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Carbon Energy 2024年第9期6卷 306-324页

作者： Hong-Jin Son Jeemin Hwang Min Young Choi Seung Hee Park jae hyuk jang Byungchan Han Sung Hoon Ahn Department of Bio-Chemical Engineering Chosun UniversityGwangjuRepublic of Korea Fuel Cell Research and Demonstration Center Hydrogen Energy Research DivisionKorea Institute of Energy Research(KIER)Buan-gunJeollabuk-doRepublic of Korea Department of Chemical and Biomolecular Engineering Yonsei UniversitySeoulRepublic of Korea Center for Research Equipment Korea Basic Science InstituteDaejeonRepublic of Korea

This study explores a symmetric configuration approach in anion exchange membrane(AEM)water electrolysis,focusing on overcoming adaptability challenges in dynamic ***,a rapid and mild synthesis technique for fabricating fibrous membrane-type catalyst electrodes is *** method leverages the contrasting oxidation states between the sulfur-doped NiFe(OH)2 shell and the metallic Ni core,as revealed by electron energy loss *** evaluations confirm that the S–NiFe(OH)_(2) active sites optimize free energy for alkaline water electrolysis *** technique bypasses traditional energy-intensive processes,achieving superior bifunctional activity beyond current *** symmetric AEM water electrolyzer demonstrates a current density of 2 A cm^(-2) at 1.78 V at 60℃ in 1 M KOH electrolyte and also sustains ampere-scale water electrolysis below 2.0 V for 140 h even in ambient *** results highlight the system's operational flexibility and structural stability,marking a significant advance-ment in AEM water electrolysis technology.

关键词： AEM water electrolysis fibrous membrane iR correction free operational stability symmetric configuration

Laser‑Induced and MOF‑Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

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Nano-Micro Letters 2024年第6期16卷 210-220页

作者： Hyeongtae Lim Hyeokjin Kwon Hongki Kang jae Eun jang hyuk‑Jun Kwon Department of Electrical Engineering and Computer Science DGISTDaegu 42988South Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988South Korea

Advancements in sensor technology have significantly enhanced atmospheric ***,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing ***,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in ***,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser *** inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C *** laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C *** optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based *** research opens up promising avenues for practical applications in MOF-derived sensing devices.

关键词： Metal-organic frameworks Metal oxide Carbon composite Laser Gas sensor

A review of understanding electrocatalytic reactions in energy conversion and energy storage systems via scanning electrochemical microscopy

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Journal of Energy Chemistry 2024年第4期91卷 155-177页

作者： Jihye Park Jong Hwan Lim Jin-hyuk Kang Jiheon Lim Ho Won jang Hosun Shin Sun Hwa Park Interdisciplinary Materials Measurement Institute Korea Research Institute of Standards and ScienceDaejeon 34113Republic of Korea Department of Material Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Department of Applied Measurement Science University of Science and TechnologyDaejeon 34113Republic of Korea

To address climate change and promote environmental sustainability,electrochemical energy conversion and storage systems emerge as promising alternative to fossil fuels,catering to the escalating demand for *** optimal energy efficiency and cost competitiveness in these systems requires the strategic design of electrocatalysts,coupled with a thorough comprehension of the underlying mechanisms and degradation behavior occurring during the electrocatalysis *** electrochemical microscopy(SECM),an analytical technique for studying surface electrochemically,stands out as a powerful tool offering electrochemical *** possesses remarkable spatiotemporal resolution,enabling the visualization of the localized electrochemical activity and surface *** review compiles crucial research findings and recent breakthroughs in electrocatalytic processes utilizing the SECM methodology,specifically focusing on applications in electrolysis,fuel cells,and metal–oxygen batteries within the realm of energy conversion and storage *** with an overview of each energy system,the review introduces the fundamental principles of SECM,and aiming to provide new perspectives and broadening the scope of applied research by describing the major research categories within SECM.

关键词： Scanning electrochemical microscopy Electrocatalyst Electrocatalysis Water splitting Fuel cell Metal-oxygen battery

Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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Computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan Muhammad Ramzan Mudassar Raza Muhammad Attique Khan Khalid Iqbal Taerang Kim jae-hyuk Cha Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

Acoustofluidic separation of prolate and spherical micro-objects

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Microsystems & Nanoengineering 2024年第1期10卷 217-230页

作者： Muhammad Soban Khan Mushtaq Ali Song Ha Lee Keun Young jang Seong jae Lee Jinsoo Park Department of Mechanical Engineering Chonnam National University77 Yongbong-roBuk-guGwangju 61186Republic of Korea Department of Polymer Engineering The University of Suwon17 Wauan-gilBongdam-eupHwaseongGyeonggi 18323Republic of Korea

Most microfluidic separation techniques rely largely on object size as a separation *** ability to separate micro-objects based on their shape is crucial in various biomedical and chemical ***,we develop an ondemand,label-free acoustofluidic method to separate prolate ellipsoids from spherical microparticles based on traveling surface acoustic wave-induced acoustic radiation force and *** freely rotating non-spherical microobjects were aligned under the progressive acoustic field by the counterrotating radiation torque,and the major axis of the prolate ellipsoids was parallel to the progressive wave *** specific alignment of the ellipsoidal particles resulted in a reduction in the cross-sectional area perpendicular to the wave *** a consequence,the acoustic backscattering decreased,resulting in a decreased magnitude of the radiation *** the variation in radiation force,which depended on the micro-object morphology enabled the acoustofluidic shape-based *** conducted numerical simulations for the wave scattering of spherical and prolate objects to elucidate the working mechanism underlying the proposed method.A series of experiments with polystyrene microspheres,prolate ellipsoids,and peanut-shaped microparticles were performed for *** quantitative analysis of the separation efficiency,we confirmed the high purity and high recovery rate of the proposed acoustofluidic shapebased separation of *** a bioparticle,we utilize Thalassiosira eccentrica to perform shape-based separation,as the species has a variety of potential applications in drug delivery,biosensing,nanofabrication,bioencapsulation and immunoisolation.

关键词： separation spherical rotating

Crystallographically vacancy‐induced MOF nanosheet as rational single‐atom support for accelerating CO_(2)electroreduction to CO

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Carbon Energy 2024年第8期6卷 49-62页

作者： Jin hyuk Cho Joonhee Ma Chaehyeon Lee Jin Wook Lim Youngho Kim Ho Yeon jang jaehyun Kim Myung‐gi Seo Youngheon Choi Youn Jeong jang Sang Hyun Ahn Ho Won jang Seoin Back Jong‐Lam Lee Soo Young Kim Department of Materials Science and Engineering Korea UniversitySeoulRepublic of Korea Department of Chemical and Biomolecular Engineering Sogang UniversitySeoulRepublic of Korea Department of Materials Science and Engineering Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Department of Materials Science and Engineering Research Institute of Advanced MaterialsSeoul National UniversitySeoulRepublic of Korea Lotte Chemical R&D Center DaejeonRepublic of Korea Department of Chemical Engineering Hanyang UniversitySeoulRepublic of Korea School of Chemical Engineering and Material Science Chung‐Ang UniversitySeoulRepublic of Korea

To attain a circular carbon economy and resolve CO_(2)electroreduction technology obstacles,single‐atom catalysts(SACs)have emerged as a logical option for electrocatalysis because of their extraordinary catalytic *** SACs,metal–organic frameworks(MOFs)have been recognized as promising support materials because of their exceptional ability to prevent metal *** study shows that atomically dispersed Ni single atoms on a precisely engineered MOF nanosheet display a high Faradaic efficiency of approximately 100%for CO formation in H‐cell and three‐compartment microfluidic flow‐cell reactors and an excellent turnover frequency of 23,699 h−1,validating their intrinsic catalytic *** results suggest that crystallographic variations affect the abundant vacancy sites on the MOF nanosheets,which are linked to the evaporation of Zn‐containing organic linkers during ***,using X‐ray absorption spectroscopy and density functional theory calculations,a comprehensive investigation of the unsaturated atomic coordination environments and the underlying mechanism involving CO^(*)preadsorbed sites as initial states was possible and provided valuable insights.

关键词： 2‐dimensional material carbon dioxide reduction metal-organic frameworks single‐atom catalysts vacancy sites

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim jae-hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

Unraveling the Harmonious Coexistence of Ruthenium States on a Self-Standing Electrode for Enhanced Hydrogen Evolution Reaction

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Energy & Environmental Materials 2024年第6期7卷 303-311页

作者： Joonhee Ma Jin hyuk Cho Chaehyeon Lee Moon Sung Kang Sungkyun Choi Ho Won jang Sang Hyun Ahn Seoin Back Soo Young Kim Department of Materials Science and Engineering Korea UniversitySeoul 02841Korea Department of Chemical and Biomolecular Engineering Institute of Emergent MaterialsSogang UniversitySeoul 04107Korea Department of Cogno-Mechatronics Engineering College of Nanoscience and NanotechnologyPusan National UniversityBusan 46241Korea Department of Materials Science and Engineering Research Institute of Advanced MaterialsSeoul National UniversitySeoul 08826Korea School of Chemical Engineering and Material Science Chung-Ang UniversitySeoul 06974Korea

The development of cost-effective,highly efficient,and durable electrocatalysts has been a paramount pursuit for advancing the hydrogen evolution reaction(HER).Herein,a simplified synthesis protocol was designed to achieve a self-standing electrode,composed of activated carbon paper embedded with Ru single-atom catalysts and Ru nanoclusters(ACP/Ru_(SAC+C))via acid activation,immersion,and high-temperature *** initio molecular dynamics(AIMD)calculations are employed to gain a more profound understanding of the impact of acid activation on carbon ***,the coexistence states of the Ru atoms are confirmed via aberration-corrected scanning transmission electron microscopy(AC-STEM),X-ray photoelectron spectroscopy(XPS),and X-ray absorption spectroscopy(XAS).Experimental measurements and theoretical calculations reveal that introducing a Ru single-atom site adjacent to the Ru nanoclusters induces a synergistic effect,tuning the electronic structure and thereby significantly enhancing their catalytic ***,the ACP/Ru_(SAC+C)exhibits a remarkable turnover frequency(TOF)of 18 s^(−1)and an exceptional mass activity(MA)of 2.2 A mg^(−1),surpassing the performance of conventional Pt *** self-standing electrode,featuring harmoniously coexisting Ru states,stands out as a prospective choice for advancing HER catalysts,enhancing energy efficiency,productivity,and selectivity.

关键词： electrocatalysis electronic coupling effect hydrogen evolution reaction selfstanding electrode

An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

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Computer Systems Science & Engineering 2024年第2期48卷 529-554页

作者： Irfan Haider Muhammad Attique Khan Muhammad Nazir Taerang Kim jae-hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science Hanyang UniversitySeoulKorea

Fruit infections have an impact on both the yield and the quality of the *** a result,an automated recognition system for fruit leaf diseases is *** artificial intelligence(AI)applications,especially in agriculture,deep learning shows promising disease detection and classification *** recent AI-based techniques have a few challenges for fruit disease recognition,such as low-resolution images,small datasets for learning models,and irrelevant feature *** work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder *** fruit types have been employed in this work for the validation process,such as apple,grape,and *** the first step,a noisy dataset is prepared by employing the original images to learn the designed framework *** EfficientNet-B0 deep model is fine-tuned on the next step and trained separately on the original and noisy *** that,features are fused using a serial concatenation approach that is later optimized in the next step using an improved Path Finder Algorithm(PFA).This algorithm aims to select the best features based on the fitness score and ignore redundant *** selected features are finally classified using machine learning classifiers such as Medium Neural Network,Wide Neural Network,and Support Vector *** experimental process was conducted on each fruit dataset separately and obtained an accuracy of 100%,99.7%,99.7%,and 93.4%for apple,grape,Citrus fruit,and citrus plant leaves,respectively.A detailed analysis is conducted and also compared with the recent techniques,and the proposed framework shows improved accuracy.

关键词： Fruit disease contrast enhancement augmentation deep learning fusion feature selection classification

Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

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Nano-Micro Letters 2024年第12期16卷 103-119页

作者： Gi Baek Nam Jung-El Ryu Tae Hoon Eom Seung Ju Kim Jun Min Suh Seungmin Lee Sungkyun Choi Cheon Woo Moon Seon Ju Park Soo Min Lee Byungsoo Kim Sung hyuk Park Jin Wook Yang Sangjin Min Sohyeon Park Sung Hwan Cho hyuk Jin Kim Sang Eon Jun Tae Hyung Lee Yeong jae Kim jae Young Kim Young Joon Hong Jong-In Shim Hyung-Gi Byun Yongjo Park Inkyu Park Sang-Wan Ryu Ho Won jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Research Laboratory of Electronics Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeMA 02139USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCA 90089USA Department of Display Materials Engineering Soonchunhyang UniversityAsan 31538Republic of Korea Department of Photonics and Nanoelectronics BK21 FOUR ERICA-ACE CenterHanyang University ERICAAnsan 15588Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok 25913Republic of Korea Advance Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Physics Chonnam National UniversityGwangju 500-757Republic of Korea

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power ***,despite these benefits,challenges still exist such as a limited range of detectable gases and slow *** this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power *** optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain ***,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)*** noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and ***-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

关键词： Micro-LED Gas sensor array Low power consumption Metal decoration Real-time detection