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Evaluating the impact of doping and interface engineering on CO₂-to-CO conversion selectivity in Cu-based electrochemical catalysts

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Surfaces and Interfaces 2025年 67卷

作者： Zhou, Jintao Liu, Yan Yang, Chuncheng Wang, Guoyong Key Laboratory of Automobile Materials Department of Materials Science and Engineering Jilin University Changchun 130025 China Key Laboratory of Bionic Engineering (Ministry of Education) and State Key Laboratory of Automotive Simulation and Control Jilin University Changchun 130022 China

Doping and interface engineering are both pivotal strategies for enhancing the selectivity of CO2 reduction catalysts. However, as these strategies have often been explored independently, the question of which is more... 详细信息

Doping and interface engineering are both pivotal strategies for enhancing the selectivity of CO₂ reduction catalysts. However, as these strategies have often been explored independently, the question of which is more effective or whether their effects can be synergistically combined remains open. Given that doping and interfaces serve as crystallographic defects, they can be engineered to coexist within the same crystal structure. In this study, we strategically introduced both defects into a copper catalyst by modulating the indium content during crystal growth. Our investigation into the electrochemical performance of these catalysts for CO conversion reveals that while doping consistently enhances conversion selectivity, the presence of Cu/In interfaces disrupts this trend. The most promising catalyst, with 3% indium doping, demonstrated an impressive CO selectivity of 95.38±1.5% and a partial current density of -2.25 mA cm^-² at -0.8 V vs. Reversible Hydrogen Electrode (RHE). This research provides critical insights into the complex interplay between doping and interface engineering, offering a new pathway for the rational design of highly selective CO₂ reduction catalysts. © 2025

关键词： CO2 electroreduction CuO(In) nanosheet catalyst Defect engineering Doping content

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Machine learning for membrane bioreactor research:principles,methods,applications,and a tutorial

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Frontiers of Environmental Science & Engineering 2025年第3期19卷 119-144页

作者： Yizhe Lai Kang Xiao Yifan He Xian Liu Jihua Tan Wenchao Xue Aiqian Zhang Xia Huang Beijing Yanshan Earth Critical Zone National Research Station College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China Key Laboratory of Earth System Numerical Modeling and Application University of Chinese Academy of SciencesBeijing 101408China Environmental Chemistry Faculty College of Resources and EnvironmentUniversity of Chinese Academy of SciencesBeijing 101408China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Department of Energy Environmentand Climate ChangeSchool of EnvironmentResourcesand DevelopmentAsian Institute of TechnologyP.O.Box 4Klong LuangPathumthani 12120Thailand State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Membrane fouling poses a significant challenge to the sustainable development of membrane bioreactor(MBR)technologies for wastewater *** accurate prediction of the membrane filtration process is of great importance for identifying and controlling *** learning methods address the limitations of traditional statistical approaches,such as low accuracy,poor generalization ability,and slow convergence,particularly in predicting complex filtration and fouling processes within the realm of big *** article provides an in-depth exposition of machine learning *** study then reviews advances in MBRs that utilize machine learning methods,including artificial neural networks(ANN),support vector machines(SVM),decision trees,and ensemble *** on current literature,this study summarizes and compares the model input and output characteristics(including foulant characteristics,solution environments,filtration conditions,operating conditions,and time factors),as well as the selection of models and optimization *** modeling procedures of SVM,random forest(RF),back propagation neural network(BPNN),long short-term memory(LSTM),and genetic algorithm-back propagation(GA-BP)methods are elucidated through a tutorial *** simulation results demonstrated that all five methods yielded accurate predictions with R2>***,the existing challenges in the implementation of machine learning models in MBRs were *** is notable that integration of deep learning,automated machine learning(AutoML)and explainable artificial intelligence(XAI)may facilitate the deployment of models in practical engineering *** insights presented here are expected to facilitate the establishment of an intelligent control framework for MBR processes in future endeavors.

关键词： Membrane bioreactor Machine learning Pollutant removal Membrane fouling Model prediction

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Root exudate-driven rhizospheric recruitment of plant growth-promoting rhizobacteria

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Pedosphere 2025年第1期35卷 216-228页

作者： Hua LIN Caixing LAI Guo YU Geoffrey ISUNAHARA Liheng LIU Habib ULLAH Jie LIU College of Environmental Science and Engineering Guilin University of TechnologyGuilin 541004China Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution ControlSchool of EnvironmentTsinghua UniversityBeijing 100084China Department of Natural Resource Sciences McGill UniversityMontreal H3A 0G4Canada Department of Environmental Science Zhejiang UniversityHangzhou 310058China

To promote growth,terrestrial plants release photosynthetic products into the rhizosphere in the form of root exudates through their root *** the attraction of root exudates,plant growth-promoting rhizobacteria(PGPR)colonize the rhizosphere to stimulate plant growth and/or suppress the growth of pathogenic *** rhizospheric recruitment of PGPR,as well as the establishment of root-microbe interactions,is initiated by chemotaxis towards root *** the past several decades,scientists have made notable strides in identifying representative chemoeffectors in root exudates,as well as the corresponding *** review here the most recent developments in rhizospheric chemotaxis,particularly the chemoeffectors in root exudates,the structural and functional characterization of methyl-accepting chemotaxis proteins of PGPR,and the impacts of rhizospheric chemotaxis on root colonization,biofilm formation,and other root-microbe interactions during ***,we propose several perspectives that aim to fully unlock the potential of PGPR as bioinoculants in sustainable agriculture.

关键词： biofilm formation chemoattractant chemoeffector methyl-accepting chemotaxis proteins rhizospheric colonization

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Effects of ammonia addition and variable valve timing on knocking and performance of ethanol pre-chamber engine with high compression ratio

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Energy 2025年 327卷

作者： Meng, Xianglong Xie, Fangxi Liu, Yu Yu, Zhenbo Jiang, Yunfeng Wang, Zhaoyu Wang, Xiangyang Jin, Zhaohui State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China China First Automobile Group Co. Ltd Changchun China FAW Jiefang Automobile Co. Ltd Changchun China

To attain the objective of carbon neutrality, improving the compression ratio of engines and utilizing renewable alcohol fuels is imperative. However, challenges such as knock limit the use of high compression ratio engines under high-load stoichiometric combustion conditions. Turbulent injection ignition (TJI) is an efficient and clean combustion technology that can effectively suppress knock. In addition, ammonia addition and variable valve technology were also considered effective means to affect knock. It can further break through the limitation of knocking on the thermal efficiency of ethanol engines under high loads. Therefore, this article developed seven valve strategies to investigate how valve strategies and the ammonia energy ratio (AER) influence the performance and knocking of pre-chamber engine fueled with ethanol. It has been observed that adjusted valve timing and added ammonia effectively reduced MAPO. When the AER exceeded 20 %, it didn't knock under any conditions. With the increase of AER, the flame development was prolonged. The effect of valve timing on CA50 was not significant. By adjusting the valve timing and AER, the optimal ITE can be increased by 4.09 %. In addition, adding ammonia can effectively reduce CO emissions by up to 14.04 %. Adjusting valve timing can reduce NOx emissions by 17.07 %. © 2025

关键词： Compression ratio (machinery)

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Optimization of Regional Energy-Water Nexus System Under Multiple Uncertainties 15th

Optimization of Regional Energy-Water Nexus System Under Mul...

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15th International Conference on Environmental Science and Development, ICESD 2024

作者： Liu, Ruoyan Li, Yongping Huang, Guohe Zhou, Yanxiao Li, Yanfeng State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing China Institute for Energy Environment and Sustainable Communities University of Regina ReginaSask Canada

ISBN: (纸本)9783031886829

In this paper, an interval chance-constrained programming (ICCP) method is proposed for collaborative energy-water nexus (EWN) system under multiple uncertainties. This method can solve the uncertainties expressed as probability distribution and interval. An ICCP-based energy-water nexus (ICCP-EWN) model is established, where 15 scenarios are designed for assessing the effect of water cycle coefficients and constraint violation risk. The results show that: (i) coal would remain the main energy sources for future energy supply, but the proportion of clean energy (e.g., natural gas, biomass) in the future power generation structure would significantly increase (about 15.6%);(ii) the water withdrawal of power generation in the energy system would be largest, where thermal power generation would have the largest proportion of water withdrawal, accounting for 85.0%;(iii) the decreasing α level and increasing p level will bring higher system benefits, and the reliability of the system would be reduced. These results can help determine the energy mix of a planned area, improve security of energy supply, and make trade-off between system benefits and the risk of violating constraints. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Normal distribution

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Hydrochemistry characteristics and genesis of shallow groundwater in diverse industrial agglomeration areas in typical alluvial plain of the Yellow River

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Science of the Total Environment 2025年 958卷 177764页

作者： Zhang, Fan Yuan, Ying Xi, Beidou Lu, Haojie Jiang, Yu Hui, Kunlong Tan, Wenbing Wang, Hui Meng, Fanhua State Key Laboratory of Environmental Criteria and Risk Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution Chinese Research Academy of Environmental Sciences Beijing100012 China

The groundwater environment in the middle and upper reaches of the Yellow River in China has attracted extensive attention. But, the hydrochemistry characteristics and genesis of shallow groundwater in diverse industrial agglomeration areas in typical alluvial plain of the Yellow River are still unknown. In this study, geochemical analysis methods, the positive matrix factorisation model, and the geodetector model were used to multidimensional analyze. The results showed that 78 % of the groundwater samples had high-alkalinity-risk and high-salinity-risk, and the water quality was poor and unsuitable for irrigation. "Very poor" and "undrinkable" samples accounted for 26.31 %. The weathering of calcium magnesium minerals and the dissolution of soluble sulfate were important sources of the main chemical components of the groundwater. However, evaporation and concentration gradually dominated from the inclined plain of the piedmont to the alluvial plain of the Yellow River. Domestic and agricultural activities were important sources of nitrogen species (NO3−, NO2−, and NH4+). The presence of Al, Mn, Zn, and Pb was caused by the production activities of various enterprises. The pollution contributors of natural sources, domestic and agricultural activities, industrial production and other sources were 29.49 %, 17.05 %, 31.41 %, and 22.05 %, respectively. Groundwater velocity and enterprise type were the main factors driving groundwater pollution by these four metal substances. The interaction between enterprise type and groundwater velocity had the greatest influence on the concentrations of Al, Mn, and Zn. Moreover, the interaction between enterprise type and the distance from enterprise had the greatest influence on Pb levels. Overall, groundwater pollution in the study area was the result of both natural and human factors. The findings of this study can offer valuable insights and theoretical support for preventing groundwater pollution in other industrial agglomerat

关键词： Groundwater geochemistry

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Multi-objective crashworthiness optimization of novel functionally-graded variable section double-hexagonal crash box

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第11期44卷 50-60and69页

作者： Xiong, Feng Zeng, Shupeng Wang, Zhanfei Zhang, Shuai Wang, Dengfeng Xu, Hongyu Joint International Research Laboratory of Intelligent Manufacturing & Control of Key Parts for Energy-efficient & New Energy Vehicles Ministry of Education Chongqing University of Technology Chongqing400054 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China School of Vehicle and Traffic Engineering Henan University of Science and Technology Luoyang471000 China State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130012 China

In order to synergistically improve the crashworthiness safety and lightweight level of the oar body, a novel functionally-graded variable section double-hexagonal crash box was designed and proposed. The substructure at its lower end with regulär shape, namely functionally-graded variable section double-hexagonal tube (FS-DHT), was extracted as research subject, on which parametric geometric modeling was conducted. The finite element model of FS-DHT under axial crushing was established and verified by experiments. A comparative study concerning crashworthiness and energy absorption characteristics under axial crushing was conducted among FS-DHT, traditional straight DHT (S-DHT) and conical DHT (C-DHT). The influence of the tube wall thickness, the circumcircle radius differences at upper and lower end face of inner and outer hexagonal tubes, and the gradient exponent on the crashworthiness and energy absorption characteristics of FS-DHT under axial crushing was explored. A multi-objective optimization was carried out on FS-DHT by combining the optimal Latin hypercube sampling, response surface Surrogate model and the second-generation non-dominating sorting genetic algorithm, then verified. The finite element model of " bumper-crash box" assembly under axial crushingwas further established to verify the improvement effect of FS-DHT optimization on the crashworthiness of functionally-graded variable section double-hexagonal crash box. The results show that after optimization of FS-DHT, its specific energy absorption (SEA) increases by 21. 61% while its initial peak crushing force (IPCF) decreases by 13.40%, which meanwhile promotes the energy absorption (EA) of functionally-graded variable section double-hexagonal crash box to increase by 17. 14% and the corresponding IPCF at the bumper to decrease by 6. 46%, indicating a good multi-objective collaborative crashworthiness optimization is obtained. An effective novel structure and new approach are provided for synerg

关键词： Multiobjective optimization

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Embedded plastics and carbon emissions:future trends and challenges ahead

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Science Bulletin 2025年第7期70卷 997-1000页

作者： Shang Zhang Jinhui Li Guochang Xu Quanyin Tan State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China Material Cycles Division National Institute for Environmental StudiesTsukuba305-8506Japan

In the past five decades,global plastic production has increased at an average yearly growth rate of 8.4%,which is significantly higher than that of other basic materials such as cement,steel,and aluminum[1,2].Moreover,the amount of global plastic waste more than doubled between 2000 and 2019,reaching an annual production of 353 million tons(Mt)[3].It was further estimated that plastic accounted for approximately 12%of municipal waste in 2016 globally[4].

关键词： global production waste municipal waste plastics carbon emissions growth rate

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Cost-Effective Mapping of Hyperlocal Air Pollution Using Large-Scale Mobile Monitoring and Land-Use Machine Learning

ACS ES&T Air

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ACS ES&T Air 2025年第2期2卷 151-161页

作者： Tie Zheng Yifan Wen Sheng Xiang Pan Yang Xuan Zheng Yan You Shaojun Zhang Ye Wu School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China State Key Laboratory of Pollution Control and Resource Reuse Tongji University Shanghai 200092 China；College of Environmental Science and Engineering Tongji University Shanghai 200092 China College of Chemistry and Environmental Engineering Shenzhen University Shenzhen 518060 China Macao Environmental Research Institute Macau University of Science and Technology Macau 999078 China；National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Macau University of Science and Technology Macao 999078 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China；Beijing Laboratory of Environmental Frontier Technologies Beijing 100084 China

The advent of large-scale mobile monitoring using fast-response instruments has enabled hyperlocal mapping (≤100 m) of traffic-related air pollution (TRAP), with important implications for air quality management. However, most related studies have been confined within small areas due to the high cost and labor intensity. This study pioneers a cost-effective TRAP mapping method by incorporating large-scale mobile monitoring and land-use machine learning (LUML). Here, over 4.6 million 1 Hz high-frequency measurements (∼1300 h) were collected on a part of major roadways in the Chinese megacity of Shenzhen. Unmeasured locations were estimated by LUML models to reduce measurement costs and labor intensity. Various ML algorithms and varying spatial aggregation segment lengths were incorporated to optimize the model performance. Hyperlocal maps of NO, NO2, and PM2.5were predicted across the entire road network covering over 1700 km2. Based on our results, LU-RF (random forest) for mapping NO and NO2and LU-GBM (Gradient Boosting Machine) for mapping PM2.5, demonstrated superior performance. Deep learning models, in contrast, did not yield comparable results. Finer partitioning of road segments (≤100 m) improved NO prediction performance, but worsened that for NO2and PM2.5. By deployment of optimal ML algorithms and segment lengths, the TRAP mapping accuracy increased by 20–80% compared to conventional land-use regression models. This study provides a promising and cost-effective approach to hyperlocal air pollution mapping and management in cities worldwide.

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Publisher Erratum: Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information(control Theory and Technology, (2025), 23, (74–90), 10.1007/s11768-024-00233-7)

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control Theory and Technology 2025年

作者： Liu, Qifang Sun, Dazhen Chen, Haowen Li, Dongzi Wang, Ping Department of Control Science and Engineering Jilin University Jilin Changchun130022 China State Key Laboratory of Automotive Simulation and Control Jilin University Jilin Changchun130022 China

In this article, the received, revised and accepted dates are missing. Please find the correct history dates here: Received: 15 October 2023. Revised: 20 March 2024. Accepted: 6 May 2024. © The Author(s), under exclusive licence to South China University of Technology and Academy of Mathematics and Systems Science, Chinese Academy of Sciences 2025.

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