The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants.In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environm...The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants.In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environment,the hot-extruded fine-grained Mg-Zn-Y-Nd alloy microtubes,which are employed to manufacture vascular stents,were tested under radial compressive stress in the dynamic Hanks'Balanced Salt Solution(HBSS).The results revealed that the high flow rate accelerates the degradation of Mg alloy microtubes and its degradation is sensitive to radial compressive stress.These results contribute to understanding the service performance of Mg alloys as vascular stent materials.展开更多
In this study,interconnected porous Mg-2Zn-xY alloys with different phase compositions were prepared by various Y additions(x=0.4,3,and 6 wt.%)to adjust the compressive properties and energy absorption characteristics...In this study,interconnected porous Mg-2Zn-xY alloys with different phase compositions were prepared by various Y additions(x=0.4,3,and 6 wt.%)to adjust the compressive properties and energy absorption characteristics.Several characterization methods were then applied to identify the microstructure of the porous Mg-Zn-Y and describe the details of the second phase.Compressive tests were performed at room temperature(RT),200℃,and 300℃to study the impact of the Y addition and testing temperature on the compressive properties of the porous Mg-Zn-Y.The experimental results showed that a high Y content promotes a microstructure refinement and increases the volume fraction of the second phase.When the Y content increases,different Mg-Zn-Y ternary phases appear:I-phase(Mg_(3)Zn_(6)Y),W-phase(Mg_(3)Zn_(3)Y_(2)),and LPSO phase(Mg_(12)ZnY).When the Y content ranges between 0.4%and 6%,the compressive strength increases from 6.30MPa to 9.23 MPa,and the energy absorption capacity increases from 7.33 MJ/m^(3)to 10.97 MJ/m^(3)at RT,which is mainly attributed to the phase composition and volume fraction of the second phase.However,the average energy absorption efficiency is independent of the Y content.In addition,the compressive deformation behaviors of the porous Mg-Zn-Y are altered by the testing temperature.The compressive strength and energy absorption capacity of the porous Mg-Zn-Y decrease due to the softening effect of the high temperature on the struts.The deformation behaviors at different temperatures are finally observed to reflect the failure mechanisms of the struts.展开更多
The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of ...The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventio...Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.展开更多
River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study d...River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.展开更多
This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf o...This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.展开更多
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining envir...Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.展开更多
Chronic compressive spinal cord injury in compressive cervical myelopathy conditions can lead to rapid neurological deterioration in the early phase,followed by partial self-recovery,and ultimately an equilibrium stat...Chronic compressive spinal cord injury in compressive cervical myelopathy conditions can lead to rapid neurological deterioration in the early phase,followed by partial self-recovery,and ultimately an equilibrium state of neurological dysfunction.Ferroptosis is a crucial pathological process in many neurodegenerative diseases;however,its role in chro nic compressive spinal cord injury remains unclear.In this study,we established a chronic compressive spinal cord injury rat model,which displayed its most severe behavioral and electrophysiological dysfunction at 4 wee ks and partial recovery at 8 weeks after compression.Bulk RNA sequencing data identified enriched functional pathways,including ferroptosis,presynapse,and postsynaptic membrane activity at both 4 and 8 wee ks following chro nic compressive spinal co rd injury.Tra nsmission electron microscopy and malondialdehyde quantification assay confirmed that ferroptosis activity peaked at 4 weeks and was attenuated at 8 weeks after chronic compression.Ferro ptosis activity was negatively correlated with behavioral score.Immunofluorescence,quantitative polymerase chain reaction,and western blotting showed that expression of the anti-ferroptosis molecules,glutathione peroxidase 4(GPX4) and MAF BZIP transcription factor G(MafG),in neuro ns was suppressed at 4 weeks and upregulated at 8 weeks following spinal co rd compression.There was a positive correlation between the expression of these two molecules,suggesting that they may work together to contribute to functional recovery following chronic compressive spinal cord injury.In conclusion,our study determined the genome-wide expression profile and fe rroptosis activity of a consistently compressed spinal cord at different time points.The results showed that anti-fe rroptosis genes,specifically GPX4 and MafG,may be involved in spontaneous neurological recovery at 8 weeks of chronic compressive spinal cord injury.These findings contribute to a better understanding of the mechanisms underlying chronic compressive spinal cord injury and may help identify new therapeutic targets for compressive cervical myelopathy.展开更多
A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. F...A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.展开更多
The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the rel...The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.展开更多
Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help under...Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help understand the effects of the mineralogical composition on evaluating the rock strength,this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks.Using rock samples collected from the outcrops in the Sichuan Basin,uniaxial compression tests have been conducted to sandstone,carbonate,and shale cores.Combining with statistical analysis,the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies.According to the statistical analysis results,the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks.The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks.Especially,to improve the evaluation accuracy,the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays,and those of carbonates can be calculated by the mineral contents of dolomite and calcite.Noticeably,the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks.Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength.As for shale rocks,no relationship exists between the rock strength and the mineralogical composition(e.g.,the clay fractions).To provide more evidences,detailed discussion also provides the readers more glances into the framework of the rock matrix,which can be further studied in the future.These findings can help understand the effects of mineralogical composition on the rock strengths,explain the contrasts in the rock strength of the responses to the same mineral content(e.g.,the quartz content),and provide another indirect method for evaluating the rock strength of common sedimentary rocks.展开更多
In the present work,Fe–Mn–Al–C powder mixtures were manufactured by elemental powders with different ball milling time,and the porous high-Mn and high-Al steel was fabricated by powder sintering.The results indicat...In the present work,Fe–Mn–Al–C powder mixtures were manufactured by elemental powders with different ball milling time,and the porous high-Mn and high-Al steel was fabricated by powder sintering.The results indicated that the powder size significantly decreased,and the morphology of the Fe powder tended to be increasingly flat as the milling time increased.However,the prolonged milling duration had limited impact on the phase transition of the powder mixture.The main phases of all the samples sintered at 640℃ were α-Fe,α-Mn and Al,and a small amount of Fe2Al5 and Al8Mn5.When the sintering temperature increased to 1200℃,the phase composition was mainly comprised of γ-Fe and α-Fe.The weight loss fraction of the sintered sample decreased with milling time,i.e.,8.3wt% after 20 h milling compared to15.3wt% for 10 h.The Mn depletion region(MDR) for the 10,15,and 20 h milled samples was about 780,600,and 370 μm,respectively.The total porosity of samples sintered at 640℃ decreased from ~46.6vol% for the 10 h milled powder to ~44.2vol% for 20 h milled powder.After sintering at 1200℃,the total porosity of sintered samples prepared by 10 and 20 h milled powder was ~58.3vol% and ~51.3vol%,respectively.The compressive strength and ductility of the 1200℃ sintered porous steel increased as the milling time increased.展开更多
Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice v...Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.展开更多
The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in thi...The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in this study,i.e.back propagation neural network(BPNN),AdaBoost-based classification and regression tree(AdaBoost-CART),support vector machine(SVM),K-nearest neighbor(KNN),and radial basis function neural network(RBFNN).A total of 351 data points with seven input parameters(i.e.diameter and height of specimen,density,temperature,confining pressure,crack damage stress and elastic modulus)and one output parameter(triaxial compressive strength)were utilized.The root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R)were used to evaluate the prediction performance of the five ML models.The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE,MAE and R values on the testing dataset of 15.4 MPa,11.03 MPa and 0.9921,respectively.The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.展开更多
Split Hopkinson pressure bar(SHPB)was used to investigate the dynamic compressive properties of sisal fiber reinforced coral aggregate concrete(SFCAC).The results showed that,with the increase of strain rate,the dynam...Split Hopkinson pressure bar(SHPB)was used to investigate the dynamic compressive properties of sisal fiber reinforced coral aggregate concrete(SFCAC).The results showed that,with the increase of strain rate,the dynamic compressive strength,peak strain and toughness index of SFCAC are all greater than its static properties,indicating that SFCAC is a kind of rate-sensitive material.When the sisal fiber was blended,the failure mode showed obvious ductility.At high strain rates,the SFCAC without sisal fiber specimen was comminuted,and the SFCAC showed a"cracked without breaking"state.The results indicated that the sisal fiber played a significant role in reinforcing and strengthening the properties of concrete.The finite element software LS-DYNA was used to simulate two working conditions with strain rates of 78 and 101 s-1.The stressstrain curves and failure patterns obtained were in good agreement with the experimental results.展开更多
Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle size...Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle sizes.The accessible porosity,compressive strength,gas permeability and microstructure have been investigated accordingly.The results have shown that adding glass powder up to 20%has a significantly negative effect on the porosity and compressive strength of mortar.The compressive strength initially rises with a 5%replacement and then decreases.Similarly,the gas permeability of the mortar displays a non-monotonic behavior;first,it decreases and then it grows with an increase in the glass powder content and particle size.The porosity and gas permeability attain a minimum for a 5%content and 10μm particle size.Application of a Nuclear magnetic resonance(NMR)technique has revealed that incorporating waste glass powder with a certainfineness can reduce the pore size and the number of pores of the mortar.Compared with the control mortar,the pore volume of the waste glass mortar with 5%and 10μm particle size is significantly reduced.When cement is partially replaced by glass powder with a particle size of 10μm and a 5%percentage,the penetration resistance and compressive strength of the mortar are significantly improved.展开更多
Dealloying by which the transition metal is partially or completely leached from an alloy precursor is an effective way to optimize the fundamental effects for further enhancing the electrocatalysis of a catalyst.Here...Dealloying by which the transition metal is partially or completely leached from an alloy precursor is an effective way to optimize the fundamental effects for further enhancing the electrocatalysis of a catalyst.Herein,to address the deficiencies associated with the commonly used dealloying methods,for example,electrochemical and sulfuric acid/nitric acid treatment,we report an acetic acid-assisted mild strategy to dealloy Cu atoms from the outer surface layers of CuPd alloy nanoparticles to achieve high-efficiency electrocatalysis for oxygen reduction and ethanol oxidation in an alkaline electrolyte.The leaching of Cu atoms by acetic acid exerts an additional compressive strain effect on the surface layers and exposes more active Pd atoms,which is beneficial for boosting the catalytic performance of a dealloyed catalyst for the oxygen reduction reaction(ORR)and the ethanol oxidation reaction(EOR).In particular,for ORR,the CuPd nanoparticles with a Pd/Cu molar ratio of 2:1 after acetic dealloying show a half-wave potential of 0.912 V(vs.RHE)and a mass activity of 0.213 AmgPd^(-1) at 0.9 V,respectively,while for EOR,the same dealloyed sample has a mass activity and a specific activity of 8.4 Amg^(-1) and 8.23 mA cm^(-2),respectively,much better than their dealloyed counterparts at other temperatures and commercial Pd/C as well as a Pt/C catalyst.展开更多
This paper presents an experimental study to explore the compressive properties of fiber recycled aggregate concrete.A total of 75 specimens with the replacement rate of recycled coarse aggregate and fiber type were c...This paper presents an experimental study to explore the compressive properties of fiber recycled aggregate concrete.A total of 75 specimens with the replacement rate of recycled coarse aggregate and fiber type were conducted under a uniaxial compressive test.The failure modes,stress-strain whole curves,peak stress,peak strain,and energy dissipation capacity were systematically observed and revealed.Test results indicate that steel fiber has the best modification effect on energy dissipation capacity and the toughness index of recycled concrete,corresponding to the enhancement of 81.75% and 22.90% on average.The addition of polyvinyl alcohol fiber can effectively improve the compressive strength and energy dissipation capacity of recycled aggregate concrete by 28.49% and 29.43% on average,respectively.The compressive strength and energy dissipation capacity of recycled aggregate concrete is increased by an average of 16.5% and 24.4% by incorporating carbon fiber.The energy dissipation capacity of recycled aggregate concrete is increased by an average of 13.5% with the incorporation of polypropylene fiber.However,the addition of carbon fiber results in a slight reduction of toughness by 16.97%,and the effect of polyvinyl alcohol fiber on the energy dissipation capacity is limited.Besides,with the increase in replacement rate,the compressive strength and the energy dissipation capacity of recycled coarse aggregate concrete with fiber decreased,and toughness first decreased and then increased.Finally,based on the analysis of test data,a segment-based stress-strain model of fiber recycled aggregate concrete was proposed,which shows good agreement with the test results.展开更多
基金the financial support of the National Key Research and Development Program of China(2018YFC1106703)the Key Projects of the Joint Fund of the National Natural Science Foundation of China(U1804251)。
文摘The degradation of Mg alloys relates to the service performance of Mg alloy biodegradable implants.In order to investigate the degradation behavior of Mg alloys as vascular stent materials in the near service environment,the hot-extruded fine-grained Mg-Zn-Y-Nd alloy microtubes,which are employed to manufacture vascular stents,were tested under radial compressive stress in the dynamic Hanks'Balanced Salt Solution(HBSS).The results revealed that the high flow rate accelerates the degradation of Mg alloy microtubes and its degradation is sensitive to radial compressive stress.These results contribute to understanding the service performance of Mg alloys as vascular stent materials.
基金supported by"The National Key Research and Development Program of China(No.2018 YFA0703300)""Science and Technology Project of Education Department of Jilin Province(No.JJKH20231086KJ)"Development Project of Jilin Province(No.2021C038-4)。
文摘In this study,interconnected porous Mg-2Zn-xY alloys with different phase compositions were prepared by various Y additions(x=0.4,3,and 6 wt.%)to adjust the compressive properties and energy absorption characteristics.Several characterization methods were then applied to identify the microstructure of the porous Mg-Zn-Y and describe the details of the second phase.Compressive tests were performed at room temperature(RT),200℃,and 300℃to study the impact of the Y addition and testing temperature on the compressive properties of the porous Mg-Zn-Y.The experimental results showed that a high Y content promotes a microstructure refinement and increases the volume fraction of the second phase.When the Y content increases,different Mg-Zn-Y ternary phases appear:I-phase(Mg_(3)Zn_(6)Y),W-phase(Mg_(3)Zn_(3)Y_(2)),and LPSO phase(Mg_(12)ZnY).When the Y content ranges between 0.4%and 6%,the compressive strength increases from 6.30MPa to 9.23 MPa,and the energy absorption capacity increases from 7.33 MJ/m^(3)to 10.97 MJ/m^(3)at RT,which is mainly attributed to the phase composition and volume fraction of the second phase.However,the average energy absorption efficiency is independent of the Y content.In addition,the compressive deformation behaviors of the porous Mg-Zn-Y are altered by the testing temperature.The compressive strength and energy absorption capacity of the porous Mg-Zn-Y decrease due to the softening effect of the high temperature on the struts.The deformation behaviors at different temperatures are finally observed to reflect the failure mechanisms of the struts.
基金supported by grants funded by Department of Mechanical Engineering,Faculty of Engineering,Chiang Mai University and the Graduate School of Chiang Mai University.
文摘The objective of this research was to determine the mechanical parameter from EVA foam and also investigate its behavior by using Blatz-Ko,Neo-Hookean,Mooney model and experimental test.The physical characteristic of EVA foam was also evaluated by scanning electron microscopy(SEM).The results show that Blatz-Ko and Neo-Hookean model can fit the curve at 5%and 8%strain,respectively.The Mooney model can fit the curve at 50%strain.The modulus of rigidity evaluated from Mooney model is 0.0814±0.0027 MPa.The structure of EVA foam from SEM image shows that EVA structure is a closed cell with homogeneous porous structure.From the result,it is found that Mooney model can adjust the data better than other models.This model can be applied for mechanical response prediction of EVA foam and also for reference value in engineering application.
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2023R809).
文摘Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.
基金supported by the National Natural Science Foundation of China(No.51709097).
文摘River sand is an essential component used as a fine aggregate in mortar and concrete.Due to unrestrained exploitation,river sand resources are gradually being exhausted.This requires alternative solutions.This study deals with the properties of cement mortar containing different levels of manufactured sand(MS)based on quartzite,used to replace river sand.The river sand was replaced at 20%,40%,60%and 80%with MS(by weight or volume).The mechanical properties,transfer properties,and microstructure were examined and compared to a control group to study the impact of the replacement level.The results indicate that the compressive strength can be improved by increasing such a level.The strength was improved by 35.1%and 45.5%over that of the control mortar at replacement levels of 60%and 80%,respectively.Although there was a weak link between porosity and gas permeability in the mortars with manufactured sand,the gas permeability decreased with growing the replacement level.The microstructure of the MS mortar was denser,and the cement paste had fewer microcracks with increasing the replacement level.
基金supported via funding from Prince Sattam Bin Abdulaziz University Project Number(PSAU/2023/R/1445).
文摘This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.
基金supported by the National Natural Science Foundation of China(Grant No.52374153).
文摘Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.
文摘Chronic compressive spinal cord injury in compressive cervical myelopathy conditions can lead to rapid neurological deterioration in the early phase,followed by partial self-recovery,and ultimately an equilibrium state of neurological dysfunction.Ferroptosis is a crucial pathological process in many neurodegenerative diseases;however,its role in chro nic compressive spinal cord injury remains unclear.In this study,we established a chronic compressive spinal cord injury rat model,which displayed its most severe behavioral and electrophysiological dysfunction at 4 wee ks and partial recovery at 8 weeks after compression.Bulk RNA sequencing data identified enriched functional pathways,including ferroptosis,presynapse,and postsynaptic membrane activity at both 4 and 8 wee ks following chro nic compressive spinal co rd injury.Tra nsmission electron microscopy and malondialdehyde quantification assay confirmed that ferroptosis activity peaked at 4 weeks and was attenuated at 8 weeks after chronic compression.Ferro ptosis activity was negatively correlated with behavioral score.Immunofluorescence,quantitative polymerase chain reaction,and western blotting showed that expression of the anti-ferroptosis molecules,glutathione peroxidase 4(GPX4) and MAF BZIP transcription factor G(MafG),in neuro ns was suppressed at 4 weeks and upregulated at 8 weeks following spinal co rd compression.There was a positive correlation between the expression of these two molecules,suggesting that they may work together to contribute to functional recovery following chronic compressive spinal cord injury.In conclusion,our study determined the genome-wide expression profile and fe rroptosis activity of a consistently compressed spinal cord at different time points.The results showed that anti-fe rroptosis genes,specifically GPX4 and MafG,may be involved in spontaneous neurological recovery at 8 weeks of chronic compressive spinal cord injury.These findings contribute to a better understanding of the mechanisms underlying chronic compressive spinal cord injury and may help identify new therapeutic targets for compressive cervical myelopathy.
基金supported by the National Natural Science Foundation of China (Grant No. 61672124)the Password Theory Project of the 13th Five-Year Plan National Cryptography Development Fund (Grant No. MMJJ20170203)+3 种基金Liaoning Province Science and Technology Innovation Leading Talents Program Project (Grant No. XLYC1802013)Key R&D Projects of Liaoning Province (Grant No. 2019020105JH2/103)Jinan City ‘20 Universities’ Funding Projects Introducing Innovation Team Program (Grant No. 2019GXRC031)Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (Grant No. MIMS20-M-02)。
文摘A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing(CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform(DWT)on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics(PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.
基金funded by the Natural Sciences and Engineering Research Council of Canada(NSERC RGPIN-2017-05537).
文摘The unconfined compressive strength(UCS)of alkali-activated slag(AAS)-based cemented paste backfill(CPB)is influenced by multiple design parameters.However,the experimental methods are limited to understanding the relationships between a single design parameter and the UCS,independently of each other.Although machine learning(ML)methods have proven efficient in understanding relationships between multiple parameters and the UCS of ordinary Portland cement(OPC)-based CPB,there is a lack of ML research on AAS-based CPB.In this study,two ensemble ML methods,comprising gradient boosting regression(GBR)and random forest(RF),were built on a dataset collected from literature alongside two other single ML methods,support vector regression(SVR)and artificial neural network(ANN).The results revealed that the ensemble learning methods outperformed the single learning methods in predicting the UCS of AAS-based CPB.Relative importance analysis based on the bestperforming model(GBR)indicated that curing time and water-to-binder ratio were the most critical input parameters in the model.Finally,the GBR model with the highest accuracy was proposed for the UCS predictions of AAS-based CPB.
基金supported by National Key Research and Development Program of China(2019YFA0708302)National Natural Science Foundation of China(Grant No.52004296,and Grant No.52274016)+1 种基金the Foundation of State Key Laboratory of Petroleum Resources and Prospecting(PRP/DX-2206)Science Foundation of China University of Petroleum-Beijing(No.2462022YXZZ007,No.2462022BJRC012).
文摘Figuring out rock strength plays essential roles in the sub ground mining activities,such as oil and gas well drilling and hydraulic fracturing,coal mining,tunneling,and other civil engineering scenarios.To help understand the effects of the mineralogical composition on evaluating the rock strength,this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks.Using rock samples collected from the outcrops in the Sichuan Basin,uniaxial compression tests have been conducted to sandstone,carbonate,and shale cores.Combining with statistical analysis,the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies.According to the statistical analysis results,the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks.The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks.Especially,to improve the evaluation accuracy,the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays,and those of carbonates can be calculated by the mineral contents of dolomite and calcite.Noticeably,the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks.Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength.As for shale rocks,no relationship exists between the rock strength and the mineralogical composition(e.g.,the clay fractions).To provide more evidences,detailed discussion also provides the readers more glances into the framework of the rock matrix,which can be further studied in the future.These findings can help understand the effects of mineralogical composition on the rock strengths,explain the contrasts in the rock strength of the responses to the same mineral content(e.g.,the quartz content),and provide another indirect method for evaluating the rock strength of common sedimentary rocks.
基金financially supported by the National Key R&D Program of China(No.2021YFB3802300)the National Natural Science Foundation of China(No.51804239)Guangdong Major Project of Basic and Applied Basic Research,China(No.2021B0301030001)。
文摘In the present work,Fe–Mn–Al–C powder mixtures were manufactured by elemental powders with different ball milling time,and the porous high-Mn and high-Al steel was fabricated by powder sintering.The results indicated that the powder size significantly decreased,and the morphology of the Fe powder tended to be increasingly flat as the milling time increased.However,the prolonged milling duration had limited impact on the phase transition of the powder mixture.The main phases of all the samples sintered at 640℃ were α-Fe,α-Mn and Al,and a small amount of Fe2Al5 and Al8Mn5.When the sintering temperature increased to 1200℃,the phase composition was mainly comprised of γ-Fe and α-Fe.The weight loss fraction of the sintered sample decreased with milling time,i.e.,8.3wt% after 20 h milling compared to15.3wt% for 10 h.The Mn depletion region(MDR) for the 10,15,and 20 h milled samples was about 780,600,and 370 μm,respectively.The total porosity of samples sintered at 640℃ decreased from ~46.6vol% for the 10 h milled powder to ~44.2vol% for 20 h milled powder.After sintering at 1200℃,the total porosity of sintered samples prepared by 10 and 20 h milled powder was ~58.3vol% and ~51.3vol%,respectively.The compressive strength and ductility of the 1200℃ sintered porous steel increased as the milling time increased.
基金Supported by the National Natural Science Foundation of China(Nos.41630969,41941013,41806225)the Tianjin Municipal Natural Science Foundation(No.20JCQNJC01290)。
文摘Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s.The prediction of sea ice is highly important,but accurate simulation of sea ice variations remains highly challenging.For improving model performance,sensitivity experiments were conducted using the coupled ocean and sea ice model(NEMO-LIM),and the simulation results were compared against satellite observations.Moreover,the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed.The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant(C^(rhg)).By reducing the C^(rhg) constant,the sea ice compressive strength increases,leading to improved simulated sea ice states.The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength.Meanwhile,dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration,reducing the simulation bias in the central Arctic Ocean in summer.The root mean square error(RMSE)between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution.The ice thickness,especially of multiyear thick ice,was also reduced and matched with the satellite observation better in the freezing season.These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.
基金We acknowledge the funding support from the National Natural Science Foundation of China(Grant No.51778575)Postdoctoral Science Foundation of China(Grant No.2021M692481)Fundamental Research Funds for the Central Universities of China(Grant No.2042021kf0055).The authors would like to thank the anonymous reviewers and editors for their constructive suggestions which greatly improve the quality of this paper.The authors are also grateful for the permission from Elsevier.
文摘The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering.Five machine learning(ML)techniques were adopted in this study,i.e.back propagation neural network(BPNN),AdaBoost-based classification and regression tree(AdaBoost-CART),support vector machine(SVM),K-nearest neighbor(KNN),and radial basis function neural network(RBFNN).A total of 351 data points with seven input parameters(i.e.diameter and height of specimen,density,temperature,confining pressure,crack damage stress and elastic modulus)and one output parameter(triaxial compressive strength)were utilized.The root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R)were used to evaluate the prediction performance of the five ML models.The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE,MAE and R values on the testing dataset of 15.4 MPa,11.03 MPa and 0.9921,respectively.The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.
基金National Natural Science Foundation of China(Nos.51508272,11832013,51878350,52078250)。
文摘Split Hopkinson pressure bar(SHPB)was used to investigate the dynamic compressive properties of sisal fiber reinforced coral aggregate concrete(SFCAC).The results showed that,with the increase of strain rate,the dynamic compressive strength,peak strain and toughness index of SFCAC are all greater than its static properties,indicating that SFCAC is a kind of rate-sensitive material.When the sisal fiber was blended,the failure mode showed obvious ductility.At high strain rates,the SFCAC without sisal fiber specimen was comminuted,and the SFCAC showed a"cracked without breaking"state.The results indicated that the sisal fiber played a significant role in reinforcing and strengthening the properties of concrete.The finite element software LS-DYNA was used to simulate two working conditions with strain rates of 78 and 101 s-1.The stressstrain curves and failure patterns obtained were in good agreement with the experimental results.
基金This work is supported by the National Natural Science Foundation of China(No.51709097).
文摘Glass powder of various particle sizes(2,5,10 and 15μm)has been assessed as a possible cement substitute for mortars.Different replacement rates of cement(5%,10%,15%,and 20%)have been considered for all particle sizes.The accessible porosity,compressive strength,gas permeability and microstructure have been investigated accordingly.The results have shown that adding glass powder up to 20%has a significantly negative effect on the porosity and compressive strength of mortar.The compressive strength initially rises with a 5%replacement and then decreases.Similarly,the gas permeability of the mortar displays a non-monotonic behavior;first,it decreases and then it grows with an increase in the glass powder content and particle size.The porosity and gas permeability attain a minimum for a 5%content and 10μm particle size.Application of a Nuclear magnetic resonance(NMR)technique has revealed that incorporating waste glass powder with a certainfineness can reduce the pore size and the number of pores of the mortar.Compared with the control mortar,the pore volume of the waste glass mortar with 5%and 10μm particle size is significantly reduced.When cement is partially replaced by glass powder with a particle size of 10μm and a 5%percentage,the penetration resistance and compressive strength of the mortar are significantly improved.
基金the financial support provided by the National Natural Science Foundation of China(22075290,21972068,52164028)the Beijing Natural Science Foundation(Z200012)+3 种基金the State Key Laboratory of Multiphase Complex Systemsthe Institute of Process Engineeringthe Chinese Academy of Sciences(MPCS-2021-A-05)the Nanjing IPE Institute of Green Manufacturing Industry(E0010725).
文摘Dealloying by which the transition metal is partially or completely leached from an alloy precursor is an effective way to optimize the fundamental effects for further enhancing the electrocatalysis of a catalyst.Herein,to address the deficiencies associated with the commonly used dealloying methods,for example,electrochemical and sulfuric acid/nitric acid treatment,we report an acetic acid-assisted mild strategy to dealloy Cu atoms from the outer surface layers of CuPd alloy nanoparticles to achieve high-efficiency electrocatalysis for oxygen reduction and ethanol oxidation in an alkaline electrolyte.The leaching of Cu atoms by acetic acid exerts an additional compressive strain effect on the surface layers and exposes more active Pd atoms,which is beneficial for boosting the catalytic performance of a dealloyed catalyst for the oxygen reduction reaction(ORR)and the ethanol oxidation reaction(EOR).In particular,for ORR,the CuPd nanoparticles with a Pd/Cu molar ratio of 2:1 after acetic dealloying show a half-wave potential of 0.912 V(vs.RHE)and a mass activity of 0.213 AmgPd^(-1) at 0.9 V,respectively,while for EOR,the same dealloyed sample has a mass activity and a specific activity of 8.4 Amg^(-1) and 8.23 mA cm^(-2),respectively,much better than their dealloyed counterparts at other temperatures and commercial Pd/C as well as a Pt/C catalyst.
基金supported by the Postdoctoral Science Foundation of China(2021M693854)the Doctoral Foundation of Guangxi University of Science and Technology(No.18Z09)Bagui Scholar Program sponsored from the People’s Government of Guangxi Zhuang Autonomous Region(No.2019(79)).
文摘This paper presents an experimental study to explore the compressive properties of fiber recycled aggregate concrete.A total of 75 specimens with the replacement rate of recycled coarse aggregate and fiber type were conducted under a uniaxial compressive test.The failure modes,stress-strain whole curves,peak stress,peak strain,and energy dissipation capacity were systematically observed and revealed.Test results indicate that steel fiber has the best modification effect on energy dissipation capacity and the toughness index of recycled concrete,corresponding to the enhancement of 81.75% and 22.90% on average.The addition of polyvinyl alcohol fiber can effectively improve the compressive strength and energy dissipation capacity of recycled aggregate concrete by 28.49% and 29.43% on average,respectively.The compressive strength and energy dissipation capacity of recycled aggregate concrete is increased by an average of 16.5% and 24.4% by incorporating carbon fiber.The energy dissipation capacity of recycled aggregate concrete is increased by an average of 13.5% with the incorporation of polypropylene fiber.However,the addition of carbon fiber results in a slight reduction of toughness by 16.97%,and the effect of polyvinyl alcohol fiber on the energy dissipation capacity is limited.Besides,with the increase in replacement rate,the compressive strength and the energy dissipation capacity of recycled coarse aggregate concrete with fiber decreased,and toughness first decreased and then increased.Finally,based on the analysis of test data,a segment-based stress-strain model of fiber recycled aggregate concrete was proposed,which shows good agreement with the test results.
