.84).These findings highlight the potential of combining genomic and immune biomarkers with machine learning to improve patient stratification and guide personalized immunotherapy decisions.An interactive web application was also developed to enable clinicians to input patient-specific molecular and clinical data and visualize individualized PFS predictions,supporting timely,data-driven treatment planning.
文摘The rapid proliferation of commercial unmanned aerial vehicles(UAVs)has revolutionized fields such as precision agriculture and disaster response.However,their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks,with potentially severe consequences.To mitigate this threat,we present a machine learning-driven framework for real-time GPS spoofing detection,designed with a balance of detection accuracy and computational efficiency.Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data,enabling robust and generalizable model development.A comprehensive evaluation ofmultiple classification algorithms identifies XGBoost as the superior performer,achieving 93.07% accuracy alongside outstanding precision,recall,and F1-scores.Beyond standard classification metrics,our assessment encompasses ROC-AUC,detection latency,and false positive rate,providing a comprehensive assessment of performance.This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks,supported by a detailed methodology,a comprehensive evaluation including inference-time latency,and a publicly available dataset.
基金supported by the National Natural Science Foundation of China(Grant No.62171182)the Natural Scienceof Hunan Province(Grant No.2025JJ50345)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20240452)。
文摘The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.
文摘As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.
文摘Sandwich functionally graded(FG)auxetic beams are extensively utilized in aerospace,automotive,and biomedical industries due to their excellent strength-toweight ratio,impact resistance,and tunable mechanical properties.The integration of FG materials with auxetic structures enhances their adaptability in advanced engineering applications.However,understanding their dynamic behavior under external excitations is essential for optimal design and structural reliability.Nonlinear interactions in such structures pose significant challenges in vibration analysis,necessitating robust analytical methods.This study presents a closed-form solution for the nonlinear forced vibration analysis of sandwich FG auxetic beams,offering an accurate and efficient method for predicting their dynamic response.The beam consists of two FG face sheets with material properties varying through the thickness and a re-entrant honeycomb auxetic core with an adjustable Poisson's ratio.The governing nonlinear equations of motion are derived using the first-order shear deformation theory(FSDT),the modified Gibson model,and the von Kármán relations,formulated through Hamilton's principle.A closed-form solution is obtained via the Galerkin method and multiple-scale technique.The results demonstrate that FG layers enable control of the overweight and dynamic response amplitude,with positive power law indexes reducing weight.Comparisons with finite element results confirm the accuracy of the proposed formulation.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金supported by the National Natural Science Foundation of China(52276196)the Foundation of State Key Laboratory of Coal Combustion(FSKLCCA2508)the High-level Talent Foundation of Anhui Agricultural University(rc412307).
文摘Flash Joule heating(FJH),as a high-efficiency and low-energy consumption technology for advanced materials synthesis,has shown significant potential in the synthesis of graphene and other functional carbon materials.Based on the Joule effect,the solid carbon sources can be rapidly heated to ultra-high temperatures(>3000 K)through instantaneous high-energy current pulses during FJH,thus driving the rapid rearrangement and graphitization of carbon atoms.This technology demonstrates numerous advantages,such as solvent-and catalyst-free features,high energy conversion efficiency,and a short process cycle.In this review,we have systematically summarized the technology principle and equipment design for FJH,as well as its raw materials selection and pretreatment strategies.The research progress in the FJH synthesis of flash graphene,carbon nanotubes,graphene fibers,and anode hard carbon,as well as its by-products,is also presented.FJH can precisely optimize the microstructures of carbon materials(e.g.,interlayer spacing of turbostratic graphene,defect concentration,and heteroatom doping)by regulating its operation parameters like flash voltage and flash time,thereby enhancing their performances in various applications,such as composite reinforcement,metal-ion battery electrodes,supercapacitors,and electrocatalysts.However,this technology is still challenged by low process yield,macroscopic material uniformity,and green power supply system construction.More research efforts are also required to promote the transition of FJH from laboratory to industrial-scale applications,thus providing innovative solutions for advanced carbon materials manufacturing and waste management toward carbon neutrality.
基金supported by the Yunnan Province Basic Research General Program,China(No.202201BE070001-002)the Major Science and Technology Projects in Yunnan Province,China(No.202402AF 080005).
文摘The rapid expansion of the photovoltaic industry has generated heavily oxidized waste silicon(wSi),which hinders efficient recycling owing to its small particle size and uncontrolled surface oxidation.This study introduces a molten salt electrochemical strategy for converting photovoltaic wSi into NiSi_(2)-silicon nanorods(NiSi_(2)-SiNRs)as high-performance anode materials for lithium-ion batteries.A stable oxidized passivation layer is formed on the wSi surface via controlled oxidation,and further in situ generated highly active NiSi_(2) droplets.The molten salt electric field modulates the surface energy of silicon,while particle integration drives localized directional growth,enabling the self-assembly of NiSi_(2)-SiNRs composites.These NiSi_(2)-SiNRs anodes exhibit rapid ion transport and effective strain buffering.The high aspect ratio of SiNRs and the presence of retained NiSi_(2) facilitate both longitudinal and transverse Li^(+) diffusion.Owing to their robust structural design,the NiSi_(2)-SiNRs anode achieves an excellent initial Coulombic efficiency of 91.61%and retains 72.99%of its capacity after 800 cycles at 2 A·g^(−1).This study establishes a model system for investigating silicide/silicon interfaces in molten salt electrochemical synthesis and provides an effective strategy for upcycling photovoltaic wSi into high-performance lithium-ion battery anodes.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
基金supported by the Original Exploratory Program of the National Natural Science Foundation of China(No.52450012)。
文摘TiB_(2)coatings can significantly enhance the high-temperature oxidation resistance of molybdenum,which would broaden the application range of molybdenum and alloys thereof.However,traditional methods for preparing TiB_(2)coatings have disadvantages such as high equipment costs,complicated processes,and highly toxic gas emissions.This paper proposes an environmentally friendly method,which requires inexpensive equipment and simple processing,for preparing TiB_(2)coating on molybdenum via electrophoretic deposition within Na3AlF6-based molten salts.The produced TiB_(2)layer had an approximate thickness of 60μm and exhibited high density,outstanding hardness(38.2 GPa)and robust adhesion strength(51 N).Additionally,high-temperature oxidation experiments revealed that,at900℃,the TiB_(2)coating provided effective protection to the molybdenum substrate against oxidation for 3 h.This result indicates that the TiB_(2)coating prepared on molybdenum using molten salt electrophoretic deposition possesses good high-temperature oxidation resistance.
文摘The Internet of Vehicles,or IoV,is expected to lessen pollution,ease traffic,and increase road safety.IoV entities’interconnectedness,however,raises the possibility of cyberattacks,which can have detrimental effects.IoV systems typically send massive volumes of raw data to central servers,which may raise privacy issues.Additionally,model training on IoV devices with limited resources normally leads to slower training times and reduced service quality.We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning(TinyML)approach,which operates on IoV edge devices without sharing sensitive raw data.Specifically,we focus on integrating split learning(SL)with federated learning(FL)and TinyML models.FL is a decentralisedmachine learning(ML)technique that enables numerous edge devices to train a standard model while retaining data locally collectively.The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain,coupled with FL and TinyML.The approach starts with the IoV learning framework,which includes edge computing,FL,SL,and TinyML,and then proceeds to discuss how these technologies might be integrated.We elucidate the comprehensive operational principles of Federated and split learning by examining and addressingmany challenges.We subsequently examine the integration of SL with FL and various applications of TinyML.Finally,exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM.It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes,thereby obviating the necessity for centralised data aggregation,which presents considerable privacy threats.The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL,hence facilitating advancement in this swiftly progressing domain.
