Catalytic graphitization of PAN-based carbon fibers with electrodeposited Ni-Fe alloy

Ni-Fe alloy was electrodeposited on the surface of polyacrylonitrile （PAN）-based carbon fibers, and catalytic graphitization effect of the heat-treated carbon fibers was investigated by X-ray diffractometry and Raman spectra. It is found that Ni-Fe alloy exhibits significant catalytic effect on the graphitization of the carbon fibers at low temperatures. The degree of graphitization of the carbon fibers coated with Ni-Fe alloy （57.91% Fe, mass fraction） reaches 69.0% through heat treatment at 1 250 °C. However, the degree of graphitization of the carbon fibers without Ni-Fe alloy is only 30.1% after being heat-treated at 2 800 °C. The catalytic effect of Ni-Fe alloy on graphitization of carbon fibers is better than that of Ni or Fe at the same temperature, indicating that Ni and Fe elements have synergic catalytic function. Furthermore, Fe content in the Ni-Fe alloy also influences catalytic effect. The catalytic graphitization of Ni-Fe alloy follows the dissolution-precipitation mechanism.

Fabrication and characterization of electrodeposited nanocrystalline Ni-Fe alloys for NiFe_2O_4 spinel coatings

Nanocrystalline Ni-Fe FCC alloy coatings with Fe content of 1.3%-39%（mass fraction） were fabricated on the nickel substrates using a DC electrodeposition technique. The crystal structure, lattice strain, grain size and lattice constant of the Ni-Fe alloy coatings were studied by X-ray diffraction technique. The chemical composition and surface morphology of the FCC Ni-Fe alloy coatings were investigated with the energy dispersive X-ray spectroscopy（EDS） and atomic force microscopy（AFM）. The results show that the Fe content of the Ni-Fe alloy coatings has a great influence on the preferred orientation, grain size, lattice constant and lattice strain. FCC Ni-Fe alloy coatings exhibit preferred orientations of（200） or（200）（111）. With an increase of Fe content, the preferred growth orientation of（200） plane is weakened gradually, while the preferred growth orientation of（111） increases. An increase of the Fe content in the range of 1.3%-25%（mass fraction） results in a significant grain refinement of the coatings. Increasing the Fe content beyond 25% does not decrease the grain size of FCC Ni-Fe alloys further. The lattice strain increases with increasing the Fe content in the FCC Ni-Fe alloys. Since the alloys with Fe content not less than 25% has similar grain size（~11 nm）, the increase in the lattice strain with the increase of Fe content cannot be attributed to the change in the grain size.

EFFECT OF COMPOSITION ON HYDROGEN EMBRITTLEMENT IN Ni-Fe FCC ALLOYS

The ductility loss and threshold stress intensity,K_(IH)during hydrogen charging were measured for pure Ni and four Ni-Fe fcc alloys.The results show that ductility loss in 40Ni60Fe alloy and K_(IH)a 50Ni50Fe alloy have a minimum value.The variations of the amounts of hydride, hydrogen evolution and dislocation structure with composition have been investigated.The va- riation of hydrogen embrittlement susceptibility with composition measured by ductility loss and by K_(IH)or K_(IH)/K_C can be explained by means of the synthetical effects of amount of hydride,solutionized hydrogen and the extent of dislocation planarity on hydrogen embrittlement susceptibility.

基于应力分析Ni-Fe合金支撑固体氧化物燃料电池的结构稳定性研究

本文以NiO和Fe2O3为原料,应用流延、丝网印刷、高温共烧结和原位还原的工艺制备多孔金属支撑固体氧化物燃料电池(MS-SOFC)。系统研究了支撑体中Fe含量对MS-SOFC的残余应力、抗弯断裂强度和电化学稳定性的影响。结果表明,在NiO中加入10at%Fe2O3,使得支撑体致密化开始温度提高到937℃,残余应力和变形翘曲度分别低至70MPa和0.15mm;电池还原之后,Ni0.9Fe0.1支撑SOFC骨架表面孔隙率为40.22%,抗弯断裂强度达到最大值62.34MPa;电化学测试过程中,Ni0.9Fe0.1支撑SOFC在650℃下,以H2为燃料,在400mA·cm^–2电流密度下可以稳定运行60h,主要因为电池具有较高的抗弯断裂强度,能够抵抗运行过程中的热应力。该研究工作为MS-SOFC结构设计和性能稳定性优化提供重要的理论依据。

负载型非晶态合金催化剂Ni-Fe-Rh-P/CNFs的制备及其催化性能

研究了化学镀Ni-Fe-Rh-P非晶态合金镀层的工艺,考察了镀液成分和工艺参数对沉积速率和镀层质量的影响。利用优化的工艺配方在经过敏化、活化处理后的纳米碳纤维(CNFs)表面沉积了Ni-Fe-Rh-P合金镀层,分别利用能量色散X射线谱(EDS)、X射线衍射仪(XRD)及扫描电子显微镜(SEM)等手段对镀层的成分、结构、形貌进行了表征,采用液相硝基苯催化加氢反应表征了制备催化剂的催化活性。结果表明,利用化学镀技术可以在纳米碳纤维表面负载连续、均匀的Ni-Fe-Rh-P合金镀层,且镀层为非晶态结构;负载型非晶态合金催化剂Ni-Fe-Rh-P/CNFs具有很高的催化活性和良好的循环使用性能。

Boosting the water gas shift reaction on Pt/CeO_(2)-based nanocatalysts by compositional modification: Support doping versus bimetallic alloying

The water gas shift reaction is of vital significance for the generation and transition of energy due to the application in hydrogen production and industries such as ammonia synthesis and fuel cells.The influence of support doping and bimetallic alloying on the catalytic performance of Pt/Ce O_(2)-based nanocatalysts in water gas shift reaction was reported in this work.Various lanthanide ions and 3d transition metals were respectively introduced into the Ce O_(2)support or Pt to form Pt/Ce O_(2):Ln(Ln=La,Nd,Gd,Tb,Yb)and Pt M/Ce O_(2)(M=Fe,Co,Ni)nanocatalysts.The sample of Pt/Ce O_(2):Tb showed the highest activity(TOF at 200℃=0.051 s^(-1))among the Pt/Ce O_(2):Ln and the undoped Pt/Ce O_(2)catalysts.Besides,the sample of Pt Fe/Ce O_(2)exhibited the highest activity(TOF at 200℃=0.12 s^(-1))among Pt M/Ce O_(2)catalysts.The results of the multiple characterizations indicated that the catalytic activity of Pt/Ce O_(2):Ln catalysts was closely correlated with the amount of oxygen vacancies in doped ceria support.However,the different activity of Pt M/Ce O_(2)bimetallic catalysts was owing to the various Pt oxidation states of the bimetals dispersed on ceria.The study of the reaction pathway indicated that both the samples of Pt/Ce O_(2)and Pt/Ce O_(2):Tb catalyzed the reaction through the formate pathway,and the enhanced activity of the latter derived from the increased concentration of oxygen vacancies along with promoted water dissociation.As for the sample of Pt Fe/Ce O_(2),its catalytic mechanism was the carboxyl route with a higher reaction rate due to the moderate valence of Pt along with improved CO activation.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Study on hydrogen evolution performance of the carbon supported PtRu alloy film electrodes

The carbon supported PtRu alloy film electrodes having Pt about 0.10 mg/cm2 or even less were prepared by ion beam sputtering method （IBSM）. It was valued on the hydrogen analyse performance, the temperature influence factor and the stability by electroanalysis hydrogen analyse method. It was found that the carbon supported PtRu alloy film electrodes had higher hydrogen evolution performance and stability, such as the hydrogen evolution exchange current density （j0） was increase as the temperature （T） rised, and it overrun 150 mA/cm2 as the trough voltage in about 0.68V, and it only had about 2.8% decline in 500 h electrolytic process. The results demonstrated that the carbon supported PtRu alloy film electrodes kept highly catalytic activity and stability, and it were successfully used in pilot plant for producing H2 on electrolysis of H2S.

Tribological properties and wear prediction model of TiC particles reinforced Ni-base alloy composite coatings

TiC particles reinforced Ni-based alloy composite coatings were prepared on 7005 aluminum alloy by plasma spray. The effects of load, speed and temperature on the tribological behavior and mechanisms of the composite coatings under dry friction were researched. The wear prediction model of the composite coatings was established based on the least square support vector machine (LS-SVM). The results show that the composite coatings exhibit smaller friction coefficients and wear losses than the Ni-based alloy coatings under different friction conditions. The predicting time of the LS-SVM model is only 12.93%of that of the BP-ANN model, and the predicting accuracies on friction coefficients and wear losses of the former are increased by 58.74%and 41.87%compared with the latter. The LS-SVM model can effectively predict the tribological behavior of the TiCP/Ni-base alloy composite coatings under dry friction.

新型混编支架的制备与径向支撑力学行为

背景:编织支架因良好的柔顺性被广泛应用于狭窄动脉的治疗,但在提供足够的径向支撑性能方面存在明显局限。目的:针对单一材料制成的编织支架存在径向支撑力不足的问题,采用不同力学参数的生物可降解材料混合编织支架,探究影响混编支架径向力学性能的影响因素。方法:在镁合金支架中引入铁合金纱线,采用有限元分析法对不同编织参数的混编支架进行参数化分析,通过与0.18 mm直径镁合金纱线制备的普通支架进行对比,评估其径向支撑力、径向回弹率和扩张不均匀性。结果与结论:①当铁合金纱线直径为0.18 mm和0.14 mm时,随着铁合金纱线数量的增多,混编支架的径向支撑力增加;此时,尽管直径0.14 mm铁合金纱线提供的径向支撑力小于同数量的0.18 mm铁合金纱线,但增加细铁合金纱线的数量可以达到与较粗铁合金纱线混编支架相媲美的径向支撑力。当铁合金纱丝直径为0.10 mm时,随着铁合金纱线数量的增加,混编支架的径向支撑力降低。②增大编织角度,混编支架的径向支撑力降低,但其径向回弹率和狗骨率得到了改善。通过降低编织角可以改善小直径铁合金纱线造成的支架径向支撑力不足。③结果显示,在镁合金支架中引入铁合金纱线,可以实现在不增加纱线直径的情况下精确地调整支架的径向力学性能,这不仅克服了单一材料支架的局限性,也为多材料支架的设计提供了新思路。

Catalytic performance enhancement by alloying Pd with Pt on ordered mesoporous manganese oxide for methane combustion

Ordered mesoporous Mn2O3 (meso‐Mn2O3) and meso‐Mn2O3‐supported Pd, Pt, and Pd‐Pt alloy x(PdyPt)/meso‐Mn2O3; x = (0.10?1.50) wt%; Pd/Pt molar ratio (y) = 4.9?5.1 nanocatalysts were prepared using KIT‐6‐templated and poly(vinyl alcohol)‐protected reduction methods, respectively.The meso‐Mn2O3 had a high surface area, i.e., 106 m2/g, and a cubic crystal structure. Noble‐metalnanoparticles (NPs) of size 2.1?2.8 nm were uniformly dispersed on the meso‐Mn2O3 surfaces. AlloyingPd with Pt enhanced the catalytic activity in methane combustion; 1.41(Pd5.1Pt)/meso‐Mn2O3gave the best performance; T10%, T50%, and T90% (the temperatures required for achieving methaneconversions of 10%, 50%, and 90%) were 265, 345, and 425 °C, respectively, at a space velocity of20000 mL/(g?h). The effects of SO2, CO2, H2O, and NO on methane combustion over1.41(Pd5.1Pt)/meso‐Mn2O3 were also examined. We conclude that the good catalytic performance of1.41(Pd5.1Pt)/meso‐Mn2O3 is associated with its high‐quality porous structure, high adsorbed oxygen species concentration, good low‐temperature reducibility, and strong interactions between Pd‐Pt alloy NPs and the meso‐Mn2O3 support.

Estimation of wear performance of AZ91 alloy under dry sliding conditions using machine learning methods

The wear behavior of AZ91 alloy was investigated by considering different parameters,such as load(10−50 N),sliding speed(160−220 mm/s)and sliding distance(250−1000 m).It was found that wear volume loss increased as load increased for all sliding distances and some sliding speeds.For sliding speed of 220 mm/s and sliding distance of 1000 m,the wear volume losses under loads of 10,20,30,40 and 50 N were calculated to be 15.0,19.0,24.3,33.9 and 37.4 mm3,respectively.Worn surfaces show that abrasion and oxidation were present at a load of 10 N,which changes into delamination at a load of 50 N.ANOVA results show that the contributions of load,sliding distance and sliding speed were 12.99%,83.04%and 3.97%,respectively.The artificial neural networks(ANN),support vector regressor(SVR)and random forest(RF)methods were applied for the prediction of wear volume loss of AZ91 alloy.The correlation coefficient(R2)values of SVR,RF and ANN for the test were 0.9245,0.9800 and 0.9845,respectively.Thus,the ANN model has promising results for the prediction of wear performance of AZ91 alloy.

Artificial intelligence model of complicated flow behaviors for Ti-13Nb-13Zr alloy and relevant applications

The comprehensive nonlinear flow behaviors of a ductile alloy play a significant role in the numerical analysis of its forming process. The accurate characterization of as-forged Ti-13 Nb-13 Zr alloy was conducted by an improved intelligent algorithm, GA-SVR, the combination of genetic algorithm(GA) and support vector regression(SVR). The GA-SVR model learns from a training dataset and then is verified by a test dataset. As for the generalization ability of the solved GA-SVR model, no matter in β phase temperature range or(α+β) phase temperature range, the correlation coefficient R-values are always larger than 0.9999, and the AARE-values are always lower than 0.18%. The solved GA-SVR model accurately tracks the highly-nonlinear flow behaviors of Ti-13 Nb-13 Zr alloy. The stress-strain data expanded by this model are input into finite element solver, and the computation accuracy is improved.

Supported metal catalysts at the single-atom limit – A viewpoint

An account of recent work on supported single‐atom catalyst design is given here for reactions as diverse as the low‐temperature water‐gas shift,methanol steam reforming,selective ethanol dehydrogenation,and selective hydrogenation of alkynes and dienes.It is of fundamental interest to investigate the intrinsic activity and selectivity of the active metal atom site and compare them to the properties of the corresponding metal nanoparticles and sub‐nm clusters.It is also important to understand what constitutes a stable active metal atom site in the various reaction environments,and maximize their loadings to allow us to design robust catalysts for industrial applications.Combined activity and stability studies,ideally following the evolution of the active site as a function of catalyst treatment in real time are recommended.Advanced characterization methods with atomic resolution will play a key role here and will be used to guide the design of new catalysts.

High-precision Thickness Setting Models for Titanium Alloy Plate Cold Rolling without Tension

Due to its highly favorable physical and chemical properties,titanium and titanium alloy are widely used in a variety of industries.Because of the low output of a single batch,plate cold rolling without tension is the most common rolling production method for titanium alloy.This method is lack of on-line thickness closed-loop control,with carefully thickness setting models for precision.A set of high-precision thickness setting models are proposed to suit the production method.Because of frequent variations in rolling specification,a model structural for the combination of analytical models and statistical models is adopted to replace the traditional self-learning method.The deformation resistance and friction factor,the primary factors which affect model precision,are considered as the objectives of statistical modeling.Firstly,the coefficient fitting of deformation resistance analytical model based on over-determined equations set is adopted.Additionally,a support vector machine（SVM）is applied to the modeling of the deformation resistance and friction factor.The setting models are applied to a 1450 plate-coiling mill for titanium alloy plate rolling,and then thickness precision is found consistently to be within 3%,exceeding the precision of traditional setting models with a self-learning method based on a large number of stable rolling data.Excellent application performance is obtained.The proposed research provides a set of high-precision thickness setting models which are well adapted to the characteristics of titanium alloy plate cold rolling without tension.

A novel approach for evaluation of load bearing capacity of duplex coatings on aluminum alloy using PLS and SVR models

Duplex NiP/TiN coatings consisting of the electroless intermediate layers and the physical vapor deposition(PVD) top layers were fabricated on the AA6061 aluminum alloy in order to enhance the load bearing capacity. The main objective of this study was to model the load bearing based on the thickness, adhesion and elastic modulus of the coatings. For this purpose, partial least square(PLS) and support vector regression(SVR) approaches were employed.The results showed that both models had an acceptable performance;however, the PLS model outperformed SVR. The correlation coefficients between thickness, adhesion and elastic modulus with load bearing were 0.841, 0.8092 and 0.7657, respectively;so, thickness had the greatest effect on the load bearing capacity. The composition and structure of the samples were evaluated using XRD and SEM. The load capacity of the coated samples was also discussed based on the wear and adhesion evaluations. Dry sliding wear tests, under a load of 2 N and a sliding distance of 100 m,demonstrated the complete destruction of the coated specimens with low load capacity. The samples with high load capacity showed not only a superior tribological performance, but also a remarkable adhesion according to the Rockwell superficial hardness test.

Sustainable solid-state synthesis of uniformly distributed PdAg alloy nanoparticles for electrocatalytic hydrogen oxidation and evolution

New sustainable syntheses based on solid-state strategies have sparked enormous attention and provided novel routes for the synthesis of supported metallic alloy nanocatalysts(SMACs).Despite considerable recent progress in this field,most of the developed methods suffer from either complex operations or poorly controlled morphology,which seriously limits their practical applications.Here,we have developed a sustainable strategy for the synthesis of PdAg alloy nanoparticles(NPs)with an ultrafine size and good dispersion on various carbon matrices by directly grinding the precursors in an agate mortar at room temperature.Interestingly,no solvents or organic reagents are used in the synthesis procedure.This simple and green synthesis procedure provides alloy NPs with clean surfaces and thus an abundance of accessible active sites.Based on the combination of this property and the synergistic and alloy effects between Pd and Ag atoms,which endow the NPs with high intrinsic activity,the PdAg/C samples exhibit excellent activities as electrocatalysts for both the hydrogen oxidation and evolution reactions(HOR and HER)in a basic medium.Pd9Ag1/C showed the highest activity in the HOR with the largest j0,m value of 26.5 A g Pd^–1 and j0,s value of 0.033 mA cmPd^–2,as well as in the HER,with the lowest overpotential of 68 mV at 10 mA cm^–2.As this synthetic method can be easily adapted to other systems,the present scalable solid-state strategy may open opportunity for the general synthesis of a wide range of well-defined SMACs for diverse applications.

Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning

Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys,principal component analysis(PCA)combined with support vector machine(SVM)and Knearest neighbor(KNN)was used.The intensity and intensity ratio of fifteen lines of six elements(Fe,Si,Mg,Cu,Zn,and Mn)in the FIBS spectrum were selected.The distances between the focusing lens and the target surface in the pre-filament,filament,and post-filament were 958 mm,976 mm,and 1000 mm,respectively.The source data set was fifteen spectral line intensity ratios,and the cumulative interpretation rates of PC1,PC2,and PC3 were 97.22%,98.17%,and 95.31%,respectively.The first three PCs obtained by PCA were the input variables of SVM and KNN.The identification accuracy of the different positions of focusing lens and target surface was obtained,and the identification accuracy of SVM and KNN in the filament was 100%and 90%,respectively.The source data set of the filament was obtained by PCA for the first three PCs,which were randomly selected as the training set and test set of SVM and KNN in 3:2.The identification accuracy of SVM and KNN was 97.5%and 92.5%,respectively.The research results can provide a reference for the identification of aluminum alloys by FIBS.

SMFs-supported Pd nanocatalysts in selective acetylene hydrogenation:Pore structure-dependent deactivation mechanism

In the present study,CNFs,ZnO and Al2O3 were deposited on the SMFs panels to investigate the deactivation mechanism of Pd-based catalysts in selective acetylene hydrogenation reaction.The examined supports were characterized by SEM,NH3-TPD and N2adsorption-desorption isotherms to indicate their intrinsic characteristics.Furthermore,in order to understand the mechanism of deactivation,the resulted green oil was characterized using FTIR and SIM DIS.FTIR results confirmed the presence of more unsaturated constituents and then,more branched hydrocarbons formed upon the reaction over alumina-supported catalyst in comparison with the ones supported on CNFs and ZnO,which in turn,could block the pores mouths.Besides the limited hydrogen transfer,N2 adsorption-desorption isotherms results supported that the lowest pore diameters of Al2O3/SMFs close to the surface led to fast deactivation,compared with the other catalysts,especially at higher temperatures.

A Pd-Fe-B/γ-Al2O3 Amorphous Alloy Catalyst for Hydrogenation of Chloronitrobenzene to Chloroaniline

A Pd-Fe-B/γ-Al2O3 amorphous alloy catalyst was prepared by impregnation and chemical reduction with borohydrine aqueous solution. The catalyst was characterized by X-ray diffraction（XRD）, scanning electron microscope（SEM）, differential scanning calorimetry（DSC） and elecdes design suite（EDS） and was used for catalytic hydrogenation of 5-nitro-2-chloro-2＇, 4＇-dimethylbenzenesulfonanilide （NCD）. The amorphous alloy catalyst shows significantly high activity and selectively for hydrogenation of NCD to 5-Amino-2-chloro- 2＇, 4＇-dimethyibenzenesuifonanilide （ACD）.