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Maximizing biomass utilization: An integrated strategy for coproducing multiple chemicals

Journal of Energy Chemistry 2025年第1期100卷 180-191页

作者： Byeongchan Ahn Sua Jo Jonggeol Na J.Jay Liu Young-Ju Kim Wangyun Won department of Chemical and Biological engineering Korea University department of Chemical engineering (Integrated process) Kyung Hee University department of Chemical engineering and Materials science Ewha Womans University department of Chemical engineering Pukyong National University Resources engineering Plant Research department Korea Institute of Geoscience and Mineral Resources

Lignocellulosic biomass is one of the viable solutions to alleviate the global warming. However, the limited utilization of biomass majorly focused on cellulose and hemicellulose restricts the economic and environmental feasibilities. To cope with this issue, we proposed an integrated process of co-producing 1,6-hexanediol（1,6-HDO） with tetrahydrofuran and adipic acid from biomass, referred to as Strategy A. To compare the impacts of lignin upgrading and feedstock, Strategy B, which co-produces tetrahydrofuran alone, and Strategy C, which is the traditional route to produce 1,6-HDO from fossil fuels, were used. Heat networks are also designed to reduce operating costs and indirect carbon emissions due to energy consumption, saving 87% and 83% of the heat and cooling requirements, respectively, in Strategy A. The market competitiveness of Strategy A was evaluated by determining the minimum selling price through techno-economic analysis, and sustainability was thoroughly investigated by quantifying the environmental impacts through both midpoint and endpoint life-cycle assessments（LCAs）.Strategy A was found to be the most favorable both economically（USRDSCHARDOLLAR3,402/ton） and environmentally（-26.9 kg CO2eq.）. This indicates that lignin valorization is not only economically but also environmentally preferred. Finally, changes in economic and environmental feasibilities depending on economic, process, and environmental parameters were investigated using sensitivity and uncertainty analyses. The results of these analyses provide valuable insight into bio-based chemical production.

关键词： Biorefinery Carbon mitigation Climate change Global warming Lignin utilization Bioeconomy

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Magnetron sputtered nickel oxide with suppressed interfacial defect states for efficient inverted perovskite solar cells

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Journal of Energy Chemistry 2025年第1期100卷 348-355页

作者： Guoqiang Ma Qin Tan Zhaoning Li Jingwei Xiu Jiafeng Wang Tianle Cheng Dong He Qiang Sun Xuhang Ma Francesco Lamberti Zhubing He department of Materials science and engineering Institute of Innovative Materials (I2M) Shenzhen Key Laboratory of Full Spectral Solar Electricity Generation (FSSEG)Southern University of Science and Technology (SUSTech) Core Research Facilities Southern University of Science and Technology department of Chemical sciences Department of Industrial Engineering University of Padova

Widely used spin-coated nickle oxide （NiOx） based perovskite solar cells often suffer from severe interfacial reactions between the NiOxand adjacent perovskite layers due to surface defect states,which inherently impair device performance in a long-term view,even with surface molecule *** this study,we developed high-quality magnetron-sputtered NiOxthin films through detailed process optimization,and compared systematically sputtered and spin-coated NiOxthin film surfaces from materials to *** sputtered NiOxfilms exhibit improved crystallinity,smoother surfaces,and significantly reduced Ni3+or Ni vacancies compared to their spin-coated ***,the interface between the perovskite and sputtered NiOxfilm shows a substantially reduced density of defect *** solar cells （PSCs） fabricated with our optimally sputtered NiOxfilms achieved a high power conversion efficiency （PCE） of up to 19.93%and demonstrated enhanced stability,maintaining 86.2% efficiency during 500 h of maximum power point tracking under one standard sun ***,with the surface modification using (4-(2,7-dibromo-9,9-dimethylacridin-10（9H）-yl)butyl)p hosphonic acid （DMAcPA）,the device PCE was further promoted to 23.07%,which is the highest value reported for sputtered NiOxbased PSCs so far.

关键词： Nickle oxide Magnetron sputter Defect Stability Perovskite solar cell

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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Computer Modeling in engineering & sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana department of Civil engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of Computer engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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Co/Co_(7)Fe_(3)heterostructures with controllable alloying degree on carbon spheres as bifunctional electrocatalyst forrechargeable zinc-air batteries

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 476-487页

作者： Junkang Chen Yongyue Zhuang Yanxin Qiao Yu Zhang Aihua Yuan Hu Zhou School of Materials science and engineering Jiangsu University of Science and TechnologyZhenjiang 212003China School of Environmental and Chemical engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

Exploring efficient and nonprecious metal electrocatalysts of oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)is crucial for developing rechargeable zinc-air batteries(ZABs).Herein,an alloying-degree control strategy was employed to fabricate nitrogen-doped carbon sphere(NCS)decorated with dual-phase Co/Co_(7)Fe_(3)heterojunctions(CoFe@NCS).The phase composition of materials has been adjusted by controlling the alloying *** optimal CoFe_(0.08)@NCS electrocatalyst displays a half-wave potential of 0.80 V for ORR and an overpotential of 283 mV at 10 mA·cm^(-2)for OER in an alkaline *** intriguing bifunctional electrocatalytic activity and durability is attributed to the hierarchically porous structure and interfacial electron coupling of highly-active Co_(7)Fe_(3)alloy and metallic Co *** the CoFe_(0.08)@NCS material is used as air-cathode catalyst of rechargeable liquid-state zinc-air battery(ZAB),the device shows a high peak power-density(157 mW·cm^(-2))and maintains a stable voltage gap over 150 h,outperforming those of the benchmark(Pt/C+RuO_(2))-based *** particular,the as-fabricated solid-state flexible ZAB delivers a reliable compatibility under different bending *** work provides a promising strategy to develop metal/alloy-based electrocatalysts for the application in renewable energy conversion technologies.

关键词： bifunctional electrocatalysts oxygen reduction reaction oxygen evolution reaction zinc-air battery metal/alloy carbon sphere

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A novel solution treatment and aging for powder bed fusion-laser beam Ti-6Al-2Sn-4Zr-6Mo alloy:Microstructural and mechanical characterization

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 414-424页

作者： Gianluca Pirro Alessandra Martucci Alessandro Morri Mariangela Lombardi Lorella Ceschini department of Industrial engineering(DIN) Alma Mater Studiorum-University of BolognaBologna 40136Italy department of Applied science and Technology(DISAT) Politecnico di TorinoTorino TO 10129Italy

Ti-6Al-4Zr-2Sn-6Mo alloy is one of the most recent titanium alloys processed using powder bed fusion-laser beam(PBF-LB)*** alloy has the potential to replace Ti-6Al-4V in automotive and aerospace applications,given its superior mechanical properties,which are approximately 10%higher in terms of ultimate tensile strength(UTS)and yield strength after appropriate heat *** as-built conditions,the alloy is characterized by the presence of soft orthorhombicα″martensite,necessitating a postprocessing heat treatment to decompose this phase and enhance the mechanical properties of the ***,PBFed Ti6246 components undergo an annealing process that transforms theα″martensite into anα-βlamellar *** primary objective of this research was to develop a solution treatment and aging(STA)heat treatment tailored to the unique microstructure produced by the additive manufacturing process to achieve an ultrafine bilamellar microstructure reinforced by precipitation *** study investigated the effects of various solution temperatures in theα-βfield(ranging from 800 to 875℃),cooling media(air and water),and aging time to determine the optimal heat treatment parameters for achieving the desired bilamellar *** each heat treatment condition,differentα-βmicrostructures were found,varying in terms of theα/βratio and the size of the primaryα-phase *** attention was given to how these factors were influenced by increases in solution temperature and how microhardness correlated with the percentage of the metastableβphase present after *** tests were performed on samples subjected to the most promising heat treatment parameters.A comparison with literature data revealed that the optimized STA treatment enhanced hardness and UTS by13%and 23%,respectively,compared with those of the annealed *** surface analyses were conducted to investigate fracture mechanisms.

关键词： powder bed fusion-laser beam titanium alloys heat treatments mechanical properties fractographic analysis

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of Computer science and engineering College of Engineering GuindyAnna UniversityChennai600025India department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University department of Electrical engineering and Automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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Preparation of silver nanoparticles through the reduction of straw-extracted lignin and its antibacterial hydrogel

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 504-514页

作者： Lou Zhang Shuo Li Fu Tang Jingkai Zhang Yuetong Kang Hean Zhang Lidong Li State Key Laboratory for Advanced Metals and Materials School of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China

Silver nanoparticles(Ag NPs)have attracted attention in the field of biomaterials due to their excellent antibacterial ***,the reducing and stabilizing agents used for the chemical reduction of Ag NPs are usually toxic and may cause water *** this work,Ag NPs(31.2 nm in diameter)were prepared using the extract of straw,an agricultural waste,as the reducing and stabilizing *** analysis revealed that the straw extract contained lignin,the structure of which possesses phenolic hydroxyl and methoxy groups that facilitate the reduction of silver salts into Ag *** surfaces of Ag NPs were negatively charged due to the encapsulation of a thin layer of lignin molecules that prevented their *** the prepared Ag NPs were added to the precursor solution of acrylamide,free radical polymerization was triggered without the need for extra heating or light irradiation,resulting in the rapid formation of an Ag NP-polyacrylamide composite *** inhibition zone test proved that the composite hydrogel possessed excellent antibacterial ability due to the presence of Ag *** prepared hydrogel may have potential applications in the fabrication of biomedical materials,such as antibacterial dressings.

关键词： silver nanoparticles hydrogel straw extraction antibacterial

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Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation

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Computer Modeling in engineering & sciences 2025年第1期142卷 1-48页

作者： Hariharasakthisudhan Ponnarengan Sivakumar Rajendran Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj department of Mechanical engineering Dr.Mahalingam College of Engineering and TechnologyPollachiCoimbatoreTamil Nadu642003India department of Mechanical engineering JKKN College of Engineering and TechnologyKumarapalayamNamakkalTamil Nadu637209India department of Mechanical engineering Gharda Institute of TechnologyLavelMaharashtra415708India department of Electrical and Electronics engineering Sri Eshwar College of EngineeringCoimbatoreTamilnadu641202India department of Mechanical engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical *** review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health *** findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and *** synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment ***,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease *** review emphasizes the need for interdisciplinary collaboration to further advance this *** research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and *** fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

关键词： Computational models biomedical engineering bioinformatics machine learning wearable technology

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Effect of Nb-V microalloying on the hot deformation behavior of medium Mn steels

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 360-368页

作者： Yumeng Wang Qinyi Guo Bin Hu Haiwen Luo School of Metallurgical and Ecological engineering University of Science and Technology BeijingBeijing 100083China

The influence of Nb-V microalloying on the hot deformation behavior and microstructures of medium Mn steel(MMS)was investigated by uniaxial hot compression *** establishing the constitutive equations for simulating the measured flow curves,we successfully constructed deformation activation energy(Q)maps and processing maps for identifying the region of flow *** concluded the following consequences of Nb-V alloying for MMS.(i)The critical strain increases and the increment diminishes with the increasing deformation temperature,suggesting that NbC precipitates more efficiently retard dynamic recrystallization(DRX)in MMS compared with solute Nb.(ii)The deformation activation energy of MMS is significantly increased and even higher than that of some reported high Mn steels,suggesting that its ability to retard DRX is greater than that of the high Mn content.(iii)The hot workability of MMS is improved by narrowing the hot processing window for the unstable flow stress,in which fine recrystallized and coarse unrecrystallized grains are present.

关键词： medium manganese steel niobium-vanadium microalloying flow behavior dynamic recrystallization hot workability

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