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A mems _-preconcentrator employing a carbon molecular sieve membrane for highly volatile organic compound sampling

Chemosensors 2021年第5期9卷

作者： Kuo, Hung-Yang Cheng, Wei-Riu Wu, Tzu-Heng Sheen, Horn-Jiunn Wang, Chih-Chia Lu, Chia-Jung Department of Chemistry National Taiwan Normal University Taipei11677 Taiwan Institute of Applied Mechanics National Taiwan University Taipei10617 Taiwan Department of Chemical and Materials Engineering Chung Cheng Institute of Technology National Defense University Taoyuan33509 Taiwan Undergraduate Degree Program of System Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

This paper presents the synthesis and evaluation of a carbon molecular sieve membrane (CMSM) grown inside a MEMS-fabricated μ-preconcentrator for sampling highly volatile organic compounds. An array of µ-pillars measuring 100 µm in diameter and 250 µm in height were fabricated inside a microfluidic channel to increase the attaching surface for the CMSM. The surface area of the CMSM was measured as high as 899 m2/g. A GC peak amplification factor >2 × 104 was demonstrated with gaseous ethyl acetate. Up to 1.4 L of gaseous ethanol at the 100 ppb level could be concentrated without exceeding the capacity of this microchip device. Sharp desorption chromatographic peaks ( © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Molecular sieves

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X-ray image based on Gray Level Cooccurrence Matrices (GLCM) k-nearest neighbor (KNN) to detect tuberculosis

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AIP Conference Proceedings 2023年第1期2536卷

作者： Suhariningsih Mohammad Yazid Bastomi Endah Purwanti Dita Aprilia Hariyani Perwira Annissa Dyah Permatasari Suryani Dyah Astuti Department of Physics Faculty of Sains and Technology Universitas Airlangga Surabaya 60115 East Java Indonesia Biomedical Engineering Program Department of Physics Faculty of Sains and Technology Universitas Airlangga Surabaya 60115 Indonesia Information System Program Department of Mathematics Faculty of Sains and Technology Universitas Airlangga Surabaya 60115 Indonesia

Tuberculosis is an infectious disease caused by a bacterium called bacillus mycobacterium tuberculosis. Tuberculosis is spread through coughing and sneezing which affects the lungs of people infected with pulmonary tuberculosis. One of the methods is using the thorax image. However, accuracy without a standard is the problem in this topic. It's caused by the analysis result depend on the ability of the medical experts only. In this study, a Tuberculosis detection program was designed using the k-nearest neighbor classification method and Gray Level Cooccurrence Matrices (GLCM) features as classification input. So that the detection program was expected to be a tool for medical experts who had standardized accuracy. The GLCM features were to input the k-nearest neighbor (kNN) classification which are contrast, correlation, energy, entropy, and homogeneity. The program output was divided into 2 classes namely abnormal (tuberculosis) and normal. The combination of entropy-correlation and entropy-energy-correlation features by an optimal level of accuracy, sensitivity, and specificity showed a value of k=1 that is 92%, 92%, 92%.

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Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

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Big Earth Data 2024年第2期8卷 189-230页

作者： Alexander Mahura Alexander Baklanov Risto Makkonen Michael Boy Tuukka Petäjä Hanna KLappalainen Roman Nuterman Veli-Matti Kerminen Stephen R.Arnold Markus Jochum Anatoly Shvidenko Igor Esau Mikhail Sofiev Andreas Stohl Tuula Aalto Jianhui Bai Chuchu Chen Yafang Cheng Oxana Drofa Mei Huang Leena Järvi Harri Kokkola Rostislav Kouznetsov Tingting Li Piero Malguzzi Sarah Monks Mads Bruun Poulsen Steffen M.Noe Yuliia Palamarchuk Benjamin Foreback Petri Clusiu Till Andreas Soya Rasmussen Jun She Jens Havskov Sørensen Dominick Spracklen Hang Su Juha Tonttila Siwen Wang Jiandong Wang Tobias Wolf-Grosse Yongqiang Yu Qing Zhang Wei Zhang Wen Zhang Xunhua Zheng Siqi Li Yong Li Putian Zhou Markku Kulmala Faculty of Science Institute for Atmospheric and Earth System Research(INAR)/PhysicsUniversity of Helsinki(UHEL)HelsinkiFinland Science and Innovation Department World Meteorological Organization(WMO)GenevaSwitzerland Niels Bohr Institute University of CopenhagenCopenhagenDenmark Climate System Research Finnish Meteorological Institute(CSR-FMI)HelsinkiFinland School of Engineering Sciences Lappeenranta University of TechnologyLappeenrantaFinland Institute for Climate and Atmospheric Science School of Earth and EnvironmentUniversity of LeedsLeedsUK Biodiversity and Natural Resources Program International Institute for Applied Systems Analysis(IIASA)LaxenburgAustria Department of Physics and Technology UiT-The Arctic University of NorwayTromsoNorway Nansen Environmental and Remote Sensing Center(NERSC) BergenNorway Atmospheric Composition Finnish Meteorological Institute(AC-FMI)HelsinkiFinland Norwegian Institute for Air Research(NILU) KjellerNorway LAGEO Institute of Atmospheric PhysicsChinese Academy of Sciences(IAP-CAS)BeijingChina Max Planck Institute for Chemistry(MPI-Chem) MainzGermany Institute of Atmospheric Sciences and Climate National Research Council of Italy(CNR-15SAC)BolognaItaly Institute of Geographic Sciences and Natural Resources Research(IGSNRR) Chinese Academy of Sciences(CAS)BeijingChina Faculty of Science Helsinki Institute of Sustainability Science(HELSUS)University of Helsinki(UHEL)HelsinkiFinland Atmospheric Research Centre of Eastern Finland Finnish Meteorological Institute(ARCEF-FMI)KuopioFinland Institute of Atmospheric Physics Chinese Academy of Sciences(IAP-CAS)BeijingChina Department of Plant Physiology Estonian University of Life Sciences(EULS)Institute of Agricultural and Environmental Sciences(IAES)TartuEstonia Danish Meteorological Institute(DMI) CopenhagenDenmark State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric Physics(IAP)Chinese Academy of Sciences(CAS)BeijingChina Uni

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research *** PEEX MP has more than 30 models and is directed towards seamless envir-onmental *** main focus area is the Arctic-boreal regions and *** models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and *** paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical *** employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are *** virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are *** over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,*** population,envir-onment and climate in the PEEX *** work was also one of the last projects of *** Zilitinkevich,who passed away on 15 February *** the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

关键词： Multi-scale and-processes modelling concept seamless coupling high-performance computing data infrastructure virtual research platforms

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Corrigendum to “Synthesis and characterization of eco-friendly chitosan-based waterborne polyurethanes and their ultraviolet degradation properties” [Polymer Degradation and Stability, 233 (2025) 111169]

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Polymer Degradation and Stability 2025年 236卷

作者： Chun-Ta Wei Jhu-Lin You Kai-Yen Chin Shu Mei Chang Shun-Yi Jian An-Yu Cheng Department of Chemical & Materials Engineering Chung Cheng Institute of Technology National Defense University Taoyuan 335 Taiwan System Engineering and Technology Program National Chiao Tung University Hsinchu 300 Taiwan Institute of Organic and Polymeric Materials Research and Development Centre for Smart Textile Technology National Taipei University of Technology Taipei 10608 Taiwan Department of Material Engineering Ming Chi University of Technology New Taipei 24301 Taiwan Center for Plasma and Thin Film Technologies Ming Chi University of Technology New Taipei 243303 Taiwan

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Direct insulator to relativistic quantum Hall transition in graphene

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Physical Review B 2023年第20期108卷 205304-205304页

作者： Ching-Chen Yeh Pin-Chi Liao Dinesh K. Patel Wei-Chen Lin Siang-Chi Wang Albert F. Rigosi Randolph E. Elmquist Chi-Te Liang Department of Physics National Taiwan University Taipei 106 Taiwan Physical Measurement Laboratory National Institute of Standards and Technology (NIST) Gaithersburg Maryland 20899 USA Physikalisch-Technische Bundesanstalt (PTB) Bundesallee 100 Braunschweig 38116 Germany Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu 300 Taiwan Center for Quantum Science and Engineering National Taiwan University Taipei 106 Taiwan Taiwan Consortium of Emergent Crystalline Materials (TCECM) Taipei 106 Taiwan

We present evidence of the direct insulator-quantum Hall transition in monolayer epitaxial graphene, a genuine two-dimensional (2D) system. We studied the transition from an insulating state to a quantum Hall state at a high Landau level filling factor ν=6>3 and the plateau-plateau transition from ν=6 to ν=2. Using scaling theory, the critical exponent κ was determined to be 0.41±0.02 for the direct insulator-quantum Hall transition. This closely aligns with κ=0.42±0.02 from the ν=6 to ν=2 quantum Hall plateau-plateau transition. These findings suggest that the two transitions may belong to the same universality class. While similar transitions have been explored in conventional two-dimensional charge systems, our study provides valuable insights into a truly 2D system, deepening our understanding of these transitions.

关键词： Electrical properties Fermions Landau levels Graphene Hall bar Resistivity measurements

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The effect of koh activator concentration upon the characteristics of biomass-derived water hyacinth process on lithium-ion capacitor 16th

The effect of koh activator concentration upon the character...

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16th International Conference on Quality in Research, QiR 2019

作者： Luthfi, Muhammad Paduraksa, Jagad Verdianto, Ariono Pambudi, Yoyok Dwi Setyo Priyono, Bambang Syahrial, Anne Zulfia Lee, Joong Kee Hudaya, Chairul Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok16424 Indonesia Energy System Engineering Study Program Faculty of Engineering Universitas Indonesia Depok16424 Indonesia Department of Metallurgy and Materials Engineering Faculty of Engineering Universitas Indonesia Depok16424 Indonesia Center for Nuclear Reactor Technology and Safety BATAN Puspiptek Area SerpongTangerang Selatan15310 Indonesia Hwarang-ro 14-gil 5 Seongbuk-gu Seoul02792 Korea Republic of

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Reshaped Weyl fermionic dispersions driven by Coulomb interactions in MoTe2

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Physical Review B 2022年第4期105卷 045143-045143页

作者： Seoung-Hun Kang Sangjun Jeon Hyun-Jung Kim Wonhee Ko Suyeon Cho Se Hwang Kang Sung Wng Kim Heejun Yang Hyo Won Kim Young-Woo Son Korea Institute for Advanced Study Seoul 02455 Korea Department of Physics Chung-Ang University Seoul 06974 Korea Peter Grünberg Institut and Institute for Advanced Simulation Forschungszentrum Jülich and JARA D-52425 Jülich Germany Samsung Advanced Institute of Technology Suwon 16678 Korea Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Chemical Engineering and Materials Science Graduate Program in System Health Science and Engineering Ewha Womans University Seoul 03760 Korea Department of Energy Science Sungkyunkwan University Suwon 16419 Korea Department of Physics Korea Advanced Institute of Science and Technology Daejeon 34141 Korea

We report the direct evidence of impacts of the Coulomb interaction in a prototypical Weyl semimetal, MoTe2, that alter its bare bands in a wide range of energy and momentum. Our quasiparticle interference patterns measured using scanning tunneling microscopy are shown to match the joint density of states of quasiparticle energy bands including momentum-dependent self-energy corrections, while electronic energy bands based on the other simpler local approximations of the Coulomb interaction fail to explain neither the correct number of quasiparticle pockets nor the shape of their dispersions observed in our spectrum. With this, we predict a transition between type-I and type-II Weyl fermions with doping and resolve its disparate quantum oscillation experiments, thus highlighting the critical roles of Coulomb interactions in layered Weyl semimetals.

关键词： Electronic structure First-principles calculations Quasiparticles & collective excitations Topological materials Weyl fermions Scanning electron microscopy Scanning tunneling spectroscopy

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Rapid and non-destructive determination of vitamin C and antioxidant activity of intact red chilies using visible near-infrared spectroscopy and machine learning tools

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Case Studies in Chemical and Environmental engineering 2023年 8卷

作者： Devianti Sufardi Hafsah, Siti Sariadi Ahmad, Fachraniah Arianti, Nunik Destria Saputra, Edo Hartuti, Sri Department of Agricultural Engineering Faculty of Agriculture Syiah Kuala University Banda Aceh 23111 Indonesia Department of Soil Science Faculty of Agriculture Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Agrotechnology Faculty of Agriculture Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Chemical Engineering Politeknik Negeri Lhokseumawe Lhokseumawe 24301 Indonesia Department of Information System Nusa Putra University Sukabumi 43155 Indonesia Department of Agricultural Technology Faculty of Agriculture Universitas Riau Pekanbaru 28293 Indonesia Agricultural Engineering Study Program IPB University Bogor 16680 Indonesia

Red chili peppers are extensively used in the culinary industry due to their high vitamin C, antioxidant content, spice, and natural colorant properties. Traditional methods for determining these parameters are time-consuming and unstable. This study investigates the viability of visible near-infrared spectroscopy as a rapid and nondestructive method for determining vitamin C content and antioxidant activity in intact red chilies. Visible near-infrared spectroscopy measures the sample's light absorption, proportional to its chemical composition. Collecting intact red chili samples from various sources and determining their vitamin C content and antioxidant activity using conventional methods were required for the study. The visible near-infrared spectra of the pieces were collected using a spectrometer, and chemometric models were devised to correlate the spectra with the vitamin C content and antioxidant activity. Traditional methods were compared to visible near-infrared spectroscopy regarding their performance of prediction. In conjunction with chemometric models and machine learning algorithms, visible near-infrared spectroscopy could accurately predict red chilies' vitamin C content and antioxidant activity. The developed models exhibited high calibration and prediction coefficients, low root mean square errors, and high prediction-to-deviation ratios. The identified absorption peaks in the visible near-infrared spectra were related to the samples' color pigments and water content. This study demonstrates the feasibility of visible near-infrared spectroscopy as a rapid and nondestructive method for determining the vitamin C content and antioxidant activity of intact red chilies. The findings may have significant ramifications for the food industry, providing a more efficient and secure quality control and nutritional labeling method. It is recommended that additional research and validation be conducted to ensure the applicability of the developed models to var

关键词： Chemometrics tools Machine learning Red chilies Spectroscopy

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