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Synergistically S/N self-doped biochar as a green bifunctional cathode catalyst in electrochemical degradation of organic pollutant

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Green Energy & Environment 2025年第1期10卷 214-230页

作者： Xuechun Wang Huizhong Wu Jiana Jing Ge song Xuyang Zhang Minghua Zhou Raf Dewil Key Laboratory of Pollution Process and Environmental Criteria Ministry of EducationCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media Pollution Nankai UniversityTianjin300350China Tianjin Advanced Water Treatment Technology International Joint Research Center College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Chemical Engineering KU LeuvenJ.De Nayerlaan 5Sint-Katelijne-Waver B2860Belgium

Biomass-derived heteroatom self-doped cathode catalysts has attracted considerable interest for electrochemical advanced oxidation processes(EAOPs)due to its high performance and sustainable ***,we illustrated the morphological fates of waste leaf-derived graphitic carbon(WLGC)produced from waste ginkgo leaves via pyrolysis temperature regulation and used as bifunctional cathode catalyst for simultaneous H_(2)O_(2) electrochemical generation and organic pollutant degradation,discovering S/N-self-doping shown to facilitate a synergistic effect on reactive oxygen species(ROS)*** the optimum temperature of 800℃,the WLGC exhibited a H_(2)O_(2) selectivity of 94.2%and tetracycline removal of 99.3%within 60 *** functional theory calculations and in-situ Fourier transformed infrared spectroscopy verified that graphitic N was the critical site for H_(2)O_(2) *** pyridinic N and thiophene S were the main active sites responsible for OH generation,N vacancies were the active sites to produce ^(1)O_(2) from O_(2).The performance of the novel cathode for tetracycline degradation remains well under a wide pH range(3–11),maintaining excellent stability in 10 *** is also industrially applicable,achieving satisfactory performance treating in real water *** system facilitates both radical and non-radical degradation,offering valuable advances in the preparation of cost-effective and sustainable electrocatalysts and hold strong potentials in metal-free EAOPs for organic pollutant degradation.

关键词： Biochar S and N self-doping H_(2)O_(2)production In-situ FTIR Metal-free electrochemical advanced oxidation processes

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Alpha-decay properties of nuclei around neutron magic numbers

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Nuclear Science and Techniques 2025年第1期36卷 140-149页

作者： Ming Li Chu-Xin Chen Lan-Fang Xiao Yi Zhang song Luo Xiao-Hua Li School of Nuclear Science and Technology University of South ChinaHengyang 421001China PET‑CT Center Chenzhou First People’s HospitalChenzhou 423000China Hengyang Ecological Environment Monitor Center Hengyang 421001China China Certification and Inspection Group Hefei 230000China National Exemplary Base for International Science and Technology Collaboration of Nuclear Energy and Nuclear Safety University of South ChinaHengyang 421001China Cooperative Innovation Center for Nuclear Fuel Cycle Technology and Equipment University of South ChinaHengyang 421001China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control Hunan Normal UniversityChangsha 410081China

By combining experimental α-decay energies and half-lives, the α-particle preformation factor for nuclei around neutron magic numbers N of 126, 152, and 162 were extracted using the two-potential approach. The nuclei around the shell closure were more tightly bound than adjacent nuclei. Additionally, based on the WS4 mass model (Wang et al., Phys. Lett.B 734, 215 (2014)), we extended the two-potential approach to predict the α-decay half-lives of nuclei around N values of178 and 184 with Z of 119 and 120. We believe that our findings will serve as guidelines for future experimental studies.

关键词： adecay Preformation factor Neutron magic number Heavy and superheavy nuclei

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Orbital XY models in moiré superlattices

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Chinese Physics B 2025年第2期34卷 19-29页

作者： Yanqi Li Yi-Jie Wang Zhi-Da song International Center for Quantum Materials School of PhysicsPeking UniversityBeijing 100871China Hefei National Laboratory Hefei 230088China Collaborative Innovation Center of Quantum Matter Beijing 100871China

Moiré superlattices provide a new platform to engineer various many-body problems. In this work, we consider arrays of quantum dots(QD) realized on semiconductor moiré superlattices with a deep moiré potential. We diagonalize single QD with multiple electrons, and find degenerate ground states serving as local degrees of freedom(qudits) in the superlattice. With a deep moiré potential, the hopping and exchange interaction between nearby QDs become irrelevant,and the direct Coulomb interaction of the density–density type dominates. Therefore, nearby QDs must arrange the spatial densities to optimize the Coulomb energy. When the local Hilbert space has a two-fold orbital degeneracy, we find that a square superlattice realizes an anisotropic XY model, while a triangular superlattice realizes a generalized XY model with geometric frustration.

关键词： moirésuperlattices quantum dots XY model

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Electrochemical conversion of methane to bridge the gap in the artificial carbon cycle

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Journal of Energy Chemistry 2025年第1期100卷 286-308页

作者： Yuhao Peng Yuefeng song Ihar Razanau Juanxiu Xiao Wei Xiao Di Hu Guoxiong Wang College of Chemistry and Molecular Sciences Hubei Key Laboratory of Electrochemical Power SourcesWuhan UniversityWuhan 430072HubeiChina State Key Laboratory of Catalysis Dalian National Laboratory for Clean EnergyiChEM(Collaborative Innovation Center of Chemistry for Energy Materials)Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina Laboratory of Physical-Chemical Technologies SSPA Scientific and Practical Materials Research Centre of NAS of BelarusMinsk 220072Belarus State Key Laboratory of Marine Resources Utilization in South China Sea Collaborative Innovation Center of Marine Science and TechnologySchool of Marine Science and EngineeringHainan UniversityHaikou 570228HainanChina Department of Chemical and Environmental Engineering University of Nottingham Ningbo ChinaNingbo 315100ZhejiangChina

Methane, an abundant one-carbon(C_(1)) resource, is extensively used in the industrial production of vital fuels and value-added chemicals. However, current industrial methane conversion technologies are energy-and carbon-intensive, mainly due to the high activation energy required to break the inert C–H bond, low selectivity, and problematic side reactions, including CO_(2)emissions and coke deposition. Electrochemical conversion of methane(ECM) using intermittent renewable energy offers an attractive solution, due to its modular reactor design and operational flexibility across a broad spectrum of temperatures and pressures. This review emphasizes conversion pathways of methane in various reaction systems, highlighting the significance and advantages of ECM in facilitating a sustainable artificial carbon cycle. This work provides a comprehensive overview of conventional methane activation mechanisms and delineates the complete pathways of methane conversion in electrolysis contexts. Based on surface/interface chemistry, this work systematically analyzes proposed reaction pathways and corresponding strategies to enhance ECM efficiency towards various target products, including syngas, hydrocarbons, oxygenates, and advanced carbon materials. The discussion also encompasses opportunities and challenges for the ECM process, including insights into ECM pathways, rational electrocatalyst design, establishment of benchmarking protocols, electrolyte engineering, enhancement of CH4conversion rates, and minimization of CO_(2)emission.

关键词： Methane Electrochemical conversion Reaction mechanism Catalyst design Electrode Electrocatalysis

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Development of an automated photolysis rates prediction system based on machine learning

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Journal of Environmental Sciences 2025年第5期151卷 211-224页

作者： Weijun Pan Sunling Gong Huabing Ke Xin Li Duohong Chen Cheng Huang Danlin song State Key Laboratory of Severe Weather&Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological SciencesBeijing 100081China College of Environmental Sciences and Engineering Peking UniversityBeijing 100871China State Environmental Key Laboratory of Reginal Air Quality Monitoring Guangdong Ecological Environmental Monitoring CenterGuangzhou 510308China State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Complex Shanghai Academy of Environmental SciencesShanghai 200233China Chengdu Academy of Environmental Sciences Chengdu 610072China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research InstituteMacao University of Science and TechnologyMacao 999078China

Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human *** evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,*** hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,*** with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical *** feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.

关键词： J-values Automated prediction system Machine learning Short-term prediction O_(3) simulated improvement

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Key Steps and Catalyst Performance for Conversion of Cellulose to Isosorbide: AReview

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Journal of Renewable Materials 2025年第1期13卷 49-78页

作者： Dan Wang Jihuan song Qiyuan Wang Chenmeng Cui Ying Yang State Key Laboratory of Heavy Oil Processing China University of Petroleum-BeijingBeijing102249China

Upgrading of abundant cellulosic biomass to isosorbide can reduce the dependence on limited fossil resources and provide a sustainable way to produce isosorbide,utilized for polymers,medicine and health care product *** review comprehensively examines the key steps and catalytic systems involved in the conversion of cel-lulose to ***,the reaction pathway from cellulose to isosorbide is elucidated,emphasizing three critical steps:cellulose hydrolysis,glucose hydrogenation,and the two-step dehydration of sorbitol to produce ***,the activation energy and acidic sites during cellulose hydrolysis,the impact of metal particle size and catalyst support on hydrogenation,and the effects of catalyst acidity,pore structure,and reaction conditions on sorbitol dehydration have been thoroughly ***,the progress made in cellulose con-version to isosorbide is summarized,current challenges are highlighted,and future development trends are pro-jected in this review.

关键词： Cellulose isosorbide reaction pathway key steps catalytic systems

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Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

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Computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun song Fen Luo School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

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Mechanistic insight on nanomaterial-induced reactive oxygen species formation

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Journal of Environmental Sciences 2025年第5期151卷 200-210页

作者： Jianzhong Cao Qingchun Wu Xuting Liu Xiangyu Zhu Chunfeng Huang Xinyu Wang Yang song State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China

Reactive oxygen species(ROS)are closely related to cell death,proliferation and ***,excessive ROS levels may exceed the cellular oxidative capacity and cause irreversible *** are often inadvertently exposed to nanomaterials(NMs).Therefore,elucidating the specific routes of ROS generation induced by NMs is crucial for comprehending the toxicity mechanisms of NMs and regulating their potential *** paper provides a comprehensive review of the toxicity mechanisms and applications of NMs from three perspectives:(1)Organelle *** the impact of NMmediated ROS onmitochondria,unravelingmechanisms at the organelle level.(2)NMs’*** the broad applications and biosafety considerations of Nanozymes,a unique class of NMs.(3)Cellular *** the toxic effects and mechanisms of NMs in cells at a holistic cellular *** on these perspectives,the paper scrutinizes the regulation of Fenton reactions by NMs in ***,it introduces diseases resulting fromNM-mediated ROS at the organism *** comprehensive review aims to provide valuable insights for studying NM-mediated mechanisms at both cellular and organism levels,offering considerations for the safe design of nanomaterials.

关键词： Nanomaterials Reactive oxygen species(ROS) Toxicity Mitochondria Oxidative stress

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Co-loading Pd and CeO_(2)on Silicalite-1 as High-performance Catalyst for Methane Dry Reforming Reaction

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Chemical Research in Chinese Universities 2025年第1期41卷 15-20页

作者： XU Jing ZHANG Rui WANG Ke WANG Xiao song Shuyan ZHANG Hongjie State Key Laboratory of Rare Earth Resource Utilization Changchun Institute of Applied ChemistryChinese Academy of SciencesChangchun130022P.R.China School of Applied Chemistry and Engineering University of Science and Technology of ChinaHefei230026P.R.China Department of Chemistry Tsinghua UniversityBeijing100084P.R.China

The methane dry reforming(DRM)reaction can convert CO_(2)and CH_(4),both of which contribute to climate change,into syngas,which holds great significance in mitigating specific environmental issues stemming from the greenhouse ***,the challenges that persist include the substantial energy consumption and the catalyst’s susceptibility to deactivation,both of which necessitate ***,we developed a catalyst,PdCe/S1,featuring small-sized Pd species and CeO_(2)stabilized on pure silicon zeolite(silicalite-1),which is employed in the DRM *** can achieve 97%CH_(4)conversion and 98%CO_(2)conversion at 750℃,surpassing binary Pd/CeO_(2)and Pd/S1 *** small size of CeO_(2)stabilized by silicalite-1 promotes oxygen defects formation and enhances the CO_(2)adsorption *** introduction of silicalite-1 further enhances the interaction between Pd and CeO_(2),boosting DRM performance.

关键词： Dry reforming of methane CeO_(2) Zeolite

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Improving the Seasonal Forecast of Summer Precipitation in Southeastern China Using a CycleGAN-based Deep Learning Bias Correction Method

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Advances in Atmospheric Sciences 2025年第1期42卷 26-35页

作者： song YANG Fenghua LING Jing-Jia LUO Lei BAI Institute of Climate Application Research(ICAR)/School of Future Technology/CIC-FEMD/KLME/ILCEC Nanjing University of Information Science and TechnologyNanjing 210044China Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and ***,the intensity of summer precipitation is often largely underestimated in many current dynamic *** study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)*** the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model *** study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.

关键词： bias correction CycleGAN QM NUIST-CFS 1.0 extreme precipitation

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