Mixed halide perovskites exhibit great potential as materials for the future generation of photovoltaic devices.Yet,their reaction to moisture remains uncertain,necessitating further exploration.While prolonged exposu...Mixed halide perovskites exhibit great potential as materials for the future generation of photovoltaic devices.Yet,their reaction to moisture remains uncertain,necessitating further exploration.While prolonged exposure to moisture can lead to degradation,it can also passivate traps at an optimal moisture level.Here,we use scanning probe microscopy to perform nanoscale moisture-dependent photovoltaic characterizations of open and compressed grain boundary(GB)structures of wide bandgap(FAPbI_(3))_(0.3)(FAPbBr_(3))_(0.7) perovskites.The investigation reveals a decrease in the potential barrier at compact GBs with increasing moisture levels,contrasting with the behavior observed in open GBs.Moreover,the photocurrent distribution over both samples proportionally increases when relative humidity(RH)is raised from 10%to 60%.Notably,following a 24-h exposure at RH 60%,the compact-GB sample demonstrates:i)a reduction in the density of charged trap states at GBs,ii)higher photocurrent,accompanied by a noticeable decrease in current hysteresis compared to the open GB sample,and iii)further enhancement in device efficiency and crystallinity compared to devices with open GBs.These findings suggest that optimizing humidity conditions in engineering the GB chemistry can enhance the optoelectrical properties of GBs,ultimately leading to improved device performance.展开更多
High‐entropy amorphous catalysts(HEACs)integrate multielement synergy with structural disorder,making them promising candidates for water splitting.Their distinctive features—including flexible coordination environm...High‐entropy amorphous catalysts(HEACs)integrate multielement synergy with structural disorder,making them promising candidates for water splitting.Their distinctive features—including flexible coordination environments,tunable electronic structures,abundant unsaturated active sites,and dynamic structural reassembly—collectively enhance electrochemical activity and durability under operating conditions.This review summarizes recent advances in HEACs for hydrogen evolution,oxygen evolution,and overall water splitting,highlighting their disorder-driven advantages over crystalline counterparts.Catalytic performance benchmarks are presented,and mechanistic insights are discussed,focusing on how multimetallic synergy,amorphization effect,and in‐situ reconstruction cooperatively regulate reaction pathways.These insights provide guidance for the rational design of next‐generation amorphous high‐entropy electrocatalysts with improved efficiency and durability.展开更多
Objectives:Decisions regarding CT after nCCRT for locally advanced rectal cancer(LARC)are challenging due to limited evidence guiding treatment.This study aimed to(i)evaluate the predictive performance of machine lear...Objectives:Decisions regarding CT after nCCRT for locally advanced rectal cancer(LARC)are challenging due to limited evidence guiding treatment.This study aimed to(i)evaluate the predictive performance of machine learning(ML)models in patients treated with neoadjuvant concurrent chemoradiotherapy(nCCRT)alone vs.those receiving nCCRT plus chemotherapy(CT),(ii)identify features associated with treatment improvement,and(iii)derive ML-based thresholds for treatment response.Methods:This retrospective study included 409 patients with LARC treated at three affiliated hospitals of Taipei Medical University.Patients were categorised into two groups:nCCRT alone followed by surgery(n=182)and nCCRT plus additional CT(n=227).Thirty-four baseline demographic,tumor,and laboratory variables were analysed.Four ML algorithms(K-Star,Random Forest,Multilayer Perceptron,and Random Committee)were evaluated,while five feature-ranking algorithms identified influential attributes among improved patients across both treatments.Decision Stump and AdaBoostM1 were applied to derive threshold-based patterns.Results:K-Star achieved the highest accuracy for nCCRT alone(80.8%;AUC=0.89),while Random Committee performed best for nCCRT plus CT(77.3%;AUC=0.84).Clinical N stage(cN)ranked highest,followed by Sodium(Na),Glutamic pyruvic transaminase,estimated glomerular filtration rate,body weight,red blood cell count,mean corpuscular hemoglobin concentration,and blood urea nitrogen.Threshold patterns suggested that CT-related improvement aligned with higher lymphocyte percentage and lower platelet distribution width,whereas nCCRT-only improvement aligned with elevated eGFR,GPT,and cN=2.Conclusions:ML-based analysis identified key predictors and demonstrated good model performance,supporting individualised post-nCCRT chemotherapy decisions.展开更多
Ammonia is the cornerstone of modern agriculture,providing a critical nitrogen source for global food production and serving as a key raw material for numerous industrial chemicals.Electrocatalytic nitrate reduction,a...Ammonia is the cornerstone of modern agriculture,providing a critical nitrogen source for global food production and serving as a key raw material for numerous industrial chemicals.Electrocatalytic nitrate reduction,as an environmentally friendly method for synthesizing ammonia,not only mitigates the reliance on current ammonia synthesis processes fed by traditional fossil fuels but also effectively reduces nitrate pollution resulting from agricultural and industrial activities.This review explores the fundamental principles of electrocata lytic nitrate reduction,focusing on the key steps of electron transfer and ammonia formation.Additionally,it summarizes the critical factors influencing the performance and selectivity of the reaction,including the properties of the electrolyte,operating voltage,electrode materials,and design of the electrolytic cell.Further discussion of recent advances in electrocatalysts,including pure metal catalysts,metal oxide catalysts,non-metallic catalysts,and composite catalysts,highlights their significant roles in enhancing both the efficiency and selectivity of electrocata lytic nitrate to ammonia(NRA)reactions.Critical challenges for the industrial NRA trials and further outlooks are outlined to propel this strategy toward real-world applications.Overall,the review provides an in-depth overview and comprehensive understanding of electrocata lytic NRA technology,thereby promoting further advancements and innovations in this domain.展开更多
Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming incre...Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming increasingly common.Among the different types of polymer gears,nylon gears are particularly popular.However,there is currently very limited understanding of the wear resistance of nylon gears and of the impact of the manufacturing method on gear wear performance.The aims of this work are(a)to study the wear process of nylon gears made using the conventional injection molding method and two popularly used AM methods,namely,fused deposition modeling and selective laser sintering,(b)to compare and understand the wear performance by monitoring the evolution of the gear surfaces of the teeth,and(c)to study the effect of wear on the gear dynamics by analyzing gearbox vibration signals.This article presents experimental work,data analysis of the wear processes using molding and image analysis techniques,as well as the vibration data collected during gear wear tests.It also provides key results and further insights into the wear performance of the tested nylon gears.The information gained in this study is useful for better understanding the degradation process of additively manufactured nylon gears.展开更多
This study details a comprehensive approach focusing on the effective separation of light rare earth elements(REEs)via solvent extraction technique.A stock solution containing lanthanum,cerium,neodymium,praseodymium,a...This study details a comprehensive approach focusing on the effective separation of light rare earth elements(REEs)via solvent extraction technique.A stock solution containing lanthanum,cerium,neodymium,praseodymium,and samarium was prepared by dissolving their pure mixed oxide(reclaimed from spent Ni-MH batteries)in a diluted HCl solution.Key extractants,including bis(2,4,4-trimethylpentyl)phosphinic acid(Cyanex 272),Cyanex 572,trialkylphosphine oxide(Cyanex 923),and 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester(PC 88A),along with tributyl phosphate(TBP)as a phase modifier,were utilized to form organic systems.The extraction behavior and separability of these systems at various pH levels as well as their extraction mechanisms were investigated.The results demonstrated a direct relationship between the extraction trend and the experimental pH value,with enhanced selectivity when TBP was added.Notably,Nd and Pr exhibited similar extraction behaviors,with minor deviations from Ce,making their separation difficult to achieve.Sm extraction followed a distinct trend,allowing for its separation from other elements at pH≤2.In contrast,La exhibited a low affinity for coordination with extractants when pH was≤3.5,facilitating the separation of other elements from La,which could then be isolated in the raffinate.Among the studied organic systems,combinations of Cyanex 572 and PC 88A with TBP demonstrated superior performance in element separation.Optimum separation factors were calculated withβ_(Ce/La)=12,βNd/La=87,β_(Pr/La)=127,andβ_(Sm/La)=3191 for the former,andβ_(Sm/Ce)=54,β_(Sm/Nd)=20,andβ_(Sm/Pr)=14 for the latter.These findings provide valuable insights for selecting extraction systems and designing experiments for the effective solvent extraction separation of light REEs from their mixture.展开更多
1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex...1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].展开更多
The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion,surface defects,and low contrast,which hinder the extraction of reliable visual information.The pro...The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion,surface defects,and low contrast,which hinder the extraction of reliable visual information.The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments.This makes conventional OCR techniques and standard deep learning models unreliable.To address these limitations,this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs.A Biorthogonal 4.4(bior4.4)wavelet transform is adaptively tuned using DE to enhance character edge clarity,suppress background noise,and retain morphological structure,thereby improving input quality for subsequent recognition.A structured three-round debate mechanism is further introduced within a multi-agent architecture,employing GPT-4o and Gemini-2.0-flash as role-specialized agents to perform complementary inference and achieve consensus.The proposed system is evaluated on a proprietary dataset of 48 high-resolution images collected under diverse industrial conditions.Experimental results show that the combination of DE-based enhancement and multi-agent collaboration consistently outperforms traditional baselines and ablated models,achieving an F1-score of 94.93%and an LCS accuracy of 93.30%.These results demonstrate the effectiveness of integrating signal processing with multi-agent LLM reasoning to achieve robust and interpretable OCR in visually complex and data-scarce industrial environments.展开更多
Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging manageme...Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.展开更多
Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-sc...Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.展开更多
Fe-doped CuCrO_(2) catalyst CuCr_(1-x)Fe_xO_(2) series were prepared by the sol-gel method with different Fe contents.The structure and properties of the catalysts were investigated by XRD(X-ray diffraction),SEM(scann...Fe-doped CuCrO_(2) catalyst CuCr_(1-x)Fe_xO_(2) series were prepared by the sol-gel method with different Fe contents.The structure and properties of the catalysts were investigated by XRD(X-ray diffraction),SEM(scanning electron microscope),and XPS(X-ray photoelectron spectroscopy)and the purification effect on NO_(x) and PM was measured through simulated emission experiments.The results indicate that CuCrO_(2) catalyst has good catalytic activity,the maximum NO_(x) conversion rate can be up to 28.15%,and the ignition temperature of PM can be reduced to 285℃.When the molecular ratio of Cr:Fe=9:1,the catalyst can achieve better catalytic effect,the maximum NO_(x) conversion rate will be up to 30.25%and the PM ignition temperature can be reduced to 280℃.In addition,the catalytic activity of catalyst supported on different carriers was also studied.The results show that catalyst on SiC foam ceramic carrier has better catalytic activity than that on cordierite honeycomb ceramic carrier.The maximum NO_(x) conversion of CuCrO_(2) and CuCr_(0.9)Fe_(0.1)O_(2) can be increased by 0.72%and 1.33%respectively,and the PM ignition temperature can be further reduced by 15 and 5℃respectively.展开更多
Coking wastewater,characterized by high biological toxicity,poses significant challenges for traditional biological treatment methods.This study developed a novel in-situ immobilized photocatalytic-algae-bacteria cons...Coking wastewater,characterized by high biological toxicity,poses significant challenges for traditional biological treatment methods.This study developed a novel in-situ immobilized photocatalytic-algae-bacteria consortia(P-ABC)system using a polyether polyurethane sponge as a carrier,aiming to enhance biological treatment efficiency for actual coking wastewater.Results showed a 16.8%increase in algal density(up to 1.51×10^(5) cells/mL)in the P-ABC system compared to non-coupled controls,with significantly improved microbial metabolic activity,confirming the carrier's exceptional biocompatibility.Compared to standalone algae-bacteria consortia systems,the P-ABC system achieved higher removal efficiencies for chemical oxygen demand(COD_(Cr),19.8%),total organic carbon(TOC,21.2%),and total nitrogen(TN,30.4%).These findings validate the system's potential for improving stable and efficient treatment of industrial wastewater.Furthermore,this study offers insights into bio-enhanced treatment technologies and provides a reference pathway for integrating advanced oxidation and biological processes.展开更多
Designing high-performance electrocatalysts is one of the key challenges in the development of microbial electrochemical hydrogen production.Transition metal-based(TM-based)electrocatalysts are introduced as an astoni...Designing high-performance electrocatalysts is one of the key challenges in the development of microbial electrochemical hydrogen production.Transition metal-based(TM-based)electrocatalysts are introduced as an astonishing alternative for future catalysts by addressing several disadvantages,like the high cost and low performance of noble metal and metal-free electrocatalysts,respectively.In this critical review,a comprehensive analysis of the major development of all families of TMbased catalysts from the beginning development of microbial electrolysis cells in the last 15 years is presented.Importantly,pivotal design parameters such as selecting efficient synthesis methods based on the type of material,main criteria during each synthesizing method,and the pros and cons of various procedures are highlighted and compared.Moreover,procedures for tuning and tailoring the structures,advanced strategies to promote active sites,and the potential for implementing novel unexplored TM-based hybrid structures suggested.Furthermore,consideration for large-scale application of TM-based catalysts for future mass production,including life cycle assessment,cost assessment,economic analysis,and recently pilot-scale studies were highlighted.Of great importance,the potential of utilizing artificial intelligence and advanced computational methods such as active learning,microkinetic modeling,and physics-informed machine learning in designing high-performance electrodes in successful practices was elucidated.Finally,a conceptual framework for future studies and remaining challenges on different aspects of TM-based electrocatalysts in microbial electrolysis cells is proposed.展开更多
Membrane desalination is an economical and energy-efficient method to meet the current worldwide water scarcity.However,state-of-the-art reverse osmosis membranes are gradually being replaced by novel membrane materia...Membrane desalination is an economical and energy-efficient method to meet the current worldwide water scarcity.However,state-of-the-art reverse osmosis membranes are gradually being replaced by novel membrane materials as a result of ongoing technological advancements.These novel materials possess intrinsic pore structures or can be assembled to form lamellar membrane channels for selective transport of water or solutes(e.g.,NaCl).Still,in real applications,the results fall below the theoretical predictions,and a few properties,including large-scale fabrication,mechanical strength,and chemical stability,also have an impact on the overall effectiveness of those materials.In view of this,we develop a new evaluation framework in the form of radar charts with five dimensions(i.e.,water permeance,water/NaCl selectivity,membrane cost,scale of development,and stability)to assess the advantages,disadvantages,and potential of state-of-the-art and newly developed desalination membranes.In this framework,the reported thin film nanocomposite membranes and membranes developed from novel materials were compared with the state-of-the-art thin film composite membranes.This review will demonstrate the current advancements in novel membrane materials and bridge the gap between different desalination membranes.In this review,we also point out the prospects and challenges of next-generation membranes for desalination applications.We believe that this comprehensive framework may be used as a future reference for designing next-generation desalination membranes and will encourage further research and development in the field of membrane technology,leading to new insights and advancements.展开更多
Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation d...Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed.In this research study a convolutional neural network(CNN)-based model optimised by the genetic algorithm(GA)is addressed.This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge be-tween them.This specifically re-starts the training process from a possibly higher-quality point in different iterations and,consequently,causes potentially yeilds better results at each iteration.The method proposed,which is called cooperative deep neural network(Co-DeepNet),is tested on two types of age prediction problems.Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency.As a result,the mean absolute deviation(MAD)is 1.49 and 3.61 years for training and testing data,respectively,when the healthy data is tested.The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data,respectively.The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements(R^(2),MAD,MSE and RMSE).The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.展开更多
基金support from the Australian Research Council through Discovery Grants and the ARC Centre of Excellence in Future Low-Energy Electronics Technologies(FLEET)supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2021K1A4A7A03093851)+1 种基金J.S.Y.acknowledges the Royal Society research grant(RGS/R1/221369)the support by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MEST)(RS-2023-00257494 and 2022H1D3A2A01082324).
文摘Mixed halide perovskites exhibit great potential as materials for the future generation of photovoltaic devices.Yet,their reaction to moisture remains uncertain,necessitating further exploration.While prolonged exposure to moisture can lead to degradation,it can also passivate traps at an optimal moisture level.Here,we use scanning probe microscopy to perform nanoscale moisture-dependent photovoltaic characterizations of open and compressed grain boundary(GB)structures of wide bandgap(FAPbI_(3))_(0.3)(FAPbBr_(3))_(0.7) perovskites.The investigation reveals a decrease in the potential barrier at compact GBs with increasing moisture levels,contrasting with the behavior observed in open GBs.Moreover,the photocurrent distribution over both samples proportionally increases when relative humidity(RH)is raised from 10%to 60%.Notably,following a 24-h exposure at RH 60%,the compact-GB sample demonstrates:i)a reduction in the density of charged trap states at GBs,ii)higher photocurrent,accompanied by a noticeable decrease in current hysteresis compared to the open GB sample,and iii)further enhancement in device efficiency and crystallinity compared to devices with open GBs.These findings suggest that optimizing humidity conditions in engineering the GB chemistry can enhance the optoelectrical properties of GBs,ultimately leading to improved device performance.
基金supported by the Australian Research Council(ARC)Projects(DP220101139,DP220101142,and LP240100542).
文摘High‐entropy amorphous catalysts(HEACs)integrate multielement synergy with structural disorder,making them promising candidates for water splitting.Their distinctive features—including flexible coordination environments,tunable electronic structures,abundant unsaturated active sites,and dynamic structural reassembly—collectively enhance electrochemical activity and durability under operating conditions.This review summarizes recent advances in HEACs for hydrogen evolution,oxygen evolution,and overall water splitting,highlighting their disorder-driven advantages over crystalline counterparts.Catalytic performance benchmarks are presented,and mechanistic insights are discussed,focusing on how multimetallic synergy,amorphization effect,and in‐situ reconstruction cooperatively regulate reaction pathways.These insights provide guidance for the rational design of next‐generation amorphous high‐entropy electrocatalysts with improved efficiency and durability.
基金funded by the Australian National Health and Medical Research Council(Grant No.GNT1192469)supported by the Research Technology Services at the University of New South Wales Sydney,Google Cloud Research(Award No.GCP19980904)。
文摘Objectives:Decisions regarding CT after nCCRT for locally advanced rectal cancer(LARC)are challenging due to limited evidence guiding treatment.This study aimed to(i)evaluate the predictive performance of machine learning(ML)models in patients treated with neoadjuvant concurrent chemoradiotherapy(nCCRT)alone vs.those receiving nCCRT plus chemotherapy(CT),(ii)identify features associated with treatment improvement,and(iii)derive ML-based thresholds for treatment response.Methods:This retrospective study included 409 patients with LARC treated at three affiliated hospitals of Taipei Medical University.Patients were categorised into two groups:nCCRT alone followed by surgery(n=182)and nCCRT plus additional CT(n=227).Thirty-four baseline demographic,tumor,and laboratory variables were analysed.Four ML algorithms(K-Star,Random Forest,Multilayer Perceptron,and Random Committee)were evaluated,while five feature-ranking algorithms identified influential attributes among improved patients across both treatments.Decision Stump and AdaBoostM1 were applied to derive threshold-based patterns.Results:K-Star achieved the highest accuracy for nCCRT alone(80.8%;AUC=0.89),while Random Committee performed best for nCCRT plus CT(77.3%;AUC=0.84).Clinical N stage(cN)ranked highest,followed by Sodium(Na),Glutamic pyruvic transaminase,estimated glomerular filtration rate,body weight,red blood cell count,mean corpuscular hemoglobin concentration,and blood urea nitrogen.Threshold patterns suggested that CT-related improvement aligned with higher lymphocyte percentage and lower platelet distribution width,whereas nCCRT-only improvement aligned with elevated eGFR,GPT,and cN=2.Conclusions:ML-based analysis identified key predictors and demonstrated good model performance,supporting individualised post-nCCRT chemotherapy decisions.
基金supported by the National Key Research and Development Program of China(2023YFE0120900)the National Natural Science Foundation of China(52377160)+2 种基金the National Natural Science Foundation of China National Young Talents Project(GYKP010)Shaanxi Provincial Natural Science Program(2023-JCYB-425)Xi’an Jiaotong University Young Top Talents Program。
文摘Ammonia is the cornerstone of modern agriculture,providing a critical nitrogen source for global food production and serving as a key raw material for numerous industrial chemicals.Electrocatalytic nitrate reduction,as an environmentally friendly method for synthesizing ammonia,not only mitigates the reliance on current ammonia synthesis processes fed by traditional fossil fuels but also effectively reduces nitrate pollution resulting from agricultural and industrial activities.This review explores the fundamental principles of electrocata lytic nitrate reduction,focusing on the key steps of electron transfer and ammonia formation.Additionally,it summarizes the critical factors influencing the performance and selectivity of the reaction,including the properties of the electrolyte,operating voltage,electrode materials,and design of the electrolytic cell.Further discussion of recent advances in electrocatalysts,including pure metal catalysts,metal oxide catalysts,non-metallic catalysts,and composite catalysts,highlights their significant roles in enhancing both the efficiency and selectivity of electrocata lytic nitrate to ammonia(NRA)reactions.Critical challenges for the industrial NRA trials and further outlooks are outlined to propel this strategy toward real-world applications.Overall,the review provides an in-depth overview and comprehensive understanding of electrocata lytic NRA technology,thereby promoting further advancements and innovations in this domain.
文摘Polymer gears are increasingly replacing metal gears in applications with low to medium torque.Traditionally,polymer gears have been manufactured using injection molding,but additive manufacturing(AM)is becoming increasingly common.Among the different types of polymer gears,nylon gears are particularly popular.However,there is currently very limited understanding of the wear resistance of nylon gears and of the impact of the manufacturing method on gear wear performance.The aims of this work are(a)to study the wear process of nylon gears made using the conventional injection molding method and two popularly used AM methods,namely,fused deposition modeling and selective laser sintering,(b)to compare and understand the wear performance by monitoring the evolution of the gear surfaces of the teeth,and(c)to study the effect of wear on the gear dynamics by analyzing gearbox vibration signals.This article presents experimental work,data analysis of the wear processes using molding and image analysis techniques,as well as the vibration data collected during gear wear tests.It also provides key results and further insights into the wear performance of the tested nylon gears.The information gained in this study is useful for better understanding the degradation process of additively manufactured nylon gears.
基金support from the Australian Research Council’s Industrial Transformation Research Hub funding scheme(project IH190100009).
文摘This study details a comprehensive approach focusing on the effective separation of light rare earth elements(REEs)via solvent extraction technique.A stock solution containing lanthanum,cerium,neodymium,praseodymium,and samarium was prepared by dissolving their pure mixed oxide(reclaimed from spent Ni-MH batteries)in a diluted HCl solution.Key extractants,including bis(2,4,4-trimethylpentyl)phosphinic acid(Cyanex 272),Cyanex 572,trialkylphosphine oxide(Cyanex 923),and 2-ethylhexylphosphonic acid mono-2-ethylhexyl ester(PC 88A),along with tributyl phosphate(TBP)as a phase modifier,were utilized to form organic systems.The extraction behavior and separability of these systems at various pH levels as well as their extraction mechanisms were investigated.The results demonstrated a direct relationship between the extraction trend and the experimental pH value,with enhanced selectivity when TBP was added.Notably,Nd and Pr exhibited similar extraction behaviors,with minor deviations from Ce,making their separation difficult to achieve.Sm extraction followed a distinct trend,allowing for its separation from other elements at pH≤2.In contrast,La exhibited a low affinity for coordination with extractants when pH was≤3.5,facilitating the separation of other elements from La,which could then be isolated in the raffinate.Among the studied organic systems,combinations of Cyanex 572 and PC 88A with TBP demonstrated superior performance in element separation.Optimum separation factors were calculated withβ_(Ce/La)=12,βNd/La=87,β_(Pr/La)=127,andβ_(Sm/La)=3191 for the former,andβ_(Sm/Ce)=54,β_(Sm/Nd)=20,andβ_(Sm/Pr)=14 for the latter.These findings provide valuable insights for selecting extraction systems and designing experiments for the effective solvent extraction separation of light REEs from their mixture.
基金the National Natural Science Foundation of China(NSFC)(62103283)the Australia Research Council’s Discovery Pro-jects Scheme(DP220100355).
文摘1.Background In the chemical industry,process plants-commonly referred to as plantwide systems-typically consist of many process units(unit operations).Driven by the considerable economic efficiency offered by complex and interactive process designs,modern plantwide systems are becoming increasingly sophisticated.The operation of these processes is typically characterized by the complexity of individual units(subsystems)and the intricate interactions between geographically distributed units through networks of material and energy flows,as well as control loops[1].
文摘The effectiveness of industrial character recognition on cast steel is often compromised by factors such as corrosion,surface defects,and low contrast,which hinder the extraction of reliable visual information.The problem is further compounded by the scarcity of large-scale annotated datasets and complex noise patterns in real-world factory environments.This makes conventional OCR techniques and standard deep learning models unreliable.To address these limitations,this study proposes a unified framework that integrates adaptive image preprocessing with collaborative reasoning among LLMs.A Biorthogonal 4.4(bior4.4)wavelet transform is adaptively tuned using DE to enhance character edge clarity,suppress background noise,and retain morphological structure,thereby improving input quality for subsequent recognition.A structured three-round debate mechanism is further introduced within a multi-agent architecture,employing GPT-4o and Gemini-2.0-flash as role-specialized agents to perform complementary inference and achieve consensus.The proposed system is evaluated on a proprietary dataset of 48 high-resolution images collected under diverse industrial conditions.Experimental results show that the combination of DE-based enhancement and multi-agent collaboration consistently outperforms traditional baselines and ablated models,achieving an F1-score of 94.93%and an LCS accuracy of 93.30%.These results demonstrate the effectiveness of integrating signal processing with multi-agent LLM reasoning to achieve robust and interpretable OCR in visually complex and data-scarce industrial environments.
文摘Optimizing photovoltaic(PV)power utilization in battery systems is challenging due to solar intermittency,battery efficiency,and lifespan management.This paper proposes a novel forecast-based battery charging management(BCM)strategy to enhance PV power utilization.A string of Li-ion battery cells with diverse capacities and states of charge(SOC)is contemplated in this constant current/-constant voltage(CC/CV)battery-charging scheme.Significant amounts of PV power are often wasted because the CC/CV mode cannot fully exploit the available power to maintain appropriate charging rates.To address this issue,the proposed BCM algorithm selects an optimal set of battery cells for charging at any given time based on forecasted PV power generation,ensuring maximum power is obtained from the PV system.Additionally,a support vector regression(SVR)-based forecasting model is developed to predict PV power generation precisely.The results indicate that the anticipated BCM strategy achieves an overall utilization rate of 87.47%of the PVgenerated power for battery charging under various weather conditions.
文摘Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.
基金Funded by National Natural Science Foundation of China(No.52494933)。
文摘Fe-doped CuCrO_(2) catalyst CuCr_(1-x)Fe_xO_(2) series were prepared by the sol-gel method with different Fe contents.The structure and properties of the catalysts were investigated by XRD(X-ray diffraction),SEM(scanning electron microscope),and XPS(X-ray photoelectron spectroscopy)and the purification effect on NO_(x) and PM was measured through simulated emission experiments.The results indicate that CuCrO_(2) catalyst has good catalytic activity,the maximum NO_(x) conversion rate can be up to 28.15%,and the ignition temperature of PM can be reduced to 285℃.When the molecular ratio of Cr:Fe=9:1,the catalyst can achieve better catalytic effect,the maximum NO_(x) conversion rate will be up to 30.25%and the PM ignition temperature can be reduced to 280℃.In addition,the catalytic activity of catalyst supported on different carriers was also studied.The results show that catalyst on SiC foam ceramic carrier has better catalytic activity than that on cordierite honeycomb ceramic carrier.The maximum NO_(x) conversion of CuCrO_(2) and CuCr_(0.9)Fe_(0.1)O_(2) can be increased by 0.72%and 1.33%respectively,and the PM ignition temperature can be further reduced by 15 and 5℃respectively.
基金supported by the National Natural Science Foundation of China(No.22076113)Shaanxi Province Key R&D Program Project(No.2020NY-235)。
文摘Coking wastewater,characterized by high biological toxicity,poses significant challenges for traditional biological treatment methods.This study developed a novel in-situ immobilized photocatalytic-algae-bacteria consortia(P-ABC)system using a polyether polyurethane sponge as a carrier,aiming to enhance biological treatment efficiency for actual coking wastewater.Results showed a 16.8%increase in algal density(up to 1.51×10^(5) cells/mL)in the P-ABC system compared to non-coupled controls,with significantly improved microbial metabolic activity,confirming the carrier's exceptional biocompatibility.Compared to standalone algae-bacteria consortia systems,the P-ABC system achieved higher removal efficiencies for chemical oxygen demand(COD_(Cr),19.8%),total organic carbon(TOC,21.2%),and total nitrogen(TN,30.4%).These findings validate the system's potential for improving stable and efficient treatment of industrial wastewater.Furthermore,this study offers insights into bio-enhanced treatment technologies and provides a reference pathway for integrating advanced oxidation and biological processes.
文摘Designing high-performance electrocatalysts is one of the key challenges in the development of microbial electrochemical hydrogen production.Transition metal-based(TM-based)electrocatalysts are introduced as an astonishing alternative for future catalysts by addressing several disadvantages,like the high cost and low performance of noble metal and metal-free electrocatalysts,respectively.In this critical review,a comprehensive analysis of the major development of all families of TMbased catalysts from the beginning development of microbial electrolysis cells in the last 15 years is presented.Importantly,pivotal design parameters such as selecting efficient synthesis methods based on the type of material,main criteria during each synthesizing method,and the pros and cons of various procedures are highlighted and compared.Moreover,procedures for tuning and tailoring the structures,advanced strategies to promote active sites,and the potential for implementing novel unexplored TM-based hybrid structures suggested.Furthermore,consideration for large-scale application of TM-based catalysts for future mass production,including life cycle assessment,cost assessment,economic analysis,and recently pilot-scale studies were highlighted.Of great importance,the potential of utilizing artificial intelligence and advanced computational methods such as active learning,microkinetic modeling,and physics-informed machine learning in designing high-performance electrodes in successful practices was elucidated.Finally,a conceptual framework for future studies and remaining challenges on different aspects of TM-based electrocatalysts in microbial electrolysis cells is proposed.
基金supported by a grant from the Research Grants Council of the Hong Kong Special Administration Region,China(SRFS2021-7S04)Partial support was also received from the Seed Funding for Strategic Interdisciplinary Research Scheme(102010174)+1 种基金Seed Fund for Basic Research(202111159075)of The University of Hong KongIn addition,part of this work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement INTELWAT(No 958454).
文摘Membrane desalination is an economical and energy-efficient method to meet the current worldwide water scarcity.However,state-of-the-art reverse osmosis membranes are gradually being replaced by novel membrane materials as a result of ongoing technological advancements.These novel materials possess intrinsic pore structures or can be assembled to form lamellar membrane channels for selective transport of water or solutes(e.g.,NaCl).Still,in real applications,the results fall below the theoretical predictions,and a few properties,including large-scale fabrication,mechanical strength,and chemical stability,also have an impact on the overall effectiveness of those materials.In view of this,we develop a new evaluation framework in the form of radar charts with five dimensions(i.e.,water permeance,water/NaCl selectivity,membrane cost,scale of development,and stability)to assess the advantages,disadvantages,and potential of state-of-the-art and newly developed desalination membranes.In this framework,the reported thin film nanocomposite membranes and membranes developed from novel materials were compared with the state-of-the-art thin film composite membranes.This review will demonstrate the current advancements in novel membrane materials and bridge the gap between different desalination membranes.In this review,we also point out the prospects and challenges of next-generation membranes for desalination applications.We believe that this comprehensive framework may be used as a future reference for designing next-generation desalination membranes and will encourage further research and development in the field of membrane technology,leading to new insights and advancements.
基金supported by the Universiti Kebangsaan Malaysia(DIP-2016-024).
文摘Prediction of the age of each individual is possible using the changing pattern of DNA methylation with age.In this paper an age prediction approach to work out multivariate regression problems using DNA methylation data is developed.In this research study a convolutional neural network(CNN)-based model optimised by the genetic algorithm(GA)is addressed.This paper contributes to enhancing age prediction as a regression problem using a union of two CNNs and exchanging knowledge be-tween them.This specifically re-starts the training process from a possibly higher-quality point in different iterations and,consequently,causes potentially yeilds better results at each iteration.The method proposed,which is called cooperative deep neural network(Co-DeepNet),is tested on two types of age prediction problems.Sixteen datasets containing 1899 healthy blood samples and nine datasets containing 2395 diseased blood samples are employed to examine the method's efficiency.As a result,the mean absolute deviation(MAD)is 1.49 and 3.61 years for training and testing data,respectively,when the healthy data is tested.The diseased blood data show MAD results of 3.81 and 5.43 years for training and testing data,respectively.The results of the Co-DeepNet are compared with six other methods proposed in previous studies and a single CNN using four prediction accuracy measurements(R^(2),MAD,MSE and RMSE).The effectiveness of the Co-DeepNet and superiority of its results is proved through the statistical analysis.
