In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Inf...In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductiv...Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.展开更多
Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effe...Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.展开更多
To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃...To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challe...Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challenging.Herein,a support electron inductive effect of Pd-Mn/Ni foam catalyst was proposed via in-situ Mn doping to optimize the electronic structure of the Ni foam(NF),which can inductive regulation of Pd for improving the EHDC performance.The mass activity and current efficiency of Pd-Mn/NF catalyst are 2.91 and 1.34 times superior to that of Pd/NF with 2,4-dichlorophenol as model compound,respectively.The Mn-doped interlayer optimized the electronic structure of Pd by bringing the d-state closer to the Fermi level than Pd on the NF surface,which optimizied the binding of EHDC intermediates.Additionally,the Mn-doped interlayer acted as a promoter for generating H∗and accelerating the EHDC reaction.This work presents a simple and effective regulation strategy for constructing high-efficient cathode catalyst for the EHDC of chlorinated organic compounds.展开更多
BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequen...BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequently influenced by the patient's nutritional status and the quality of nursing care provided.AIM To examine the comprehensive impact of personalized nutritional support and nursing strategies on the postoperative rehabilitation of patients with liver cancer.METHODS In this study,a retrospective comparative analysis was conducted involving 60 post-operative liver cancer patients.The subjects were selected as subjects and divided into two groups based on differing nursing interventions,with each group comprising 30 patients.The control group received standard nutritional support and care,whereas the experimental group received individualized nutritional support and nursing strategies.The study aimed to evaluate the impact of individualized nutrition by comparing the rehabilitation indices,nutritional status,quality of life(QoL),and complication rates between the two groups.RESULTS The results showed that the recovery index of the experimental group was significantly better than that of the control group 2 weeks after surgery,and the average liver function recovery index of the experimental group was 85.significantly higher than that of the control group(73.67±7.19).In terms of nutritional status,the serum albumin level and body weight stabilization rate of the experimental group were also significantly higher than those of the control group,which were 42.33±2.4 g/L and 93.3%,respectively,compared with 36.01±3.85 g/L and 76.7%of the control group.In addition,the average QoL score of the experimental group was 84.66±3.7 points,which was significantly higher than that of the control group(70.92±4.28 points).At the psychological level,the average anxiety score of the experimental group was 1.17±0.29,and the average depression score was 1.47±0.4,which were significantly lower than the 2.26±0.42 and 2.57±0.45 of the control group.This showed that patients in the experimental group were better relieved of anxiety and depression under the individualized nutrition support and nursing strategy.More importantly,the complication rate in the experimental group was only 10%,much lower than the 33.3%in the control group.CONCLUSION Personalized nutritional support and tailored nursing strategies significantly enhance the postoperative rehabilitation of liver cancer patients.Consequently,it is recommended to implement and advocate for these individualized approaches to improve both the recovery outcomes and QoL for these patients.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice,particularly in achieving accurate early diagnosis and risk stratification.While traditional approach...The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice,particularly in achieving accurate early diagnosis and risk stratification.While traditional approaches rely heavily on subjective interpretations and variable expertise,machine learning(ML)has emerged as a transformative tool in healthcare.We conducted a comprehensive review of published literature on ML applications in esophageal diseases,analyzing technical approaches,validation methods,and clinical outcomes.ML demonstrates superior performance:In gastroesophageal reflux disease,ML models achieve 80%-90%accuracy in potential of hydrogen-impedance analysis and endoscopic grading;for Barrett’s esophagus,ML-based approaches show 88%-95% accuracy in invasive diagnostics and 77%-85% accuracy in non-invasive screening.In esophageal cancer,ML improves early detection and survival prediction by 6%-10% compared to traditional methods.Novel applications in achalasia and esophageal varices demonstrate promising results in automated diagnosis and risk stratification,with accuracy rates exceeding 85%.While challenges persist in data standardization,model interpretability,and clinical integration,emerging solutions in federated learning and explainable artificial intelligence offer promising pathways forward.The continued evolution of these technologies,coupled with rigorous validation and thoughtful implementation,may fundamentally transform our approach to esophageal disease management in the era of precision medicine.展开更多
Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3...Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.展开更多
Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
There is a strong evidence supporting the hypothesis of synaptic dysfunction as a major contributor to neural circuit and network disruption underlying emotional and mood dysregulation in psychiatric disorders(Simmons...There is a strong evidence supporting the hypothesis of synaptic dysfunction as a major contributor to neural circuit and network disruption underlying emotional and mood dysregulation in psychiatric disorders(Simmons et al.,2024).Diverse sets of distinct molecular signaling pathways converge on the synapse to regulate synaptogenesis,synaptic function,and synaptic plasticity in brain regions and circuits through complex interactions organized by numerous multivalent protein scaffolds,including the family of proteins known as A-kinase anchoring proteins(AKAPs).展开更多
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al...In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.展开更多
BACKGROUND Traumatic injuries,such as falling,car accidents,and crushing mostly cause spinal fractures in young and middle-aged people,and>50%of them are thoracolumbar fractures.This kind of fracture is easily comb...BACKGROUND Traumatic injuries,such as falling,car accidents,and crushing mostly cause spinal fractures in young and middle-aged people,and>50%of them are thoracolumbar fractures.This kind of fracture is easily combined with serious injuries to peripheral nerves and soft tissues,which causes paralysis of the lower limbs if there is no timely rehabilitation treatment.Young patients with thoracolumbar fractures find it difficult to recover after the operation,and they are prone to depression,low self-esteem,and other negative emotions.AIM To investigate the association between anxiety,depression,and social stress in young patients with thoracolumbar spine fractures and the effect on rehabilitation outcomes.METHODS This study retrospectively analyzed 100 patients admitted to the orthopedic department of Honghui Hospital,Xi’an Jiaotong University who underwent thoracolumbar spine fracture surgery from January 2022 to June 2023.The general data of the patients were assessed with the Hamilton anxiety scale(HAMA),Hamilton depression scale(HAMD),life events scale,and social support rating scale(SSRS)to identify the correlation between anxiety,depression scores,and social stress and social support.The Japanese Orthopedic Association(JOA)was utilized to evaluate the rehabilitation outcomes of the patients and to analyze the effects of anxiety and depression scores on rehabilitation.RESULTS According to the scores of HAMD and HAMA in all patients,the prevalence of depression in patients was 39%(39/100),and the prevalence of anxiety was 49%(49/100).Patients were categorized into non-depression(n=61)and depression(n=39),non-anxiety(n=51),and anxiety(n=49)groups.Statistically significant differences in gender,occupation,Pittsburgh Sleep Quality Index(PSQI)score,and monthly family income were observed between the non-depression and depression groups(P<0.05).A significant difference in occupation and PSQI score was found between the non-anxiety and anxiety groups.Both depression(r=0.207,P=0.038)and anxiety scores(r=0.473,P<0.001)were significantly and positively correlated with negative life events.The difference in negative life event scores as well as SSRS total and item scores was statist-ically significant between patients in the non-depression and depression groups(P<0.05).The difference between the non-anxiety and anxiety groups was statistically significant(P<0.05)in the negative life event scores as well as the total SSRS scores.Additionally,JOA scores were significantly lower in both anxious and depressed patients.CONCLUSION Young patients with thoracolumbar fractures are prone to anxiety and depression.Patients’anxiety and depression are closely associated with social pressure,which reduces the life pressure of young patients with thoracolumbar fractures,enhances social support,and improves the psychology of anxiety and depression.,which affects patients’recovery.展开更多
Mesoporous carbon supports mitigate platinum(Pt)sulfonic poisoning through nanopore-confined Pt deposition,yet their morphological impacts on oxygen transport remain unclear.This study integrates carbon support morpho...Mesoporous carbon supports mitigate platinum(Pt)sulfonic poisoning through nanopore-confined Pt deposition,yet their morphological impacts on oxygen transport remain unclear.This study integrates carbon support morphology simulation with an enhanced agglomerate model to establish a mathematical framework elucidating pore evolution,Pt utilization,and oxygen transport in catalyst layers.Results demonstrate dominant local mass transport resistance governed by three factors:(1)active site density dictating oxygen flux;(2)ionomer film thickness defining shortest transport path;(3)ionomer-to-Pt surface area ratio modulating practical pathway length.At low ionomer-to-carbon(I/C)ratios,limited active sites elevate resistance(Factor 1 dominant).Higher I/C ratios improve the ionomer coverage but eventually thicken ionomer films,degrading transport(Factors 2–3 dominant).The results indicate that larger carbon particles result in a net increase in local transport resistance by reducing external surface area and increasing ionomer thickness.As the proportion of Pt situated in nanopores or the Pt mass fraction increases,elevated Pt density inside the nanopores exacerbates pore blockage.This leads to the increased transport resistance by reducing active sites,and increasing ionomer thickness and surface area.Lower Pt loading linearly intensifies oxygen flux resistance.The model underscores the necessity to optimize support morphology,Pt distribution,and ionomer content to prevent pore blockage while balancing catalytic activity and transport efficiency.These insights provide a systematic approach for designing high-performance mesoporous carbon catalysts.展开更多
The preferential oxidation of CO(CO-PROX)reaction is a cost-effective method for eliminating trace amounts of CO from the fuel H2.Pt-based catalysts have been extensively studied for COPROX,with their activity influen...The preferential oxidation of CO(CO-PROX)reaction is a cost-effective method for eliminating trace amounts of CO from the fuel H2.Pt-based catalysts have been extensively studied for COPROX,with their activity influenced by the morphology of the support.Hydrothermal synthesis was employed to produce different morphologies ofγ-Al_(2)O_(3):flower-likeγ-Al_(2)O_(3)(f)exposing(110)crystal faces,sheet-likeγ-Al_(2)O_(3)(s)revealing(100)crystal faces,and rod-likeγ-Al_(2)O_(3)(r)displaying(111)crystal faces,followed by loading PtCo nanoparticles.The exposed crystal faces of the support impact the alloying degree of the PtCo nanoparticles,and an increase in the alloying degree correlates with enhanced catalyst reactivity.Pt_(3)Co intermetallic compounds were identified onγ-Al_(2)O_(3)(f)exposing(110)crystal faces,and PtCo/γ-Al_(2)O_(3)(f)showed high catalytic activity in the CO-PROX reaction,achieving 100%CO conversion across a broad temperature range of 50−225°C.In contrast,only partial alloying of PtCo was observed onγ-Al_(2)O_(3)(s).Furthermore,no alloying between Pt and Co occurred in PtCo/γ-Al_(2)O_(3)(r),resulting in a reaction rate at 50°C that was merely 11%of that of PtCo/γ-Al_(2)O_(3)(f).The formation of Pt3Co intermetallic compounds led to a more oxidized state of Pt,which significantly diminished the adsorption of CO on Pt and augmented the active oxygen species,thereby facilitating the selective oxidation of CO.展开更多
With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Ni...With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Nickel-based catalysts are renowned for their outstanding activity and selectivity in this process.The impact of metal-support interaction(MSI),on Ni-based catalyst performance has been extensively researched and debated recently.This paper reviews the recent research progress of MSI on Ni-based catalysts and their characterization and modulation strategies in catalytic reactions.From the perspective of MSI,the effects of different carriers(metal oxides,carbon materials and molecular sieves,etc.)are introduced on the dispersion and surface structure of Ni active metal particles,and the effect of MSI on the activity and stability of DRM reactions on Ni-based catalysts is discussed in detail.Future research should focus on better understanding and controlling MSI to improve the performance and durability of nickel-based catalysts in CH_(4)-CO_(2)reforming,advancing cleaner energy technologies.展开更多
Objective:Non-suicidal self-injury(NSSI)is a mental health problem that often occurs in adolescents in Indonesia.Even though NSSI does not have the intention of suicide,some cases result in death.This research aimed t...Objective:Non-suicidal self-injury(NSSI)is a mental health problem that often occurs in adolescents in Indonesia.Even though NSSI does not have the intention of suicide,some cases result in death.This research aimed to identify the predisposition and precipitation factors associated with the dynamics of NSSI behaviors among adolescents.Methods:This research is an explanatory research design with a cross-sectional study.Out of 4000 students,904 students from State High School and Vocational School in Central Java Province were selected as respondents using non-probability sampling and a purposive sampling approach.This research was carried out using Google Forms from September to November 2023.The instrument used was the Self-Harm Inventory(SHI).Data analysis in this study used the chi-squared test.Results:The results of this study indicate that the most significant predisposition factors of NSSI behaviors experienced by respondents in this study are introverted personality and the environment that supports NSSI behaviors.In contrast,the precipitation factors are bullying and deep disappointment.Otherwise,based on the data analysis,the maladaptive coping mechanism(P-value=0.029)has become a predisposition factor related to the dynamics of NSSI behaviors.In contrast,bullying(P-value=0.000)and deep disappointment(P-value=0.000)still become precipitation factors related to the dynamics of NSSI behaviors.Conclusions:The results of this study can be evidence-based for stakeholders to provide interventions,especially for the vulnerable population affected by NSSI behaviors.展开更多
文摘In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the National Natural Science Foundation of China(No.52127816),the National Key Research and Development Program of China(No.2020YFA0715000)the National Natural Science and Hong Kong Research Grant Council Joint Research Funding Project of China(No.5181101182)the NSFC/RGC Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the National Natural Science Foundation of China(No.N_PolyU513/18).
文摘Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.
基金funded by the National Natural Science Foundation of China (No. 52304133)the National Key R&D Program of China (No. 2022YFC3004605)the Department of Science and Technology of Liaoning Province (No. 2023-BS-083)。
文摘Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.
基金The National Natural Science Foundation of China(No.52338011).
文摘To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
基金supported by the National Natural Science Foundation of China(Nos.22178388 and 22108306)Taishan Scholars Program of Shandong Province(No.tsqn201909065)Chongqing Science and Technology Bureau(No.cstc2019jscx-gksb X0032).
文摘Structural regulation of Pd-based electrocatalytic hydrodechlorination(EHDC)catalyst for constructing high-efficient cathode materials with low noble metal content and high atom utilization is crucial but still challenging.Herein,a support electron inductive effect of Pd-Mn/Ni foam catalyst was proposed via in-situ Mn doping to optimize the electronic structure of the Ni foam(NF),which can inductive regulation of Pd for improving the EHDC performance.The mass activity and current efficiency of Pd-Mn/NF catalyst are 2.91 and 1.34 times superior to that of Pd/NF with 2,4-dichlorophenol as model compound,respectively.The Mn-doped interlayer optimized the electronic structure of Pd by bringing the d-state closer to the Fermi level than Pd on the NF surface,which optimizied the binding of EHDC intermediates.Additionally,the Mn-doped interlayer acted as a promoter for generating H∗and accelerating the EHDC reaction.This work presents a simple and effective regulation strategy for constructing high-efficient cathode catalyst for the EHDC of chlorinated organic compounds.
文摘BACKGROUND Hepatocellular carcinoma ranks among the most prevalent malignant neoplasms.Surgical intervention constitutes a critical therapeutic approach for this condition.Nonetheless,postoperative recovery is frequently influenced by the patient's nutritional status and the quality of nursing care provided.AIM To examine the comprehensive impact of personalized nutritional support and nursing strategies on the postoperative rehabilitation of patients with liver cancer.METHODS In this study,a retrospective comparative analysis was conducted involving 60 post-operative liver cancer patients.The subjects were selected as subjects and divided into two groups based on differing nursing interventions,with each group comprising 30 patients.The control group received standard nutritional support and care,whereas the experimental group received individualized nutritional support and nursing strategies.The study aimed to evaluate the impact of individualized nutrition by comparing the rehabilitation indices,nutritional status,quality of life(QoL),and complication rates between the two groups.RESULTS The results showed that the recovery index of the experimental group was significantly better than that of the control group 2 weeks after surgery,and the average liver function recovery index of the experimental group was 85.significantly higher than that of the control group(73.67±7.19).In terms of nutritional status,the serum albumin level and body weight stabilization rate of the experimental group were also significantly higher than those of the control group,which were 42.33±2.4 g/L and 93.3%,respectively,compared with 36.01±3.85 g/L and 76.7%of the control group.In addition,the average QoL score of the experimental group was 84.66±3.7 points,which was significantly higher than that of the control group(70.92±4.28 points).At the psychological level,the average anxiety score of the experimental group was 1.17±0.29,and the average depression score was 1.47±0.4,which were significantly lower than the 2.26±0.42 and 2.57±0.45 of the control group.This showed that patients in the experimental group were better relieved of anxiety and depression under the individualized nutrition support and nursing strategy.More importantly,the complication rate in the experimental group was only 10%,much lower than the 33.3%in the control group.CONCLUSION Personalized nutritional support and tailored nursing strategies significantly enhance the postoperative rehabilitation of liver cancer patients.Consequently,it is recommended to implement and advocate for these individualized approaches to improve both the recovery outcomes and QoL for these patients.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金Supported by the Central Funds Guiding the Local Science and Technology Development,No.202207AB110017Key Research and Development Program of Yunnan,No.202302AD080004+1 种基金Yunnan Academician and Expert Workstation,No.202205AF150023the Scientific and Technological Innovation Team in Kunming Medical University,No.CXTD202215.
文摘The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice,particularly in achieving accurate early diagnosis and risk stratification.While traditional approaches rely heavily on subjective interpretations and variable expertise,machine learning(ML)has emerged as a transformative tool in healthcare.We conducted a comprehensive review of published literature on ML applications in esophageal diseases,analyzing technical approaches,validation methods,and clinical outcomes.ML demonstrates superior performance:In gastroesophageal reflux disease,ML models achieve 80%-90%accuracy in potential of hydrogen-impedance analysis and endoscopic grading;for Barrett’s esophagus,ML-based approaches show 88%-95% accuracy in invasive diagnostics and 77%-85% accuracy in non-invasive screening.In esophageal cancer,ML improves early detection and survival prediction by 6%-10% compared to traditional methods.Novel applications in achalasia and esophageal varices demonstrate promising results in automated diagnosis and risk stratification,with accuracy rates exceeding 85%.While challenges persist in data standardization,model interpretability,and clinical integration,emerging solutions in federated learning and explainable artificial intelligence offer promising pathways forward.The continued evolution of these technologies,coupled with rigorous validation and thoughtful implementation,may fundamentally transform our approach to esophageal disease management in the era of precision medicine.
基金Research Institute for Smart Energy(CDB2)the grant from the Research Institute for Advanced Manufacturing(CD8Z)+4 种基金the grant from the Carbon Neutrality Funding Scheme(WZ2R)at The Hong Kong Polytechnic Universitysupport from the Hong Kong Polytechnic University(CD9B,CDBZ and WZ4Q)the National Natural Science Foundation of China(22205187)Shenzhen Municipal Science and Technology Innovation Commission(JCYJ20230807140402006)Start-up Foundation for Introducing Talent of NUIST and Natural Science Foundation of Jiangsu Province of China(BK20230426).
文摘Catalyst–support interaction plays a crucial role in improving the catalytic activity of oxygen evolution reaction(OER).Here we modulate the catalyst–support interaction in polyaniline-supported Ni_(3)Fe oxide(Ni_(3)Fe oxide/PANI)with a robust hetero-interface,which significantly improves oxygen evolution activities with an overpotential of 270 mV at 10 mA cm^(-2)and specific activity of 2.08 mA cm_(ECSA)^(-2)at overpotential of 300 mV,3.84-fold that of Ni_(3)Fe oxide.It is revealed that the catalyst–support interaction between Ni_(3)Fe oxide and PANI support enhances the Ni–O covalency via the interfacial Ni–N bond,thus promoting the charge and mass transfer on Ni_(3)Fe oxide.Considering the excellent activity and stability,rechargeable Zn-air batteries with optimum Ni_(3)Fe oxide/PANI are assembled,delivering a low charge voltage of 1.95 V to cycle for 400 h at 10 mA cm^(-2).The regulation of the effect of catalyst–support interaction on catalytic activity provides new possibilities for the future design of highly efficient OER catalysts.
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
基金supported by the National Institute of Mental Health (NIH/NIMH)the National Institute of Neurological Disorders and Stroke(NIH/NINDS):Grants#R21 MH132136 to FSN and R01 MH123700 and R01 NS040701 to MLD
文摘There is a strong evidence supporting the hypothesis of synaptic dysfunction as a major contributor to neural circuit and network disruption underlying emotional and mood dysregulation in psychiatric disorders(Simmons et al.,2024).Diverse sets of distinct molecular signaling pathways converge on the synapse to regulate synaptogenesis,synaptic function,and synaptic plasticity in brain regions and circuits through complex interactions organized by numerous multivalent protein scaffolds,including the family of proteins known as A-kinase anchoring proteins(AKAPs).
基金supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.
文摘In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.
文摘BACKGROUND Traumatic injuries,such as falling,car accidents,and crushing mostly cause spinal fractures in young and middle-aged people,and>50%of them are thoracolumbar fractures.This kind of fracture is easily combined with serious injuries to peripheral nerves and soft tissues,which causes paralysis of the lower limbs if there is no timely rehabilitation treatment.Young patients with thoracolumbar fractures find it difficult to recover after the operation,and they are prone to depression,low self-esteem,and other negative emotions.AIM To investigate the association between anxiety,depression,and social stress in young patients with thoracolumbar spine fractures and the effect on rehabilitation outcomes.METHODS This study retrospectively analyzed 100 patients admitted to the orthopedic department of Honghui Hospital,Xi’an Jiaotong University who underwent thoracolumbar spine fracture surgery from January 2022 to June 2023.The general data of the patients were assessed with the Hamilton anxiety scale(HAMA),Hamilton depression scale(HAMD),life events scale,and social support rating scale(SSRS)to identify the correlation between anxiety,depression scores,and social stress and social support.The Japanese Orthopedic Association(JOA)was utilized to evaluate the rehabilitation outcomes of the patients and to analyze the effects of anxiety and depression scores on rehabilitation.RESULTS According to the scores of HAMD and HAMA in all patients,the prevalence of depression in patients was 39%(39/100),and the prevalence of anxiety was 49%(49/100).Patients were categorized into non-depression(n=61)and depression(n=39),non-anxiety(n=51),and anxiety(n=49)groups.Statistically significant differences in gender,occupation,Pittsburgh Sleep Quality Index(PSQI)score,and monthly family income were observed between the non-depression and depression groups(P<0.05).A significant difference in occupation and PSQI score was found between the non-anxiety and anxiety groups.Both depression(r=0.207,P=0.038)and anxiety scores(r=0.473,P<0.001)were significantly and positively correlated with negative life events.The difference in negative life event scores as well as SSRS total and item scores was statist-ically significant between patients in the non-depression and depression groups(P<0.05).The difference between the non-anxiety and anxiety groups was statistically significant(P<0.05)in the negative life event scores as well as the total SSRS scores.Additionally,JOA scores were significantly lower in both anxious and depressed patients.CONCLUSION Young patients with thoracolumbar fractures are prone to anxiety and depression.Patients’anxiety and depression are closely associated with social pressure,which reduces the life pressure of young patients with thoracolumbar fractures,enhances social support,and improves the psychology of anxiety and depression.,which affects patients’recovery.
基金supported by the Program of Ministry of Science and Technology of China(No.2023YFB2504200)support of Shanghai Rising-Star Program(Grant No.24QB2703200)the Major Science and Technology Projects of Yunnan Province(No.202302AH360001).
文摘Mesoporous carbon supports mitigate platinum(Pt)sulfonic poisoning through nanopore-confined Pt deposition,yet their morphological impacts on oxygen transport remain unclear.This study integrates carbon support morphology simulation with an enhanced agglomerate model to establish a mathematical framework elucidating pore evolution,Pt utilization,and oxygen transport in catalyst layers.Results demonstrate dominant local mass transport resistance governed by three factors:(1)active site density dictating oxygen flux;(2)ionomer film thickness defining shortest transport path;(3)ionomer-to-Pt surface area ratio modulating practical pathway length.At low ionomer-to-carbon(I/C)ratios,limited active sites elevate resistance(Factor 1 dominant).Higher I/C ratios improve the ionomer coverage but eventually thicken ionomer films,degrading transport(Factors 2–3 dominant).The results indicate that larger carbon particles result in a net increase in local transport resistance by reducing external surface area and increasing ionomer thickness.As the proportion of Pt situated in nanopores or the Pt mass fraction increases,elevated Pt density inside the nanopores exacerbates pore blockage.This leads to the increased transport resistance by reducing active sites,and increasing ionomer thickness and surface area.Lower Pt loading linearly intensifies oxygen flux resistance.The model underscores the necessity to optimize support morphology,Pt distribution,and ionomer content to prevent pore blockage while balancing catalytic activity and transport efficiency.These insights provide a systematic approach for designing high-performance mesoporous carbon catalysts.
基金supported by the National Natural Science Foundation of China(22376063,21976057)the Fund of the National Engineering Laboratory for Mobile Source Emission Control Technology(NELMS2020A05)Fundamental Research Funds for the Central Universities.
文摘The preferential oxidation of CO(CO-PROX)reaction is a cost-effective method for eliminating trace amounts of CO from the fuel H2.Pt-based catalysts have been extensively studied for COPROX,with their activity influenced by the morphology of the support.Hydrothermal synthesis was employed to produce different morphologies ofγ-Al_(2)O_(3):flower-likeγ-Al_(2)O_(3)(f)exposing(110)crystal faces,sheet-likeγ-Al_(2)O_(3)(s)revealing(100)crystal faces,and rod-likeγ-Al_(2)O_(3)(r)displaying(111)crystal faces,followed by loading PtCo nanoparticles.The exposed crystal faces of the support impact the alloying degree of the PtCo nanoparticles,and an increase in the alloying degree correlates with enhanced catalyst reactivity.Pt_(3)Co intermetallic compounds were identified onγ-Al_(2)O_(3)(f)exposing(110)crystal faces,and PtCo/γ-Al_(2)O_(3)(f)showed high catalytic activity in the CO-PROX reaction,achieving 100%CO conversion across a broad temperature range of 50−225°C.In contrast,only partial alloying of PtCo was observed onγ-Al_(2)O_(3)(s).Furthermore,no alloying between Pt and Co occurred in PtCo/γ-Al_(2)O_(3)(r),resulting in a reaction rate at 50°C that was merely 11%of that of PtCo/γ-Al_(2)O_(3)(f).The formation of Pt3Co intermetallic compounds led to a more oxidized state of Pt,which significantly diminished the adsorption of CO on Pt and augmented the active oxygen species,thereby facilitating the selective oxidation of CO.
基金supported by the Natural Science Foundation of Shanxi Province(202203021221155)the Foundation of National Key Laboratory of High Efficiency and Low Carbon Utilization of Coal(J23-24-902)。
文摘With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Nickel-based catalysts are renowned for their outstanding activity and selectivity in this process.The impact of metal-support interaction(MSI),on Ni-based catalyst performance has been extensively researched and debated recently.This paper reviews the recent research progress of MSI on Ni-based catalysts and their characterization and modulation strategies in catalytic reactions.From the perspective of MSI,the effects of different carriers(metal oxides,carbon materials and molecular sieves,etc.)are introduced on the dispersion and surface structure of Ni active metal particles,and the effect of MSI on the activity and stability of DRM reactions on Ni-based catalysts is discussed in detail.Future research should focus on better understanding and controlling MSI to improve the performance and durability of nickel-based catalysts in CH_(4)-CO_(2)reforming,advancing cleaner energy technologies.
文摘Objective:Non-suicidal self-injury(NSSI)is a mental health problem that often occurs in adolescents in Indonesia.Even though NSSI does not have the intention of suicide,some cases result in death.This research aimed to identify the predisposition and precipitation factors associated with the dynamics of NSSI behaviors among adolescents.Methods:This research is an explanatory research design with a cross-sectional study.Out of 4000 students,904 students from State High School and Vocational School in Central Java Province were selected as respondents using non-probability sampling and a purposive sampling approach.This research was carried out using Google Forms from September to November 2023.The instrument used was the Self-Harm Inventory(SHI).Data analysis in this study used the chi-squared test.Results:The results of this study indicate that the most significant predisposition factors of NSSI behaviors experienced by respondents in this study are introverted personality and the environment that supports NSSI behaviors.In contrast,the precipitation factors are bullying and deep disappointment.Otherwise,based on the data analysis,the maladaptive coping mechanism(P-value=0.029)has become a predisposition factor related to the dynamics of NSSI behaviors.In contrast,bullying(P-value=0.000)and deep disappointment(P-value=0.000)still become precipitation factors related to the dynamics of NSSI behaviors.Conclusions:The results of this study can be evidence-based for stakeholders to provide interventions,especially for the vulnerable population affected by NSSI behaviors.
