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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Hot air treatment: Alternative annealing of TiO2 nanoparticulate films without substrate deformation

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AIP Conference Proceedings 2020年第1期2279卷

作者： Arisara Panthawan Winai Thongpan Porntipa Pooseekheaw Nidchamon Jumrus Tewasin Kumpika Wattikon Sroila Ekkapong Kantarak Panupong Sanmuangmoon Adisorn Tuantranont Wiradej Thongsuwan Pisith Singjai 1PhD’s Degree Program in Materials Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 3Department of Physics and Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 2Center of Excellence in Materials Science and Technology Chiang Mai University Chiang Mai 50200 Thailand 4Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand 5Thailand Organic and Printed Electronics Innovation Center National Electronics and Computer Technology Center National Science and Technology Development Agency (Klong Luang Pathumthani 12120 Thailand) 6Center of Advanced Materials of Printed Electronics and Sensors Materials Science Research Center Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand

Titanium dioxide (TiO2) nanoparticulate films was successfully annealed on a glass substrate via alternative hot air treatment (HAT). Interestingly, HAT not only protects the glass substrate deformation but can also reduce cost and time in the annealing process. The annealed films using HAT at 500 °C for 10 min can be compared with the annealed films in conventional furnace heat treatment (FHT) at 500 °C for 60 min. The results showed the sizes of ∼7.9 nm are obtained after the films annealed with both annealing techniques. The surface roughness of the as-deposited, the annealed films using FHT and HAT were 17.37, 23.74 and 23.26 nm, respectively. The energy band gap of the as- deposited films, the annealed films using FHT and HAT were 3.24 eV, 3.1 and 3.19 eV, respectively. Moreover, the annealed films using FHT and HAT techniques show superhydrophilic with a water contact angle of 3.42° and 2.81°, while the as-deposited films was 8.93°. After aging time testing, superhydrophilicity of the annealed films using HAT is greater than FHT. The result is in good agreement with Ti wt% of the as-deposited, the annealed films using FHT and HAT left on the substrate to 0.15, 0.73 and 0.65 nm after testing simulation for 20 years.

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A lanthanide-rich kilonova in the aftermath of a long gamma-ray burst

arXiv

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arXiv 2023年

作者： Yang, Yu-Han Troja, Eleonora O’Connor, Brendan Fryer, Chris L. Im, Myungshin Durbak, Joe Paek, Gregory S.H. Ricci, Roberto De Bom, Clécio R. Gillanders, James H. Castro-Tirado, Alberto J. Peng, Zong-Kai Dichiara, Simone Ryan, Geoffrey van Eerten, Hendrik Dai, Zi-Gao Chang, Seo-Won Choi, Hyeonho De, Kishalay Hu, Youdong Kilpatrick, Charles D. Kutyrev, Alexander Jeong, Mankeun Lee, Chung-Uk Makler, Martin Navarete, Felipe Pérez-García, Ignacio Department of Physics University of Rome "Tor Vergata" via della Ricerca Scientifica 1 RomeI-00133 Italy INAF - Istituto Nazionale di Astrofisica Rome00133 Italy Department of Physics The George Washington University 725 21st Street NW WashingtonDC20052 United States Department of Astronomy University of Maryland College ParkMD20742-4111 United States Astrophysics Science Division NASA Goddard Space Flight Center 8800 Greenbelt Rd GreenbeltMD20771 United States Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States The University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The University of New Mexico AlbuquerqueNM87131 United States The George Washington University WashingtonDC20052 United States SNU Astronomy Research Center Astronomy Program Dept. of Physics & Astronomy Seoul National University Seoul08826 Korea Republic of Istituto Nazionale di Ricerca Metrologica TorinoI-10135 Italy INAF - Istituto di Radioastronomia via Gobetti 101 BolognaI-40129 Italy Centro Brasileiro de Pesquisas Físicas Rua Dr. Xavier Sigaud 150 RJ Rio de JaneiroCEP 22290-180 Brazil Centro Federal de Educação Tecnológica Celso Suckow da Fonseca Rodovia Mário Covas lote J2 quadra J RJ ItaguaíCEP 23810-000 Brazil Glorieta de la Astronomía s/n Granada18080 Spain Unidad Asociada al CSIC Departamento de Ingeniería de Sistemas y Automática Escuela de Ingenierías Industriales Arquitecto Francisco Peñalosa 6 Málaga29071 Spain Institute for Frontier in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China Department of Astronomy and Astrophysics The Pennsylvania State University 525 Davey Lab University ParkPA16802 United States Perimeter Institute for Theoretical Phy

Kilonovae are a rare class of astrophysical transients powered by the radioactive decay of nuclei heavier than iron, synthesized in the merger of two compact objects 1–4. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate2, 5. On timescales of weeks to months, its behavior is predicted to differ depending on the ejecta composition and merger remnant6–9. However, late-time observations of known kilonovae are either missing or limited10, 11. Here we report observations of a luminous red transient with a quasi-thermal spectrum, following an unusual gamma-ray burst of long duration. We classify this thermal emission as a kilonova and track its evolution up to two months after the burst. At these late times, the recession of the photospheric radius and the rapidly-decaying bolometric luminosity (Lbol ∝ t−2.7±0.4) support the recombination of lanthanide-rich ejecta as they cool. Copyright © 2023, The Authors. All rights reserved.

关键词： Rare earth elements

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Author Correction: Supply chains create global benefits from improved vaccine accessibility

引用

Nature communications 2023年第1期14卷 5462页

作者： Daoping Wang Ottar N Bjørnstad Tianyang Lei Yida Sun Jingwen Huo Qi Hao Zhao Zeng Shupeng Zhu Stéphane Hallegatte Ruiyun Li Dabo Guan Nils C Stenseth Department of Computer Science and Technology University of Cambridge Cambridge UK. The World Economic Forum Geneva Switzerland. Center for Infectious Disease Dynamics Department of Entomology Pennsylvania State University State College PA USA. Department of Earth System Science Tsinghua University Beijing China. College of Management and Economics Tianjin University Tianjin China. Advanced Power and Energy Program University of California Irvine Irvine CA USA. The World Bank Washington DC USA. Centre for Ecological and Evolutionary Synthesis Department of Biosciences Faculty of Mathematics and Natural Sciences University of Oslo Oslo Norway. Department of Earth System Science Tsinghua University Beijing China. guandabo@***. The Bartlett School of Sustainable Construction University College London London UK. guandabo@***. Centre for Ecological and Evolutionary Synthesis Department of Biosciences Faculty of Mathematics and Natural Sciences University of Oslo Oslo Norway. n.c.stenseth@mn.uio.no. Centre for Pandemics and One Health Research Faculty of Medicine University of Oslo Oslo Norway. n.c.stenseth@mn.uio.no.

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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A Basis Set of Elementary Operations Captures Recombination of Neocortical Cell Assemblies during Basal Conditions and Learning

SSRN

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SSRN 2019年

作者： Aschauer, Dominik F. Eppler, Jens-Bastian Ewig, Luke Chambers, Anna Pokorny, Christoph Kaschube, Matthias Rumpel, Simon Institute of Physiology Focus Program Translational Neurosciences University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Frankfurt Institute of Advanced Studies Goethe University Frankfurt Frankfurt /M. Germany Institute of Basic Medical Sciences Faculty of Medicine University of Oslo Oslo Norway Institute of Theoretical Computer Science Graz University of Technology Graz Austria Department of Basic Neurosciences University of Geneva Geneva Switzerland

Cell assemblies — subgroups within neuronal networks — are believed to serve as functional entities underlying cognitive capabilities such as categorical perception or memory formation and storage. However, little is known about their long-term dynamics. Using chronic in vivo calcium imaging in the mouse auditory cortex, we find that cell assemblies undergo continuous recombination, even under behaviorally stable conditions. We identify a basis set of elementary operations capturing the dynamics of cell assemblies, which involve plasticity of both the stimulus tuning of particular assemblies as well as the cellular composition of an assembly. Auditory fear conditioning introduces biases in the frequency of specific cell assembly operations that lead to increased association of representations and improved sound encoding. Thus, decomposing cell assembly dynamics during basal conditions into a basis set of elementary operations provides a general framework to explain learning-induced changes in global population activity. © 2019, The Authors. All rights reserved.

关键词： Neurons

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Effect of oxygen plasma treatment on ITO nanorods films prepared by glancing angle deposition

引用

AIP Conference Proceedings 2020年第1期2279卷

作者： P. Nuchuay W. Promcham C. Laongwan T. Chaikeeree S. Limwichean P. Eiamchai V. Pattantsetakul N. Nuntawong C. Chananonnawathorn C. Oros P. Songsirirtthigul M. Horprathum Program of Industrial Electrical Technology Faculty of Science and Technology Suratthani Rajabhat University Muang Surat Thani 841000 Thailand School of Energy Environment and Materials King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand National Electronics and Computer Technology Center Pathum Thani 12120 Thailand Faculty of Liberal-arts Rajamangala University of Technology Rattanakosin Nakornpathom 73170 Thailand Research Network NANOTECH-SUT on Advanced Nanomaterials and Characterization School of Physics Suranaree University of Technology Nakhon Ratchasima 30000 Thailand

The effect of oxygen (O2) plasma treatment on indium doped tin oxide (ITO) nanorods films prepared by the ion-assisted electron-beam evaporation with the glancing-angle deposition technique (GLAD) were investigated. The ITO nanorods films were plasma treated in oxygen ambient at different O2 flow rates in the range of 20-100 sccm. The physical microstructures have been investigated by grazing-incident X-ray diffraction (GIXRD) and field-emission scanning electron microscopy (FE-SEM). The electrical and optical properties were characterized by four-point probe measurements and UV-VIS-NIR spectrophotometry, respectively. The results showed that all the ITO nanorods films exhibited as bixbite structure. The morphology of as prepared films showed vertically aligned nanocolumnar with diameter of 50-63 nm cause of the shadowing effect and limited adatom diffusion. In addition, the influence of O2 plasma treatment on electrical and anti-reflection with omnidirectional property of ITO nanorods films were systematically evaluated and discussed in this paper.

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[email protected] core-shell nanoparticles as a p-type gas sensor: Novel synthesis, characterization, and their gas sensing properties with sensing mechanism

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Sensors and Actuators B: Chemical 2018年 268卷 223-231页

作者： Sanjit Manohar Majhi Gautam Kumar Naik Hu-Jun Lee Ho-Geun Song Cheul-Ro Lee In-Hwan Lee Yeon-Tae Yu Division of Advanced Materials Engineering and Research Center for Advanced Materials Development College of Engineering Chonbuk National University Jeonju 561-756 South Korea Electrical Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Ogam Technology Jeonju 54882 South Korea Department of Materials Science and Engineering Korea University Seoul 02841 South Korea

In this work, [email protected] core-shell nanoparticles (C-S NPs) as a p-type gas sensing material was synthesized by a facile wet-chemical method, and evaluated their gas sensing properties as compared to the pristine NiO NPs gas sensors. Transmission electron microscope (TEM) results exhibited the well-dispersed formation of [email protected] C-S NPs having the total size of 70–120 nm and NiO shells having 30–50 nm thickness. The C-S morphology as well as the overall particle sizes are unchanged even at 500 °C. The gas sensing result reveals that the response of [email protected] C-S NPs gas sensor is higher than pristine NiO NPs gas sensor for 100 ppm of ethanol at 200 °C operating temperature. The baseline resistance in the air for [email protected] C-S NPs sensor is lowered as compared to pristine NiO NPs, which is due to the increased number of holes as charge carriers in [email protected] C-S NPs. The high response of [email protected] core-shell NPs as compared to pristine NiO NPs is attributed to electronic and chemical sensitization effects of Au. In [email protected] C-S structure, the contact between metal (Au) and semiconductor (NiO) formed a Schottky junction since Au metal acted as electron acceptor, a withdrawal of electrons from NiO by Au metal core leaved behind number of holes as charge carriers in [email protected] C-S NPs. Therefore, the baseline resistance of [email protected] C-S NPs greatly decreased than pristine NiO NPs, as a result the [email protected] C-S NPs showed higher response. On the other hand, in chemical sensitization effect, Au NPs catalyzed to dissociate O 2 molecules into ionic species. This work will give some clue to the researchers for the further development of p-type based C-S NPs sensors.

关键词： [email protected] Core-shell NPs Baseline resistance Sensitivity [email protected]

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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