With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Significant progress has been achieved in the field of organic solar cells(OSCs). Most devices with power conversion efficiencies(PCEs) exceeding 20% rely predominantly on active materials that incorporate D18 or its ...Significant progress has been achieved in the field of organic solar cells(OSCs). Most devices with power conversion efficiencies(PCEs) exceeding 20% rely predominantly on active materials that incorporate D18 or its derivatives as the donor. In contrast, the PCEs over 20% have been realized as well for OSCs with the non-D18-based donor materials by simultaneously optimizing material properties, active layer morphologies and interface engineering, thereby demonstrating the potential to outperform D18 counterparts. Therefore, this review summarizes an overview of recent advancements in OSCs with the PCEs over20% utilizing the non-D18-based donor materials, and highlights three critical aspects including molecular design strategies,the active layer morphologies, and the interface optimization. Their synergistic roles are advantageous in enhancing the exciton dissociation, facilitating the charge transport, and suppressing the recombination losses, accordingly supporting the improved PCEs over 20%. Furthermore, the challenges and valuable insights are discussed, which can lead to improved efficiency, scalable fabrication, and enhanced environmental and thermal stability, potentially accelerating the commercialization of OSCs.展开更多
High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as not...High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as notable solid-state welding and processing techniques,have been proved effectiveness in enhancing microstructures and mechanical properties of HEAs.This review article summarizes the current status of FSW/P of HEAs.The welding materials and conditions used for FSW/P in HEAs are reviewed and discussed.The effects of FSW/P on the evolutions of grain structure,texture,dislocation,and secondary phase for different HEAs are highlighted.Furthermore,the influences of FSW/P on the mechanical properties of various HEAs are analyzed.Finally,potential applications,challenges,and future directions of FSW/P in HEAs are forecasted.Overall,FSW/P enable to refine grains of HEAs through dynamic recrystallization and to activate diverse deformation mechanisms of HEAs through tailoring phase structures,thereby significantly improving the strength,hardness,and ductility of both single-and dual-phase HEAs.Future progress in this field will rely on comprehensive optimization of processing parameters and alloy composition,integration of multi-scale modeling with advanced characterization for in-depth exploration of microstructural mechanisms,systematic evaluation of functional properties,and effective bridging of the gap between laboratory research and industrial application.The review aims to provide an overview of recent advancements in the FSW/P of HEAs and encourage further research in this area.展开更多
Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^(...Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^([1]).By 2050,the country is forecast to remain the one with the largest population of overweight and obese globally^([1]),if no effective strategies were applied on overweight/obesity control.展开更多
In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment techni...In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.展开更多
The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process ...The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.展开更多
The paper presents the results of geomechanical and CT-based studies of deformation,fracture and filtration processes in reservoir rocks of the Arctic shelf gas condensate field.The experimental study combines(i)deter...The paper presents the results of geomechanical and CT-based studies of deformation,fracture and filtration processes in reservoir rocks of the Arctic shelf gas condensate field.The experimental study combines(i)determination of mechanical properties,(ii)true triaxial physical modeling of near-wellbore filtration and geomechanical processes,(iii)triaxial sand production studies,and(iv)digital CT-analysis of the rock matrix and sand particles.Based on true triaxial physical modeling,the relationships between permeability,rock deformation,and stresses around a horizontal well during drawdown were determined.Hollow cylinder-type tests were used to determine the stress conditions for sand release initiation,the intensity of sand production under varying stress states,and the total volume of sand produced.Digital particle size analysis of the matrix and released sand provided insights into the dominant mechanisms of hole failure during sand production.A significant strength anisotropy of reservoir rocks was identified,suggesting that drawdown in horizontal wells could lead to asymmetric bottomhole zone fracture,initiated at the upper and lower points on the wellbore contour.The obtained results allowed to determine(i)the drawdowns required to maintain wellbore stability in the given reservoir interval;(ii)the optimal parameters of downhole gravel filter screens for sand control;(iii)to identify the prevailing type of wellbore fracture and to localize failure initiation points on the wellbore walls.The results highlight the importance of integrating modern laboratory core analysis methods to enhance the development of complex reservoirs and reduce the risks of fractures and sand production in weakly cemented formations.展开更多
Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual ...Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.展开更多
Bacterial and mycoplasma infections pose a severe hazard to human life and property.These necessitate the development of antibacterial metallic materials that can be produced efficiently in large quantities.In this st...Bacterial and mycoplasma infections pose a severe hazard to human life and property.These necessitate the development of antibacterial metallic materials that can be produced efficiently in large quantities.In this study,an(Fe_(63.3)Mn_(14)Si_(9.1)Cr_(9.8)C_(3.8))_(86)Cu_(12)Ag_(2)medium-entropy alloy(MEA)consisting of in situ FCC1(austenite)and FCC2(Cu–Ag-rich)phases was prepared.It displayed a yield strength of 1100 MPa,fracture strength of 1921 MPa,and compressive plasticity of 27%at room temperature.This is attributed to the low stacking fault energy(3.7 m J m^(-2))inducing strong transformation-induced plasticity(TRIP),twinning-induced plasticity(TWIP),and lattice distortion.The alloy contained nano-and microscale antibacterial phases.This enabled it to achieve an antimicrobial efficiency higher than 99.9%against E.coli and S.aureus after6 h of exposure.The hot working efficiency makes it preferable for mass production with critical process parameters.A constitutive model was established using the Arrhenius equation to validate the applicability of the dynamic materials model(DMM).Subsequently,the hot processing map of the medium-entropy alloy was established based on the DMM.The optimal processing parameters were determined as 800℃with strain rates of10^(–1)–10^(–2)s^(-1).The low stacking fault energy ensures that dynamic recrystallization is the primary softening mechanism in the“safe”region.Finally,the density of states(DOS)of the MEA(determined by first-principles calculations)was significantly lower(162.1 eV)than those of Ni and Fe.This indicated a strong high-temperature stability.The DOS increased marginally with an increase in deformation.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
Efficient and innovative nano-catalytic oxidation technologies offer a breakthrough in removing emerging contaminants(ECs)from water,surpassing the limitations of traditional methods.Environmental functional materials...Efficient and innovative nano-catalytic oxidation technologies offer a breakthrough in removing emerging contaminants(ECs)from water,surpassing the limitations of traditional methods.Environmental functional materials(EFMs),particularly high-end oxidation systems using eco-friendly nanomaterials,show promise for absorbing and degrading ECs.This literature review presents a comprehensive analysis of diverse traditional restoration techniques-biological,physical,and chemical-assessing their respective applications and limitations in pesticide-contaminated water purification.Through meticulous comparison,we unequivocally advocate for the imperative integration of environmentally benign nanomaterials,notably titanium-based variants,in forthcoming methodologies.Our in-depth exploration scrutinizes the catalytic efficacy,underlying mechanisms,and adaptability of pioneering titanium-based nanomaterials across a spectrum of environmental contexts.Additionally,strategic recommendations are furnished to surmount challenges and propel the frontiers of implementing eco-friendly nanomaterials in practical water treatment scenarios.展开更多
The widespread occurrence of antibiotics in wastewater aroused serious attention.UV-based advanced oxidation processes(UV-AOPs)are powerful technologies in removing antibiotics in wastewater,which include UV/catalyst,...The widespread occurrence of antibiotics in wastewater aroused serious attention.UV-based advanced oxidation processes(UV-AOPs)are powerful technologies in removing antibiotics in wastewater,which include UV/catalyst,UV/H_(2)O_(2),UV/Fenton,UV/persulfate,UV/chlorine,UV/ozone,and UV/peracetic acid.In this review,we collated recent advances in application of UV-AOPs for the abatement of fiuoroquinolones(FQs)as widely used class of antibiotics.Representative FQs of ciprofioxacin,norfioxacin,ofioxacin,and enrofioxacin were most extensively studied in the state-of-art studies.The evolvement of gas-state and solid-state UV light sources was presented and batch and continuous fiow UV reactors were compared towards practical applications in UV-AOPs.Generally,degradation of FQs followed the pseudo-first order kinetics in UV-AOPs and strongly affected by the operating factors and components of water matrix.Participation of reactive species and transformation mechanisms of FQs were compared among different UV-AOPs.Challenges and future prospects were pointed out for providing insights into the practical application of UV-AOPs for antibiotic remediation in wastewater.展开更多
Development of sustainable construction materials has been the focus of research efforts worldwide in recent years.Concrete is a major construction material;hence,finding alternatives to ordinary Portland cement is of...Development of sustainable construction materials has been the focus of research efforts worldwide in recent years.Concrete is a major construction material;hence,finding alternatives to ordinary Portland cement is of extreme importance due to the high levels of carbon dioxide emissions associated with its manufacturing process.This study investigates the geopolymerization process.Specimens with,two different water/binder weight ratios,0.30 and 0.35,were monitored using acoustic emission.Results show that there is a significant difference in the acquisition data between the two different water/binder weight ratios.In addition,acoustic emission can be used to beneficially monitor and investigate the early geopolymerization process.The acoustic emission data were processed through pattern recognition.Two clusters were identified,assigned to a specific mechanism depending on their characteristics.SEM observations were coincided with pattern recognition findings.展开更多
Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nic...Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.展开更多
With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking f...With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
In this work, friction stir processing(FSP) was applied to the high-strength and high-melting-point Ni–Fe-based superalloy(HT700) for the first time with negligible wear of the stir tool. Different rotation rates wer...In this work, friction stir processing(FSP) was applied to the high-strength and high-melting-point Ni–Fe-based superalloy(HT700) for the first time with negligible wear of the stir tool. Different rotation rates were chosen to investigate the effect of heat input on microstructure and tensile properties at different temperatures of friction stir processed Ni–Fe-based superalloy. The results showed that with increasing rotation rate, the percentage of high-angle grain boundaries and twin boundaries gradually decreased whereas the grain size initially increased and then remained almost constant;the difference in tensile properties of FSP samples with rotation rates of 500–700 rpm was small attributing to their similar grain size, but the maximum strength was achieved in the FSP sample with a rotation rate of 400 rpm and traverse speed of 50 mm/min due to its finest grain size. More importantly, we found that the yield strength of all FSP samples tensioned at 700 ℃ was enhanced clearly resulting from the reprecipitation of γ′ phase. In addition, the grain refinement mechanism of HT700 alloy during FSP was proved to be continuous dynamic recrystallization and the specific refinement process was given.展开更多
Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize pr...Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize process parameters for the preparation of V-Fe-based alloy via silicon thermal reduction.Experiments were conducted to investigate the effects of reduction temperature,holding time,and slag composition on alloy-slag separation,alloy microstructure,and the oxide content of residual slag,with an emphasis on the recovery of valuable metal elements.The results indicated that the optimal process conditions for silicon thermal reduction were achieved at reduction temperature of 1823 K,holding time of 240 min,and slag composition of 45 wt.%SiO_(2),40 wt.%CaO,and 15 wt.%Al_(2)O_(3).The resulting V-Fe-based alloy predominantly consisted of Fe-based phases such as Fe,titanium(Ti),silicon(Si)and manganese(Mn),with Si,V,as well as chromium(Cr)concentrated in the intercrystalline phase of the Fe-based alloy.The recoveries of Fe,Mn,Cr,V,and Ti under the optimal conditions were 96.30%,91.96%,86.53%,80.29%,and 74.82%,respectively.The key components of the V-Fe-based alloy obtained were 41.96 wt.%Si,27.55 wt.%Fe,12.13 wt.%Mn,5.53 wt.%V,4.86 wt.%Cr,and 3.74 wt.%Ti,thereby enabling the comprehensive recovery of the valuable metal from vanadium slag.展开更多
Perovskite solar cells(PSCs) have revolutionized photovoltaic research. As a result, a certified power conversion efficiency(PCE) of 25.5% was recorded in late 2020. Although this efficiency is comparable with silicon...Perovskite solar cells(PSCs) have revolutionized photovoltaic research. As a result, a certified power conversion efficiency(PCE) of 25.5% was recorded in late 2020. Although this efficiency is comparable with silicon solar cells;some issues remain partially unsolved, such as lead toxicity, instability of perovskite materials under continuous illumination, moisture and oxygen, and degradation of the metallic counter electrodes. As an alternative to tackle this last concern, carbon materials have been recently used, due to their good electrical and thermal conductivity, and chemical stability, which makes them one of the most promising materials to replace metallic counter electrodes in the fabrication of PSCs. This review highlights the recent advances of carbon-based PSCs, where the carbon electrode(CE) is the main actor.CEs have become very promising candidates for PSCs;they are mainly fabricated using a simple combination of graphite and carbon black powders embedded in a binder matrix, giving a paste that is then solution-processable, resulting in devices with improved quality stability, when compared to metallic electrodes. In this review, CE’s composition is emphasized, since it can give both, high and lowtemperature processed electrodes, compatible with different device configurations. Finally, the tendencies and opportunities to use CE in PSCs devices are presented.展开更多
Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An ...Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.展开更多
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
基金support from the National Key Research and Development Program of China (2022YFB3803300)the National Natural Science Foundation of China (U23A20138 and 52173192)Hunan Provincial Major Basic Research Project (2025JC0004)。
文摘Significant progress has been achieved in the field of organic solar cells(OSCs). Most devices with power conversion efficiencies(PCEs) exceeding 20% rely predominantly on active materials that incorporate D18 or its derivatives as the donor. In contrast, the PCEs over 20% have been realized as well for OSCs with the non-D18-based donor materials by simultaneously optimizing material properties, active layer morphologies and interface engineering, thereby demonstrating the potential to outperform D18 counterparts. Therefore, this review summarizes an overview of recent advancements in OSCs with the PCEs over20% utilizing the non-D18-based donor materials, and highlights three critical aspects including molecular design strategies,the active layer morphologies, and the interface optimization. Their synergistic roles are advantageous in enhancing the exciton dissociation, facilitating the charge transport, and suppressing the recombination losses, accordingly supporting the improved PCEs over 20%. Furthermore, the challenges and valuable insights are discussed, which can lead to improved efficiency, scalable fabrication, and enhanced environmental and thermal stability, potentially accelerating the commercialization of OSCs.
基金supported by National Natural Science Foundation of China(Grant No.52171032)Hebei Natural Science Foundation(Grant No.E2023501002)Fundamental Research Funds for the Central Universities(Grant No.2024GFYD003)。
文摘High entropy alloys(HEAs)have recently attracted significant attention due to their exceptional mechanical properties and potential applications across various fields.Friction stir welding and processing(FSW/P),as notable solid-state welding and processing techniques,have been proved effectiveness in enhancing microstructures and mechanical properties of HEAs.This review article summarizes the current status of FSW/P of HEAs.The welding materials and conditions used for FSW/P in HEAs are reviewed and discussed.The effects of FSW/P on the evolutions of grain structure,texture,dislocation,and secondary phase for different HEAs are highlighted.Furthermore,the influences of FSW/P on the mechanical properties of various HEAs are analyzed.Finally,potential applications,challenges,and future directions of FSW/P in HEAs are forecasted.Overall,FSW/P enable to refine grains of HEAs through dynamic recrystallization and to activate diverse deformation mechanisms of HEAs through tailoring phase structures,thereby significantly improving the strength,hardness,and ductility of both single-and dual-phase HEAs.Future progress in this field will rely on comprehensive optimization of processing parameters and alloy composition,integration of multi-scale modeling with advanced characterization for in-depth exploration of microstructural mechanisms,systematic evaluation of functional properties,and effective bridging of the gap between laboratory research and industrial application.The review aims to provide an overview of recent advancements in the FSW/P of HEAs and encourage further research in this area.
文摘Overweight and obesity has been a major public health problem globally.It was estimated that more than 2.1 billion adults were affected by overweight or obese in 2021 worldwide,about one fifth of whom lived in China^([1]).By 2050,the country is forecast to remain the one with the largest population of overweight and obese globally^([1]),if no effective strategies were applied on overweight/obesity control.
基金supported by the Major Science and Technology Project of Zhongshan City(No.2022AJ004)the Key Basic and Applied Research Program of Guangdong Province(Nos.2019B030302010 and 2022B1515120082)Guangdong Science and Technology Innovation Project(No.2021TX06C111).
文摘In general,the rapid growth of α-Fe clusters is a challenge in high Fe-content Fe-based amorphous alloys,negatively affecting their physical properties.Herein,we introduce an efficient and rapid post-treatment technique known as ultrasonic vibration rapid processing(UVRP),which enables the formation of high-density strong magnetic α-Fe clusters,thereby enhancing the soft magnetic properties of Fe_(78)Si(13)B_(9) amorphous alloy ribbon.
基金support of the Korea Institute of Industrial Technol-ogy as“Development of a remote manufacturing system for high-risk,high-difficulty pipe production processes”(kitech EH-25-0004)supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program)(RS-2023–00237714+2 种基金Development of Dynamic Metrology Tool for CMP Process StabilizationRS-2025–02634755Development of Real-Time Electrical Fire Prevention System Technology Reflecting the Characteristics of Traditional Markets)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea).
文摘The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.
基金supported by the Russian Science Foundation(Grant No.23-77-01037,https://rscf.ru/en/project/23-77-01037/).
文摘The paper presents the results of geomechanical and CT-based studies of deformation,fracture and filtration processes in reservoir rocks of the Arctic shelf gas condensate field.The experimental study combines(i)determination of mechanical properties,(ii)true triaxial physical modeling of near-wellbore filtration and geomechanical processes,(iii)triaxial sand production studies,and(iv)digital CT-analysis of the rock matrix and sand particles.Based on true triaxial physical modeling,the relationships between permeability,rock deformation,and stresses around a horizontal well during drawdown were determined.Hollow cylinder-type tests were used to determine the stress conditions for sand release initiation,the intensity of sand production under varying stress states,and the total volume of sand produced.Digital particle size analysis of the matrix and released sand provided insights into the dominant mechanisms of hole failure during sand production.A significant strength anisotropy of reservoir rocks was identified,suggesting that drawdown in horizontal wells could lead to asymmetric bottomhole zone fracture,initiated at the upper and lower points on the wellbore contour.The obtained results allowed to determine(i)the drawdowns required to maintain wellbore stability in the given reservoir interval;(ii)the optimal parameters of downhole gravel filter screens for sand control;(iii)to identify the prevailing type of wellbore fracture and to localize failure initiation points on the wellbore walls.The results highlight the importance of integrating modern laboratory core analysis methods to enhance the development of complex reservoirs and reduce the risks of fractures and sand production in weakly cemented formations.
基金supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2023-00239657)in part by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.RS-2024-00423772)。
文摘Nondestructive testing(NDT)methods such as visual inspection and ultrasonic testing are widely applied in manufacturing quality control,but they remain limited in their ability to detect defect characteristics.Visual inspection depends strongly on operator experience,while ultrasonic testing requires physical contact and stable coupling conditions that are difficult to maintain in production lines.These constraints become more pronounced when defect-related information is scarce or when background noise interferes with signal acquisition in manufacturing processes.This study presents a non-contact acoustic method for diagnosing defects in scroll compressors during the manufacturing process.The diagnostic approach leverages Mel-frequency cepstral coefficients(MFCC),and shorttime Fourier transform(STFT)parameters to capture the rotational frequency and harmonic characteristics of the scroll compressor.These parameters enable the extraction of defect-related features even in the presence of background noise.A convolutional neural network(CNN)model was constructed using MFCCs and spectrograms as image inputs.The proposed method was validated using acoustic data collected from compressors operated at a fixed rotational speed under real manufacturing process.The method identified normal operation and three defect types.These results demonstrate the applicability of this method in noise-prone manufacturing environments and suggest its potential for improving product quality,manufacturing reliability and productivity.
基金financially supported by the Science and Technology Program Project of Gansu Province(No.24ZD13GA018)the National Natural Science Foundation of China(Nos.12404230 and 52061027)+1 种基金Zhejiang Provincial Natural Science Foundation of China(No.LY23E010002)Lanzhou Youth Science and Technology Talent Innovation Project(No.2023-QN-91)
文摘Bacterial and mycoplasma infections pose a severe hazard to human life and property.These necessitate the development of antibacterial metallic materials that can be produced efficiently in large quantities.In this study,an(Fe_(63.3)Mn_(14)Si_(9.1)Cr_(9.8)C_(3.8))_(86)Cu_(12)Ag_(2)medium-entropy alloy(MEA)consisting of in situ FCC1(austenite)and FCC2(Cu–Ag-rich)phases was prepared.It displayed a yield strength of 1100 MPa,fracture strength of 1921 MPa,and compressive plasticity of 27%at room temperature.This is attributed to the low stacking fault energy(3.7 m J m^(-2))inducing strong transformation-induced plasticity(TRIP),twinning-induced plasticity(TWIP),and lattice distortion.The alloy contained nano-and microscale antibacterial phases.This enabled it to achieve an antimicrobial efficiency higher than 99.9%against E.coli and S.aureus after6 h of exposure.The hot working efficiency makes it preferable for mass production with critical process parameters.A constitutive model was established using the Arrhenius equation to validate the applicability of the dynamic materials model(DMM).Subsequently,the hot processing map of the medium-entropy alloy was established based on the DMM.The optimal processing parameters were determined as 800℃with strain rates of10^(–1)–10^(–2)s^(-1).The low stacking fault energy ensures that dynamic recrystallization is the primary softening mechanism in the“safe”region.Finally,the density of states(DOS)of the MEA(determined by first-principles calculations)was significantly lower(162.1 eV)than those of Ni and Fe.This indicated a strong high-temperature stability.The DOS increased marginally with an increase in deformation.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金supported by the Research Platform Open Fund Project of Zhejiang Industry and Trade Vocation College(No.Kf202203)the Scientific Research Project of CCCC First Harbor Engineering Company Ltd.(No.2022-7-2)+3 种基金the National Natural Science Foundation of China(No.22406142)the Fellowship of China National Postdoctoral Program for Innovative Talents(No.BX20230262)the Fellowship of China Postdoctoral Science Foundation(No.2023M732636)the Shanghai Post-doctoral Excellence Program(No.2023755).
文摘Efficient and innovative nano-catalytic oxidation technologies offer a breakthrough in removing emerging contaminants(ECs)from water,surpassing the limitations of traditional methods.Environmental functional materials(EFMs),particularly high-end oxidation systems using eco-friendly nanomaterials,show promise for absorbing and degrading ECs.This literature review presents a comprehensive analysis of diverse traditional restoration techniques-biological,physical,and chemical-assessing their respective applications and limitations in pesticide-contaminated water purification.Through meticulous comparison,we unequivocally advocate for the imperative integration of environmentally benign nanomaterials,notably titanium-based variants,in forthcoming methodologies.Our in-depth exploration scrutinizes the catalytic efficacy,underlying mechanisms,and adaptability of pioneering titanium-based nanomaterials across a spectrum of environmental contexts.Additionally,strategic recommendations are furnished to surmount challenges and propel the frontiers of implementing eco-friendly nanomaterials in practical water treatment scenarios.
基金the financial support from National Natural Science Foundation of China(Nos.52100204 and 52330005)Beijing Outstanding Young Scientist Program(No.BJJWZYJH01201910004016)。
文摘The widespread occurrence of antibiotics in wastewater aroused serious attention.UV-based advanced oxidation processes(UV-AOPs)are powerful technologies in removing antibiotics in wastewater,which include UV/catalyst,UV/H_(2)O_(2),UV/Fenton,UV/persulfate,UV/chlorine,UV/ozone,and UV/peracetic acid.In this review,we collated recent advances in application of UV-AOPs for the abatement of fiuoroquinolones(FQs)as widely used class of antibiotics.Representative FQs of ciprofioxacin,norfioxacin,ofioxacin,and enrofioxacin were most extensively studied in the state-of-art studies.The evolvement of gas-state and solid-state UV light sources was presented and batch and continuous fiow UV reactors were compared towards practical applications in UV-AOPs.Generally,degradation of FQs followed the pseudo-first order kinetics in UV-AOPs and strongly affected by the operating factors and components of water matrix.Participation of reactive species and transformation mechanisms of FQs were compared among different UV-AOPs.Challenges and future prospects were pointed out for providing insights into the practical application of UV-AOPs for antibiotic remediation in wastewater.
基金supported by the U.S.Department of Energy Office of Science,Office of Basic Energy Sciences,and Office of Biological and Environmental Research under Award Number DE-SC-00012530.
文摘Development of sustainable construction materials has been the focus of research efforts worldwide in recent years.Concrete is a major construction material;hence,finding alternatives to ordinary Portland cement is of extreme importance due to the high levels of carbon dioxide emissions associated with its manufacturing process.This study investigates the geopolymerization process.Specimens with,two different water/binder weight ratios,0.30 and 0.35,were monitored using acoustic emission.Results show that there is a significant difference in the acquisition data between the two different water/binder weight ratios.In addition,acoustic emission can be used to beneficially monitor and investigate the early geopolymerization process.The acoustic emission data were processed through pattern recognition.Two clusters were identified,assigned to a specific mechanism depending on their characteristics.SEM observations were coincided with pattern recognition findings.
基金financially supported by the National Key R&D Program of China(No.2021YFB3702301)the National Natural Science Foundation of China(No.52101068]+2 种基金the China Postdoctoral Science Foundation[No.2022T150342]the Postdoctoral International Exchange Program[No.YJ20210129]the Shuimu Tsinghua Scholar Program(No.2020SM100)
文摘Notable advancements have been made in the additive manufacturing(AM)of aerospace materials,driven by the needs for integrated components with intricate geometries and small-lot production of high-value components.Nickel-based superalloys,pivotal materials for high-temperature bearing components in aeroengines,present significant challenges in the fabrication of complex parts due to their great hardness.Huge attention and rapid progress have been garnered in AM processing of nicklebased superalloys,largely owing to its distinct benefits in the freedom of fabrication and reduced manufacturing lifecycle.Despite extensive research into AM in nickel-based superalloys,the corresponding results and conclusions are scattered attributed to the variety of nickel-based superalloys and complex AM processing parameters.Therefore,there is still a pressing need for a comprehensive and deep understanding of the relationship between the AM processing and microstructures and mechanical performance of nickel-based superalloys.This review introduces the processing characteristics of four primary AM technologies utilized for superalloys and summarizes the microstructures and mechanical properties prior to and post-heat treatments.Additionally,this review presents innovative superalloys specifically accommodated to AM processing and offers insights into the material development and performance improvement,aiming to provide a valuable assessment on AM processing of nickel-based superalloys and an effective guidance for the future research.
基金the financial support of the National Key Research and Development Program of China(2021YFE0112800)EU RISE project OPTIMAL(101007963).
文摘With growing concerns over environmental issues,ethylene manufacturing is shifting from a sole focus on economic benefits to an additional consideration of environmental impacts.The operation of the thermal cracking furnace in ethylene manufacturing determines not only the profitability of an ethylene plant but also the carbon emissions it releases.While multi-objective optimization of the thermal cracking furnace to balance profit with environmental impact is an effective solution to achieve green ethylene man-ufacturing,it carries a high computational demand due to the complex dynamic processes involved.In this work,artificial intelligence(AI)is applied to develop a novel hybrid model based on physically consistent machine learning(PCML).This hybrid model not only reduces the computational demand but also retains the interpretability and scalability of the model.With this hybrid model,the computational demand of the multi-objective dynamic optimization is reduced to 77 s.The optimization results show that dynamically adjusting the operating variables with coke formation can effectively improve profit and reduce CO_(2)emissions.In addition,the results from this study indicate that sacrificing 28.97%of the annual profit can significantly reduce the annual CO_(2)emissions by 42.89%.The key findings of this study highlight the great potential for green ethylene manufacturing based on AI through modeling and optimization approaches.This study will be important for industrial practitioners and policy-makers.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金the National Natural Science Foundation of China(Grant Nos.11872354 and 11627803)the National Key R&D Program of China(Nos.2019YFA0705304 and 2017YFA0700703)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB22040502)。
文摘In this work, friction stir processing(FSP) was applied to the high-strength and high-melting-point Ni–Fe-based superalloy(HT700) for the first time with negligible wear of the stir tool. Different rotation rates were chosen to investigate the effect of heat input on microstructure and tensile properties at different temperatures of friction stir processed Ni–Fe-based superalloy. The results showed that with increasing rotation rate, the percentage of high-angle grain boundaries and twin boundaries gradually decreased whereas the grain size initially increased and then remained almost constant;the difference in tensile properties of FSP samples with rotation rates of 500–700 rpm was small attributing to their similar grain size, but the maximum strength was achieved in the FSP sample with a rotation rate of 400 rpm and traverse speed of 50 mm/min due to its finest grain size. More importantly, we found that the yield strength of all FSP samples tensioned at 700 ℃ was enhanced clearly resulting from the reprecipitation of γ′ phase. In addition, the grain refinement mechanism of HT700 alloy during FSP was proved to be continuous dynamic recrystallization and the specific refinement process was given.
基金the financial support provided by the National Key R&D Program of China(Grant No.2023YFC3903900)the Science and Technology Innovation Talent Program of Hubei Province(Grant No.2022EJD002)+1 种基金the Sichuan Science and Technology Program(Grant No.2025ZNSFSC0378)the Key Laboratory of Green Chemistry of Sichuan Institutes of Higher Education(Grant No.LZJ2303).
文摘Specialized vanadium(V)-iron(Fe)-based alloy additives utilized in the production of V-containing steels were investigated.Vanadium slag from the Panzhihua region of China was utilized as a raw material to optimize process parameters for the preparation of V-Fe-based alloy via silicon thermal reduction.Experiments were conducted to investigate the effects of reduction temperature,holding time,and slag composition on alloy-slag separation,alloy microstructure,and the oxide content of residual slag,with an emphasis on the recovery of valuable metal elements.The results indicated that the optimal process conditions for silicon thermal reduction were achieved at reduction temperature of 1823 K,holding time of 240 min,and slag composition of 45 wt.%SiO_(2),40 wt.%CaO,and 15 wt.%Al_(2)O_(3).The resulting V-Fe-based alloy predominantly consisted of Fe-based phases such as Fe,titanium(Ti),silicon(Si)and manganese(Mn),with Si,V,as well as chromium(Cr)concentrated in the intercrystalline phase of the Fe-based alloy.The recoveries of Fe,Mn,Cr,V,and Ti under the optimal conditions were 96.30%,91.96%,86.53%,80.29%,and 74.82%,respectively.The key components of the V-Fe-based alloy obtained were 41.96 wt.%Si,27.55 wt.%Fe,12.13 wt.%Mn,5.53 wt.%V,4.86 wt.%Cr,and 3.74 wt.%Ti,thereby enabling the comprehensive recovery of the valuable metal from vanadium slag.
基金financial support of the Colombia Scientific Program within the framework of the call Ecosistema Cientifíco (Contract FP44842-218-2018)。
文摘Perovskite solar cells(PSCs) have revolutionized photovoltaic research. As a result, a certified power conversion efficiency(PCE) of 25.5% was recorded in late 2020. Although this efficiency is comparable with silicon solar cells;some issues remain partially unsolved, such as lead toxicity, instability of perovskite materials under continuous illumination, moisture and oxygen, and degradation of the metallic counter electrodes. As an alternative to tackle this last concern, carbon materials have been recently used, due to their good electrical and thermal conductivity, and chemical stability, which makes them one of the most promising materials to replace metallic counter electrodes in the fabrication of PSCs. This review highlights the recent advances of carbon-based PSCs, where the carbon electrode(CE) is the main actor.CEs have become very promising candidates for PSCs;they are mainly fabricated using a simple combination of graphite and carbon black powders embedded in a binder matrix, giving a paste that is then solution-processable, resulting in devices with improved quality stability, when compared to metallic electrodes. In this review, CE’s composition is emphasized, since it can give both, high and lowtemperature processed electrodes, compatible with different device configurations. Finally, the tendencies and opportunities to use CE in PSCs devices are presented.
基金financial support from the Spanish Agencia Estatal de Investigación (AEI) through project PID2023-149895OB-I00a predoctoral research grant from the Public University of Navarrafinancial support under the National Recovery and Resilience Plan (NRRP),Mission 4,Component 2,Investment 1.1,Call for tender No.1409 published on 14.9.2022 by the Italian Ministry of University and Research (MUR),funded by the European Union–NextGenerationEU–Project Title‘‘Fiber optics sensors as a platform for cancer diagnosis and in vitro model testing”–CUP B53D23024170001-Grant Assignment Decree No.1383 adopted on 01/09/2023 by the Italian MUR.
文摘Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.
