Improving the empirical model for plasma nitrided AISI 316L corrosion resistance based on Mössbauer spectroscopy View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2011-11

AUTHORS

M. Campos, S. D. de Souza, S. de Souza, M. Olzon-Dionysio

ABSTRACT

Traditional plasma nitriding treatments using temperatures ranging from approximately 650 to 730 K can improve wear, corrosion resistance and surface hardness on stainless steels. The nitrided layer consists of some iron nitrides: the cubic γ′ phase (Fe4N), the hexagonal phase ε (Fe2 − 3N) and a nitrogen supersatured solid phase γN. An empirical model is proposed to explain the corrosion resistance of AISI 316L and ASTM F138 nitrided samples based on Mössbauer Spectroscopy results: the larger the ratio between ε and γ′ phase fractions of the sample, the better its resistance corrosion is. In this work, this model is examined using some new results of AISI 316L samples, nitrided under the same previous conditions of gas composition and temperature, but at different pressure, for 3, 4 and 5 h. The sample nitrided for 4 h, whose value for ε/γ′ is maximum (= 0.73), shows a slightly better response than the other two samples, nitrided for 5 and 3 h (ε/γ′ = 0.72 and 0.59, respectively). Moreover, these samples show very similar behavior. Therefore, this set of samples was not suitable to test the empirical model. However, the comparison between the present results of potentiodynamic polarization curves and those obtained previously at 4 and 4.5 torr, could indicated that the corrosion resistance of the sample which only presents the γN phase was the worst of them. Moreover, the empirical model seems not to be ready to explain the response to corrosion and it should be improved including the γN phase. More... »

PAGES

105-112

References to SciGraph publications

  • 1982-06. The influence of carbon on nitrogen substitution in iron ε -phases in JOURNAL OF MATERIALS SCIENCE
  • 1977-03. Mössbauer spectroscopy of hexagonal iron-nitrogen alloys in METALLURGICAL TRANSACTIONS A
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10751-011-0351-3

    DOI

    http://dx.doi.org/10.1007/s10751-011-0351-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1015067962


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0912", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Materials Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Federal University of S\u00e3o Carlos", 
              "id": "https://www.grid.ac/institutes/grid.411247.5", 
              "name": [
                "Departamento de F\u00edsica, Universidade Federal de S\u00e3o Carlos, 13565-950, S\u00e3o Carlos, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Campos", 
            "givenName": "M.", 
            "id": "sg:person.015010561521.07", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015010561521.07"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Federal University of S\u00e3o Carlos", 
              "id": "https://www.grid.ac/institutes/grid.411247.5", 
              "name": [
                "Departamento de F\u00edsica, Universidade Federal de S\u00e3o Carlos, 13565-950, S\u00e3o Carlos, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "de Souza", 
            "givenName": "S. D.", 
            "id": "sg:person.07371642111.97", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07371642111.97"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Instituto de Pesquisas Energ\u00e9ticas e Nucleares", 
              "id": "https://www.grid.ac/institutes/grid.466806.a", 
              "name": [
                "Centro de Ci\u00eancia e Tecnologia de Materiais, Instituto de Pesquisas Energ\u00e9ticas e Nucleares, 05508-000, S\u00e3o Paulo, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "de Souza", 
            "givenName": "S.", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Federal University of S\u00e3o Carlos", 
              "id": "https://www.grid.ac/institutes/grid.411247.5", 
              "name": [
                "Departamento de F\u00edsica, Universidade Federal de S\u00e3o Carlos, 13565-950, S\u00e3o Carlos, SP, Brazil"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Olzon-Dionysio", 
            "givenName": "M.", 
            "id": "sg:person.011267130362.18", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011267130362.18"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.surfcoat.2010.04.034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000370156"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00540806", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001915239", 
              "https://doi.org/10.1007/bf00540806"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0257-8972(00)00836-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008359318"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02661752", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016133843", 
              "https://doi.org/10.1007/bf02661752"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02661752", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016133843", 
              "https://doi.org/10.1007/bf02661752"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.matchar.2010.06.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020849491"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0038-1098(69)90129-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023809251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0038-1098(69)90129-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023809251"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0304-8853(92)91062-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032328022"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0304-8853(92)91062-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032328022"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.msea.2006.06.023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035061082"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.surfcoat.2007.12.040", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047967106"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0257-8972(02)00185-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048410945"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0304-8853(01)00426-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052365965"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.corsci.2005.06.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052617799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1063/1.358561", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1057979598"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1143/jpsj.33.62", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1063099781"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2011-11", 
        "datePublishedReg": "2011-11-01", 
        "description": "Traditional plasma nitriding treatments using temperatures ranging from approximately 650 to 730 K can improve wear, corrosion resistance and surface hardness on stainless steels. The nitrided layer consists of some iron nitrides: the cubic \u03b3\u2032 phase (Fe4N), the hexagonal phase \u03b5 (Fe2 \u2212 3N) and a nitrogen supersatured solid phase \u03b3N. An empirical model is proposed to explain the corrosion resistance of AISI 316L and ASTM F138 nitrided samples based on M\u00f6ssbauer Spectroscopy results: the larger the ratio between \u03b5 and \u03b3\u2032 phase fractions of the sample, the better its resistance corrosion is. In this work, this model is examined using some new results of AISI 316L samples, nitrided under the same previous conditions of gas composition and temperature, but at different pressure, for 3, 4 and 5 h. The sample nitrided for 4 h, whose value for \u03b5/\u03b3\u2032 is maximum (= 0.73), shows a slightly better response than the other two samples, nitrided for 5 and 3 h (\u03b5/\u03b3\u2032 = 0.72 and 0.59, respectively). Moreover, these samples show very similar behavior. Therefore, this set of samples was not suitable to test the empirical model. However, the comparison between the present results of potentiodynamic polarization curves and those obtained previously at 4 and 4.5 torr, could indicated that the corrosion resistance of the sample which only presents the \u03b3N phase was the worst of them. Moreover, the empirical model seems not to be ready to explain the response to corrosion and it should be improved including the \u03b3N phase.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10751-011-0351-3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1038685", 
            "issn": [
              "0304-3843", 
              "1572-9540"
            ], 
            "name": "Hyperfine Interactions", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1-3", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "203"
          }
        ], 
        "name": "Improving the empirical model for plasma nitrided AISI 316L corrosion resistance based on M\u00f6ssbauer spectroscopy", 
        "pagination": "105-112", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "47c9ce2673d694aa9c287c7a84497e1de3a624fe498cbdf96ca75f2ac8f44aa7"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10751-011-0351-3"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1015067962"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10751-011-0351-3", 
          "https://app.dimensions.ai/details/publication/pub.1015067962"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T19:57", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8681_00000511.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs10751-011-0351-3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10751-011-0351-3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10751-011-0351-3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10751-011-0351-3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10751-011-0351-3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    128 TRIPLES      21 PREDICATES      41 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10751-011-0351-3 schema:about anzsrc-for:09
    2 anzsrc-for:0912
    3 schema:author Nadb880783b6b4c43bfb2ed0e80b22c10
    4 schema:citation sg:pub.10.1007/bf00540806
    5 sg:pub.10.1007/bf02661752
    6 https://doi.org/10.1016/0038-1098(69)90129-x
    7 https://doi.org/10.1016/0304-8853(92)91062-x
    8 https://doi.org/10.1016/j.corsci.2005.06.006
    9 https://doi.org/10.1016/j.matchar.2010.06.015
    10 https://doi.org/10.1016/j.msea.2006.06.023
    11 https://doi.org/10.1016/j.surfcoat.2007.12.040
    12 https://doi.org/10.1016/j.surfcoat.2010.04.034
    13 https://doi.org/10.1016/s0257-8972(00)00836-7
    14 https://doi.org/10.1016/s0257-8972(02)00185-8
    15 https://doi.org/10.1016/s0304-8853(01)00426-7
    16 https://doi.org/10.1063/1.358561
    17 https://doi.org/10.1143/jpsj.33.62
    18 schema:datePublished 2011-11
    19 schema:datePublishedReg 2011-11-01
    20 schema:description Traditional plasma nitriding treatments using temperatures ranging from approximately 650 to 730 K can improve wear, corrosion resistance and surface hardness on stainless steels. The nitrided layer consists of some iron nitrides: the cubic γ′ phase (Fe4N), the hexagonal phase ε (Fe2 − 3N) and a nitrogen supersatured solid phase γN. An empirical model is proposed to explain the corrosion resistance of AISI 316L and ASTM F138 nitrided samples based on Mössbauer Spectroscopy results: the larger the ratio between ε and γ′ phase fractions of the sample, the better its resistance corrosion is. In this work, this model is examined using some new results of AISI 316L samples, nitrided under the same previous conditions of gas composition and temperature, but at different pressure, for 3, 4 and 5 h. The sample nitrided for 4 h, whose value for ε/γ′ is maximum (= 0.73), shows a slightly better response than the other two samples, nitrided for 5 and 3 h (ε/γ′ = 0.72 and 0.59, respectively). Moreover, these samples show very similar behavior. Therefore, this set of samples was not suitable to test the empirical model. However, the comparison between the present results of potentiodynamic polarization curves and those obtained previously at 4 and 4.5 torr, could indicated that the corrosion resistance of the sample which only presents the γN phase was the worst of them. Moreover, the empirical model seems not to be ready to explain the response to corrosion and it should be improved including the γN phase.
    21 schema:genre research_article
    22 schema:inLanguage en
    23 schema:isAccessibleForFree false
    24 schema:isPartOf N583def1c4c234011ba9c2aefb6bad541
    25 Ndec6dc610e3d4193be1848a0c647e1f7
    26 sg:journal.1038685
    27 schema:name Improving the empirical model for plasma nitrided AISI 316L corrosion resistance based on Mössbauer spectroscopy
    28 schema:pagination 105-112
    29 schema:productId N1e76d9f4842849a0b39e3eb57daac71f
    30 N508facabf54a4e29adbcc80036688071
    31 Na36e6732d3944c238599921bb5c98bcb
    32 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015067962
    33 https://doi.org/10.1007/s10751-011-0351-3
    34 schema:sdDatePublished 2019-04-10T19:57
    35 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    36 schema:sdPublisher Neb14b7abc59b43428c2960a1f84cb4c2
    37 schema:url http://link.springer.com/10.1007%2Fs10751-011-0351-3
    38 sgo:license sg:explorer/license/
    39 sgo:sdDataset articles
    40 rdf:type schema:ScholarlyArticle
    41 N1423ee61639e431cbb1748b86097ca93 rdf:first sg:person.07371642111.97
    42 rdf:rest N1cee61825f9648e2afbb2c4a35ccd456
    43 N1cee61825f9648e2afbb2c4a35ccd456 rdf:first Ne04f85dfcd1040e2ab0972805349b6f0
    44 rdf:rest N96e6cf5444a44a24b5cf02490edd8cd6
    45 N1e76d9f4842849a0b39e3eb57daac71f schema:name readcube_id
    46 schema:value 47c9ce2673d694aa9c287c7a84497e1de3a624fe498cbdf96ca75f2ac8f44aa7
    47 rdf:type schema:PropertyValue
    48 N508facabf54a4e29adbcc80036688071 schema:name dimensions_id
    49 schema:value pub.1015067962
    50 rdf:type schema:PropertyValue
    51 N583def1c4c234011ba9c2aefb6bad541 schema:issueNumber 1-3
    52 rdf:type schema:PublicationIssue
    53 N96e6cf5444a44a24b5cf02490edd8cd6 rdf:first sg:person.011267130362.18
    54 rdf:rest rdf:nil
    55 Na36e6732d3944c238599921bb5c98bcb schema:name doi
    56 schema:value 10.1007/s10751-011-0351-3
    57 rdf:type schema:PropertyValue
    58 Nadb880783b6b4c43bfb2ed0e80b22c10 rdf:first sg:person.015010561521.07
    59 rdf:rest N1423ee61639e431cbb1748b86097ca93
    60 Ndec6dc610e3d4193be1848a0c647e1f7 schema:volumeNumber 203
    61 rdf:type schema:PublicationVolume
    62 Ne04f85dfcd1040e2ab0972805349b6f0 schema:affiliation https://www.grid.ac/institutes/grid.466806.a
    63 schema:familyName de Souza
    64 schema:givenName S.
    65 rdf:type schema:Person
    66 Neb14b7abc59b43428c2960a1f84cb4c2 schema:name Springer Nature - SN SciGraph project
    67 rdf:type schema:Organization
    68 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    69 schema:name Engineering
    70 rdf:type schema:DefinedTerm
    71 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
    72 schema:name Materials Engineering
    73 rdf:type schema:DefinedTerm
    74 sg:journal.1038685 schema:issn 0304-3843
    75 1572-9540
    76 schema:name Hyperfine Interactions
    77 rdf:type schema:Periodical
    78 sg:person.011267130362.18 schema:affiliation https://www.grid.ac/institutes/grid.411247.5
    79 schema:familyName Olzon-Dionysio
    80 schema:givenName M.
    81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011267130362.18
    82 rdf:type schema:Person
    83 sg:person.015010561521.07 schema:affiliation https://www.grid.ac/institutes/grid.411247.5
    84 schema:familyName Campos
    85 schema:givenName M.
    86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015010561521.07
    87 rdf:type schema:Person
    88 sg:person.07371642111.97 schema:affiliation https://www.grid.ac/institutes/grid.411247.5
    89 schema:familyName de Souza
    90 schema:givenName S. D.
    91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07371642111.97
    92 rdf:type schema:Person
    93 sg:pub.10.1007/bf00540806 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001915239
    94 https://doi.org/10.1007/bf00540806
    95 rdf:type schema:CreativeWork
    96 sg:pub.10.1007/bf02661752 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016133843
    97 https://doi.org/10.1007/bf02661752
    98 rdf:type schema:CreativeWork
    99 https://doi.org/10.1016/0038-1098(69)90129-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1023809251
    100 rdf:type schema:CreativeWork
    101 https://doi.org/10.1016/0304-8853(92)91062-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1032328022
    102 rdf:type schema:CreativeWork
    103 https://doi.org/10.1016/j.corsci.2005.06.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052617799
    104 rdf:type schema:CreativeWork
    105 https://doi.org/10.1016/j.matchar.2010.06.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020849491
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1016/j.msea.2006.06.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035061082
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1016/j.surfcoat.2007.12.040 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047967106
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1016/j.surfcoat.2010.04.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000370156
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1016/s0257-8972(00)00836-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008359318
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1016/s0257-8972(02)00185-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048410945
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1016/s0304-8853(01)00426-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052365965
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1063/1.358561 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057979598
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1143/jpsj.33.62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063099781
    122 rdf:type schema:CreativeWork
    123 https://www.grid.ac/institutes/grid.411247.5 schema:alternateName Federal University of São Carlos
    124 schema:name Departamento de Física, Universidade Federal de São Carlos, 13565-950, São Carlos, SP, Brazil
    125 rdf:type schema:Organization
    126 https://www.grid.ac/institutes/grid.466806.a schema:alternateName Instituto de Pesquisas Energéticas e Nucleares
    127 schema:name Centro de Ciência e Tecnologia de Materiais, Instituto de Pesquisas Energéticas e Nucleares, 05508-000, São Paulo, SP, Brazil
    128 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...