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Bioinspired supramolecular macrocycle hybrid membranes with enhanced proton conductivity

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Nano Research 2024年第2期17卷 797-805页

作者： Pengfei Yang Linlin Xu Panagiotis Trogadas Marc-Olivier Coppens Yang lan Centre for Nature Inspired Engineering Department of Chemical EngineeringUniversity College LondonLondon WC1E 7JEUK

Enhancing the proton conductivity of proton exchange membranes(PEMs)is essential to expand the applications of proton exchange membrane fuel cells(PEMFCs).Inspired by the proton conduction mechanism of bacteriorhodopsin,cucurbit[n]urils(CB[n],where n is the number of glycoluril units,n=6,7,or 8)are introduced into sulfonated poly(ether ether ketone)(sPEEK)matrix to fabricate hybrid PEMs,employing a nature-inspired chemical engineering(NICE)*** carbonyl groups of CB[n]act as proton-conducting sites,while the host–guest interaction between CB[n]and water molecules offers extra protonconducting ***,the molecular size of CB[n]aids in their dispersion within the sPEEK matrix,effectively bridging the unconnected proton-conducting sulfonic group domains within the sPEEK ***,all hybrid membranes exhibit significantly enhanced proton ***,the sPEEK membrane incorporating 1 wt.%CB[8](CB[8]/sPEEK-1%)demonstrates the highest proton conductivity of 198.0 ms·cm^(−1) at 60°C and 100%relative humidity(RH),which is 228%greater than that of the pure sPEEK membrane under the same ***,hybrid membranes exhibit superior fuel cell *** CB[8]/sPEEK-1%membrane achieves a maximum power density of 214 mW·cm^(−2),representing a 140%improvement over the pure sPEEK membrane(89 mW·cm^(−2))at 50°C and 100%*** findings serve as a foundation for constructing continuous proton-conducting pathways within membranes by utilizing supramolecular macrocycles as fuel cell electrolytes and in other applications.

关键词： supramolecular macrocycle proton conductivity cucurbit[n]uril proton exchange membrane fuel cell

Engineering punching shear strength of flat slabs predicted by nature-inspired metaheuristic optimized regression system

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Frontiers of structural and Civil Engineering 2024年第4期18卷 551-567页

作者： Dinh-Nhat TRUONG Van-lan TO Gia Toai TRUONG Hyoun-seung JANG Department of Civil Engineering University of Architecture Ho Chi Minh CityHo Chi Minh City 700000Vietnam Faculty of Civil Engineering and Technology Dong A UniversityDa Nang City 550000Vietnam school of Architecture Seoul National University of Science and TechnologySeoul 01811Republic of Korea

Reinforced concrete(RC)flat slabs,a popular choice in construction due to their flexibility,are susceptible to sudden and brittle punching shear *** design methods often exhibit significant bias and *** estimation of punching shear strength in RC flat slabs is crucial for effective concrete structure design and *** study introduces a novel computation method,the jellyfish-least square support vector machine(Js-LssVR)hybrid model,to predict punching shear *** combining machine learning(LssVR)with jellyfish swarm(Js)intelligence,this hybrid model ensures precise and reliable *** model’s development utilizes a real-world experimental data *** with seven established optimizers,including artificial bee colony(ABC),differential evolution(DE),genetic algorithm(GA),and others,as well as existing machine learning(ML)-based models and design codes,validates the superiority of the Js-LssVR hybrid *** innovative approach significantly enhances prediction accuracy,providing valuable support for civil engineers in estimating RC flat slab punching shear strength.

关键词： punching shear strength reinforced concrete flat slabs machine learning jellyfish search support vector machine

Polymer transfer film formation from cryogenic to elevated temperatures

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Friction 2024年第9期12卷 2018-2032页

作者： Kian BAsHANDEH Vasilis TsIGKIs Ahmad AMIRI Pixiang lan Andreas A.POLYCARPOU J.Mike Walker’66 Department of Mechanical Engineering Texas A&M UniversityCollege StationTexas 77843USA ATsP Innovations Inc.HoustonTexas 77055USA Russell school of Chemical Engineering The University of TulsaTulsaOklahoma 74104USA Department of Mechanical Engineering The University of TulsaTulsaOklahoma 74104USA

This study reports on the tribological performance of aromatic thermosetting co-polyester(ATsP)and polyether ether ketone(PEEK)-based polymer composite coatings mixed with PTFE *** coatings were tested across a wide temperature range from−180 to 110℃ to simulate the environmental temperatures on Titan,Moon,and Mars,which are of particular interest for NAsA’s future exploratory *** experimental setup was developed to conduct the pin-on-disk experiments under dry sliding conditions and extreme temperature and contact *** film formation and its characteristics were found to play significant roles in the tribological performance,and the characteristics of the film were *** XPs and sEM analysis indicated the increase of the PTFE content in the transfer film as the temperature decreased to cryogenic *** coefficient of friction did not follow a linear trend with temperature and was minimum at 110℃ and maximum at−180℃.ATsP coating showed superior performance with lower friction and unmeasurable wear at all temperatures,whereas PEEK coating exhibited maximum wear at 25℃ followed by−180,and 110℃.

关键词： transfer film cryogenic high temperature tribology space tribology polymer coating

Mechanical Behavior and Failure Mechanism of an As-Extruded Mg-11wt%Y Alloy at Elevated Temperature

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Acta Metallurgica sinica(English Letters) 2024年第6期37卷 969-981页

作者： lan Zhang Dao-Kui Xu Bao-Jie Wang Cui-lan Lu shuo Wang Xiang-Bo Xu Dong-Liang Wang Xin Lv En-Hou Han school of Materials science and Engineering University of Science and Technology of ChinaShenyang110016China Key Laboratory of Nuclear Materials and safety Assessment Institute of Metal ResearchChinese Academy of SciencesShenyang110016China school of Environmental and Chemical Engineering Shenyang Ligong UniversityShenyang110159China Key Lab of Electromagnetic Processing of Materials Ministry of EducationNortheastern UniversityShenyang110819China Institute of Corrosion science and Technology Guangdong510070China

Through carrying out the high-temperature tensile experiments on an as-extruded Mg-11wt%Y alloy at 350℃,400℃,450℃,500℃and 550℃,the mechanical behavior and fracture mechanisms at elevated temperatures are investigated and *** results show that with the increase of temperature,the yield strength and ultimate tensile strength of the alloy increase at first and then decrease,while that the elongation ratio decreases firstly and then *** the sample being tested at 350℃,the values of yield strength,ultimate tensile strength and the elongation ratio are 188 MPa,266 MPa and 11%,*** 400℃,the yield strength and ultimate tensile strength reach the maximum values of,respectively,198 MPa and 277 MPa,but the elongation ratio is the lowest and its value is only 8%.When the applied temperature is increased to 550℃,the values of yield strength and ultimate tensile strength,respectively,decrease to 140 MPa and 192 MPa and the elongation ratio increases to 38%.Failure analysis demonstrates that the fracture surfaces of different samples are mainly composed of plastic dimples and exhibit the typical characteristic of ductile *** observation to the fracture side surfaces indicates that at the temperatures of 350℃and 400℃,microcracks mainly initiate in the interior of Mg_(24)Y_(5)*** the temperatures are 450℃,500℃and 550℃,the cracks preferentially initiate at the Mg_(24)Y_(5)/α-Mg interfaces.

关键词： Mg-Y alloy High-temperature tensile properties Fracture mechanism Failure analysis

Microwave Through Wall Imaging via An Induced Current Learning Method

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Journal of Beijing Institute of Technology 2024年第3期33卷 248-254页

作者： Pengjin lan Jianguo Huang Tianyi Zhou Faculty of Electrical Engineering and Computer science Ningbo UniversityNingbo 315211China

In this paper,an induced current learning method(ICLM)for microwave through wall imaging(TWI),named as TWI-ICLM,is *** the inversion of induced current,the unknown object along with the enclosed walls are treated as a combination of ***,a non-iterative method called distorted-Born backpropagation(DB-BP)is utilized to generate the initial *** the training stage,several convolutional neural networks(CNNs)are cascaded to improve the estimated induced *** addition,a hybrid loss function consisting of the induced current error and the permittivity error is used to optimize the network ***,the relative permittivity images are conducted analytically using the predicted current based on *** the numerical and experimental TWI tests prove that,the proposed method can achieve better imaging accuracy compared to traditional distorted-Born iterative method(DBIM).

关键词： through wall imaging inverse scattering problem convolutional neural network(CNN)

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selective photosynthesis of Z-olefins through crystalline metal-organic cage-initiated expeditious cascade reactions

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Carbon Energy 2024年第3期6卷 118-131页

作者： Jia-Ni Lu Yunze Huang Yuan-sheng Xia Long-Zhang Dong Lei Zhang Jing-Jing Liu lan-Gui Xie Jiang Liu Ya-Qian lan Jiangsu Key Laboratory of New Power Batteries School of Chemistry and Materials ScienceJiangsu Collaborative Innovation Centre of Biomedical Functional MaterialsNanjing Normal UniversityNanjingChina school of Chemistry South China Normal UniversityGuangzhouChina

The semi-hydrogenation of alkyne to form Z-olefins with high conversion and high selectivity is still a huge challenge in the chemical ***,flammable and explosive hydrogen as the common hydrogen source of this reaction increases the cost and danger of industrial ***,we connect the photocatalytic hydrogen evolution reaction and the semihydrogenation reaction of alkynes in series and successfully realize the high selective production of Z-alkenes using low-cost,safe,and green water as the proton *** the cascade reaction,a series of isomorphic metal–organic cage catalysts(Co_(x)Zn_(8−x)L_(6),x=0,3,4,5,and 8)are designed and synthesized to improve the yield of the photocatalytic hydrogen *** them,Co_(5)Zn_(3)L_(6) shows the highest photocatalytic activity,with a H_(2) generation rate of 8.81 mmol g^(−1) h^(−1).Then,Co_(5)Zn_(3)L_(6) is further applied in the above tandem reaction to efficiently reduce alkynes to Z-alkenes under ambient conditions,which can reach high conversion of>98%and high selectivity of>99%,and maintain original catalytic activity after multiple ***“one-pot”tandem reaction can achieve a highly selective and safe stepwise conversion from water into hydrogen into Z-olefins under mild reaction conditions.

关键词： alkyne semi-hydrogenation hydrogen evolution reaction metal-organic cages photocatalysis Z-olefins

A strong,Tough,and self-Healing strengthening Thioctic Acid-based Elastomer for Highly Reliable Flexible strain sensor

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Chinese Journal of Polymer science 2024年第10期42卷 1610-1618,I0016页

作者： Xin-Yu Chen Yu-Bing Fu Xue-Ling Yan lan Liu school of Materials science and Engineering Key Lab of Guangdong Province for High Property and Functional Polymer MaterialsSouth China University of TechnologyGuangzhou510641China

Elastomers with high strength and toughness,excellent self-healing properties,and biocompatibility have broad application prospects in wearable electronics and other fields,but preparing it remains a *** this work,we propose a highly adaptable strategy by introducing the small molecule crosslinking agent of triethanolamine(TEA)to the poly(thioctic acid)(PTA)chains and preparing the PAx Ey elastomers using a simple synthesis *** strategy stabilizes the PTA chains by constructing multiple non-covalent cross-linked dynamic networks,endowing materials with excellent strength and toughness(tensile strength of 288 kPa,toughness of 278.2 kJ/m3),admirable self-healing properties(self-healing efficiency of 121.6%within 7 h at 70℃),and good *** PAx Ey elastomers can also be combined with MWNTs to become flexible strain sensors,which can be used to monitor human joint movements with high sensitivity,repeatable responses,and stability.

关键词： Thioctic acid Ion clusters Multiple dynamic networks self-healing strengthening Flexible strain sensor

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A multiscale adaptive framework based on convolutional neural network: Application to fluid catalytic cracking product yield prediction

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Petroleum science 2024年第4期21卷 2849-2869页

作者： Nan Liu Chun-Meng Zhu Meng-Xuan Zhang Xing-Ying lan state Key Laboratory of Heavy Oil Processing China University of Petroleum (Beijing) College of Artificial Intelligence China University of Petroleum (Beijing)

since chemical processes are highly non-linear and multiscale, it is vital to deeply mine the multiscale coupling relationships embedded in the massive process data for the prediction and anomaly tracing of crucial process parameters and production indicators. While the integrated method of adaptive signal decomposition combined with time series models could effectively predict process variables, it does have limitations in capturing the high-frequency detail of the operation state when applied to complex chemical processes. In light of this, a novel Multiscale Multi-radius Multi-step Convolutional Neural Network(Msrt Net) is proposed for mining spatiotemporal multiscale information. First, the industrial data from the Fluid Catalytic Cracking(FCC) process decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) extract the multi-energy scale information of the feature subset. Then, convolution kernels with varying stride and padding structures are established to decouple the long-period operation process information encapsulated within the multi-energy scale data. Finally, a reconciliation network is trained to reconstruct the multiscale prediction results and obtain the final output. Msrt Net is initially assessed for its capability to untangle the spatiotemporal multiscale relationships among variables in the Tennessee Eastman Process(TEP). subsequently, the performance of Msrt Net is evaluated in predicting product yield for a 2.80 × 10～6 t/a FCC unit, taking diesel and gasoline yield as examples. In conclusion, Msrt Net can decouple and effectively extract spatiotemporal multiscale information from chemical process data and achieve a approximately reduction of 30% in prediction error compared to other time-series models. Furthermore, its robustness and transferability underscore its promising potential for broader applications.

关键词： Fluid catalytic cracking Product yield Data-driven modeling Multiscale prediction Data decomposition Convolution neural network

High-entropy design boosts dielectric energy storage

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Rare Metals 2024年第8期43卷 4013-4015页

作者： Qiu-Ying Xia He Zhu si lan Yang Ren Herbert Gleiter Institute of Nanoscience School of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Physics City University of Hong KongHong Kong999077China

Dielectric capacitors are vital for advanced electronic and electrical power systems due to their impressive power density and ***,a persistent challenge has been enhancing their energy densities while maintaining high *** in science,a novel high-entropy design for relaxor ferroelectric materials has been proposed,promising significant improvements in both energy density and efficiency for multilayer dielectric ceramic *** the crucial role of high-entropy design in energy storage materials and devices,this highlight focuses on interpreting the progress and significance of this innovative work.

关键词： ceramic dielectric energy

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