Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.
Identifieur interne : 000004 ( Main/Exploration ); précédent : 000003; suivant : 000005Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.
Auteurs : Hye Ryoung Lee [États-Unis] ; Lei Liao [États-Unis] ; Wang Xiao [États-Unis] ; Arturas Vailionis [États-Unis, Lituanie] ; Antonio J. Ricco [États-Unis] ; Robin White [États-Unis] ; Yoshio Nishi [États-Unis] ; Wah Chiu [États-Unis] ; Steven Chu [États-Unis] ; Yi Cui [États-Unis]Source :
- Nano letters [ 1530-6992 ] ; 2021.
Descripteurs français
- KwdFr :
- Apprentissage profond (MeSH), Filtration (statistiques et données numériques), Humains (MeSH), Imagerie tridimensionnelle (MeSH), Microbiologie de l'air (MeSH), Microscopie électronique à balayage (MeSH), Nanoparticules (ultrastructure), Pandémies (prévention et contrôle), Polypropylènes (MeSH), Porosité (MeSH), Taille de particule (MeSH), Textiles (virologie), Tomographie à rayons X (MeSH).
- MESH :
- prévention et contrôle : Pandémies.
- statistiques et données numériques : Filtration.
- virologie : Textiles.
- Apprentissage profond, Humains, Imagerie tridimensionnelle, Microbiologie de l'air, Microscopie électronique à balayage, Nanoparticules, Polypropylènes, Porosité, Taille de particule, Tomographie à rayons X.
English descriptors
- KwdEn :
- Air Microbiology (MeSH), COVID-19 (prevention & control), COVID-19 (transmission), COVID-19 (virology), Deep Learning (MeSH), Filtration (statistics & numerical data), Humans (MeSH), Imaging, Three-Dimensional (MeSH), Microscopy, Electron, Scanning (MeSH), N95 Respirators (standards), N95 Respirators (statistics & numerical data), N95 Respirators (virology), Nanoparticles (ultrastructure), Pandemics (prevention & control), Particle Size (MeSH), Polypropylenes (MeSH), Porosity (MeSH), SARS-CoV-2 (ultrastructure), Textiles (virology), Tomography, X-Ray (MeSH).
- MESH :
- chemical : Polypropylenes.
- prevention & control : COVID-19, Pandemics.
- standards : N95 Respirators.
- statistics & numerical data : Filtration, N95 Respirators.
- transmission : COVID-19.
- ultrastructure : Nanoparticles, SARS-CoV-2.
- virology : COVID-19, N95 Respirators, Textiles.
- Air Microbiology, Deep Learning, Humans, Imaging, Three-Dimensional, Microscopy, Electron, Scanning, Particle Size, Porosity, Tomography, X-Ray.
Abstract
The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.
DOI: 10.1021/acs.nanolett.0c04230
PubMed: 33283521
PubMed Central: PMC7737533
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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wicri:level="4"><nlm:affiliation>Geballe Laboratory for Advanced Materials, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Geballe Laboratory for Advanced Materials, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025</wicri:regionArea><wicri:noRegion>California 94025</wicri:noRegion></affiliation></author><author><name sortKey="Liao, Lei" sort="Liao, Lei" uniqKey="Liao L" first="Lei" last="Liao">Lei Liao</name><affiliation wicri:level="1"><nlm:affiliation>4C Air, Inc., Sunnyvale, California 94089, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>4C Air, Inc., Sunnyvale, California 94089</wicri:regionArea><wicri:noRegion>California 94089</wicri:noRegion></affiliation></author><author><name sortKey="Xiao, Wang" sort="Xiao, Wang" uniqKey="Xiao W" first="Wang" last="Xiao">Wang Xiao</name><affiliation wicri:level="1"><nlm:affiliation>4C Air, Inc., Sunnyvale, California 94089, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>4C Air, Inc., Sunnyvale, California 94089</wicri:regionArea><wicri:noRegion>California 94089</wicri:noRegion></affiliation></author><author><name sortKey="Vailionis, Arturas" sort="Vailionis, Arturas" uniqKey="Vailionis A" first="Arturas" last="Vailionis">Arturas Vailionis</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Nano Shared Facility, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Nano Shared Facility, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Department of Physics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.</nlm:affiliation><country xml:lang="fr">Lituanie</country><wicri:regionArea>Department of Physics, Kaunas University of Technology, LT-51368 Kaunas</wicri:regionArea><wicri:noRegion>LT-51368 Kaunas</wicri:noRegion></affiliation></author><author><name sortKey="Ricco, Antonio J" sort="Ricco, Antonio J" uniqKey="Ricco A" first="Antonio J" last="Ricco">Antonio J. Ricco</name><affiliation wicri:level="4"><nlm:affiliation>Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="White, Robin" sort="White, Robin" uniqKey="White R" first="Robin" last="White">Robin White</name><affiliation wicri:level="1"><nlm:affiliation>Carl Zeiss X-ray Microscopy, Inc., Pleasanton, California 94588, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Carl Zeiss X-ray Microscopy, Inc., Pleasanton, California 94588</wicri:regionArea><wicri:noRegion>California 94588</wicri:noRegion></affiliation></author><author><name sortKey="Nishi, Yoshio" sort="Nishi, Yoshio" uniqKey="Nishi Y" first="Yoshio" last="Nishi">Yoshio Nishi</name><affiliation wicri:level="4"><nlm:affiliation>Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Electrical Engineering, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Chiu, Wah" sort="Chiu, Wah" uniqKey="Chiu W" first="Wah" last="Chiu">Wah Chiu</name><affiliation wicri:level="4"><nlm:affiliation>Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025</wicri:regionArea><wicri:noRegion>California 94025</wicri:noRegion></affiliation></author><author><name sortKey="Chu, Steven" sort="Chu, Steven" uniqKey="Chu S" first="Steven" last="Chu">Steven Chu</name><affiliation wicri:level="4"><nlm:affiliation>Department of Physics, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Physics, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Molecular and Cellular Physiology, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Molecular and Cellular Physiology, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Cui, Yi" sort="Cui, Yi" uniqKey="Cui Y" first="Yi" last="Cui">Yi Cui</name><affiliation wicri:level="1"><nlm:affiliation>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025</wicri:regionArea><wicri:noRegion>California 94025</wicri:noRegion></affiliation><affiliation wicri:level="4"><nlm:affiliation>Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Materials Science and Engineering, Stanford University, Stanford, California 94305</wicri:regionArea><orgName type="university">Université Stanford</orgName><placeName><settlement type="city">Stanford (Californie)</settlement><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Nano letters</title><idno type="eISSN">1530-6992</idno><imprint><date when="2021" type="published">2021</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Air Microbiology (MeSH)</term><term>COVID-19 (prevention & control)</term><term>COVID-19 (transmission)</term><term>COVID-19 (virology)</term><term>Deep Learning (MeSH)</term><term>Filtration (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Imaging, Three-Dimensional (MeSH)</term><term>Microscopy, Electron, Scanning (MeSH)</term><term>N95 Respirators (standards)</term><term>N95 Respirators (statistics & numerical data)</term><term>N95 Respirators (virology)</term><term>Nanoparticles (ultrastructure)</term><term>Pandemics (prevention & control)</term><term>Particle Size (MeSH)</term><term>Polypropylenes (MeSH)</term><term>Porosity (MeSH)</term><term>SARS-CoV-2 (ultrastructure)</term><term>Textiles (virology)</term><term>Tomography, X-Ray (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Apprentissage profond (MeSH)</term><term>Filtration (statistiques et données numériques)</term><term>Humains (MeSH)</term><term>Imagerie tridimensionnelle (MeSH)</term><term>Microbiologie de l'air (MeSH)</term><term>Microscopie électronique à balayage (MeSH)</term><term>Nanoparticules (ultrastructure)</term><term>Pandémies (prévention et contrôle)</term><term>Polypropylènes (MeSH)</term><term>Porosité (MeSH)</term><term>Taille de particule (MeSH)</term><term>Textiles (virologie)</term><term>Tomographie à rayons X (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Polypropylenes</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>COVID-19</term><term>Pandemics</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Pandémies</term></keywords><keywords scheme="MESH" qualifier="standards" xml:lang="en"><term>N95 Respirators</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Filtration</term><term>N95 Respirators</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Filtration</term></keywords><keywords scheme="MESH" qualifier="transmission" xml:lang="en"><term>COVID-19</term></keywords><keywords scheme="MESH" qualifier="ultrastructure" xml:lang="en"><term>Nanoparticles</term><term>SARS-CoV-2</term></keywords><keywords scheme="MESH" qualifier="virologie" xml:lang="fr"><term>Textiles</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>COVID-19</term><term>N95 Respirators</term><term>Textiles</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Air Microbiology</term><term>Deep Learning</term><term>Humans</term><term>Imaging, Three-Dimensional</term><term>Microscopy, Electron, Scanning</term><term>Particle Size</term><term>Porosity</term><term>Tomography, X-Ray</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Apprentissage profond</term><term>Humains</term><term>Imagerie tridimensionnelle</term><term>Microbiologie de l'air</term><term>Microscopie électronique à balayage</term><term>Nanoparticules</term><term>Polypropylènes</term><term>Porosité</term><term>Taille de particule</term><term>Tomographie à rayons X</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">33283521</PMID><DateCompleted><Year>2021</Year><Month>01</Month><Day>22</Day></DateCompleted><DateRevised><Year>2021</Year><Month>01</Month><Day>22</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1530-6992</ISSN><JournalIssue CitedMedium="Internet"><Volume>21</Volume><Issue>1</Issue><PubDate><Year>2021</Year><Month>01</Month><Day>13</Day></PubDate></JournalIssue><Title>Nano letters</Title><ISOAbbreviation>Nano Lett</ISOAbbreviation></Journal><ArticleTitle>Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning.</ArticleTitle><Pagination><MedlinePgn>651-657</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1021/acs.nanolett.0c04230</ELocationID><Abstract><AbstractText>The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lee</LastName><ForeName>Hye Ryoung</ForeName><Initials>HR</Initials><Identifier Source="ORCID">0000-0003-4546-4880</Identifier><AffiliationInfo><Affiliation>Geballe Laboratory for Advanced Materials, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Liao</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>4C Air, Inc., Sunnyvale, California 94089, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>Wang</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>4C Air, Inc., Sunnyvale, California 94089, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Vailionis</LastName><ForeName>Arturas</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Stanford Nano Shared Facility, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Physics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ricco</LastName><ForeName>Antonio J</ForeName><Initials>AJ</Initials><AffiliationInfo><Affiliation>Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>White</LastName><ForeName>Robin</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Carl Zeiss X-ray Microscopy, Inc., Pleasanton, California 94588, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Nishi</LastName><ForeName>Yoshio</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chiu</LastName><ForeName>Wah</ForeName><Initials>W</Initials><Identifier Source="ORCID">0000-0002-8910-3078</Identifier><AffiliationInfo><Affiliation>Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chu</LastName><ForeName>Steven</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Physics, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Molecular and Cellular Physiology, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cui</LastName><ForeName>Yi</ForeName><Initials>Y</Initials><Identifier Source="ORCID">0000-0002-6103-6352</Identifier><AffiliationInfo><Affiliation>Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>ECCS-1542152</GrantID><Agency>National Science Foundation</Agency><Country>International</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>12</Month><Day>07</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Nano Lett</MedlineTA><NlmUniqueID>101088070</NlmUniqueID><ISSNLinking>1530-6984</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D011126">Polypropylenes</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000391" MajorTopicYN="N">Air Microbiology</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName><QualifierName UI="Q000635" MajorTopicYN="N">transmission</QualifierName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D000077321" MajorTopicYN="N">Deep Learning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005374" MajorTopicYN="N">Filtration</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D021621" MajorTopicYN="N">Imaging, Three-Dimensional</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008855" MajorTopicYN="N">Microscopy, Electron, Scanning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000087162" MajorTopicYN="N">N95 Respirators</DescriptorName><QualifierName UI="Q000592" MajorTopicYN="N">standards</QualifierName><QualifierName UI="Q000706" MajorTopicYN="N">statistics & numerical data</QualifierName><QualifierName UI="Q000821" MajorTopicYN="Y">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D053758" MajorTopicYN="N">Nanoparticles</DescriptorName><QualifierName UI="Q000648" MajorTopicYN="N">ultrastructure</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="Y">Pandemics</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010316" MajorTopicYN="N">Particle Size</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011126" MajorTopicYN="N">Polypropylenes</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016062" MajorTopicYN="N">Porosity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName><QualifierName UI="Q000648" MajorTopicYN="Y">ultrastructure</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013784" MajorTopicYN="N">Textiles</DescriptorName><QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014056" MajorTopicYN="N">Tomography, X-Ray</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">N95 respirator</Keyword><Keyword MajorTopicYN="Y">X-ray tomography</Keyword><Keyword MajorTopicYN="Y">deep learning</Keyword><Keyword MajorTopicYN="Y">face mask</Keyword><Keyword MajorTopicYN="Y">particle distribution</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>12</Month><Day>8</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2021</Year><Month>1</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>12</Month><Day>7</Day><Hour>8</Hour><Minute>42</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">33283521</ArticleId><ArticleId IdType="doi">10.1021/acs.nanolett.0c04230</ArticleId><ArticleId IdType="pmc">PMC7737533</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Lituanie</li><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Lee, Hye Ryoung" sort="Lee, Hye Ryoung" uniqKey="Lee H" first="Hye Ryoung" last="Lee">Hye Ryoung Lee</name></region><name sortKey="Chiu, Wah" sort="Chiu, Wah" uniqKey="Chiu W" first="Wah" last="Chiu">Wah Chiu</name><name sortKey="Chiu, Wah" sort="Chiu, Wah" uniqKey="Chiu W" first="Wah" last="Chiu">Wah Chiu</name><name sortKey="Chu, Steven" sort="Chu, Steven" uniqKey="Chu S" first="Steven" last="Chu">Steven Chu</name><name sortKey="Chu, Steven" sort="Chu, Steven" uniqKey="Chu S" first="Steven" last="Chu">Steven Chu</name><name sortKey="Cui, Yi" sort="Cui, Yi" uniqKey="Cui Y" first="Yi" last="Cui">Yi Cui</name><name sortKey="Cui, Yi" sort="Cui, Yi" uniqKey="Cui Y" first="Yi" last="Cui">Yi Cui</name><name sortKey="Lee, Hye Ryoung" sort="Lee, Hye Ryoung" uniqKey="Lee H" first="Hye Ryoung" last="Lee">Hye Ryoung Lee</name><name sortKey="Liao, Lei" sort="Liao, Lei" uniqKey="Liao L" first="Lei" last="Liao">Lei Liao</name><name sortKey="Nishi, Yoshio" sort="Nishi, Yoshio" uniqKey="Nishi Y" first="Yoshio" last="Nishi">Yoshio Nishi</name><name sortKey="Ricco, Antonio J" sort="Ricco, Antonio J" uniqKey="Ricco A" first="Antonio J" last="Ricco">Antonio J. Ricco</name><name sortKey="Vailionis, Arturas" sort="Vailionis, Arturas" uniqKey="Vailionis A" first="Arturas" last="Vailionis">Arturas Vailionis</name><name sortKey="White, Robin" sort="White, Robin" uniqKey="White R" first="Robin" last="White">Robin White</name><name sortKey="Xiao, Wang" sort="Xiao, Wang" uniqKey="Xiao W" first="Wang" last="Xiao">Wang Xiao</name></country><country name="Lituanie"><noRegion><name sortKey="Vailionis, Arturas" sort="Vailionis, Arturas" uniqKey="Vailionis A" first="Arturas" last="Vailionis">Arturas Vailionis</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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