A fast method for the calculation of electron number density and temperature in laser-induced breakdown spectroscopy plasmas using artificial neural ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-10

AUTHORS

Fábio O. Borges, Gildo H. Cavalcanti, Gabriela C. Gomes, Vincenzo Palleschi, Alexandre Mello

ABSTRACT

A fast and precise method for the determination of electron temperature and electron number density in laser-induced plasmas is presented. The method is based on the use of a simple artificial neural network (ANN), trained on a suitable set of laser-induced breakdown spectroscopy spectra. The training procedure is quite fast; once the ANN is set, the determination of plasma temperature and electron number density is almost instantaneous, allowing the possibility of measuring these parameters, with good precision, in real time. A direct application of this new method could be the characterization of plasmas generated during pulsed laser deposition process of thin films and nanoparticles generation. The plasma electronic parameters will help to tune the energies involved in the stoichiometry and crystallization control of those nanostructured materials. As an example, the characteristics of the plasma induced by a Nd:YAG laser on a pure titanium target are determined, at different laser fluences. More... »

PAGES

437-444

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00340-014-5852-8

DOI

http://dx.doi.org/10.1007/s00340-014-5852-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038298959


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/2008/0202", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atomic, Molecular, Nuclear, Particle and Plasma Physics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Fluminense Federal University", 
          "id": "https://www.grid.ac/institutes/grid.411173.1", 
          "name": [
            "Instituto de F\u00edsica, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/n\u00ba, Campus da Praia Vermelha, CEP 24210-346, Niter\u00f3i, Rio de Janeiro, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Borges", 
        "givenName": "F\u00e1bio O.", 
        "id": "sg:person.015542524525.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015542524525.74"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Fluminense Federal University", 
          "id": "https://www.grid.ac/institutes/grid.411173.1", 
          "name": [
            "Instituto de F\u00edsica, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/n\u00ba, Campus da Praia Vermelha, CEP 24210-346, Niter\u00f3i, Rio de Janeiro, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Cavalcanti", 
        "givenName": "Gildo H.", 
        "id": "sg:person.013431655051.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013431655051.27"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centro Brasileiro de Pesquisas F\u00edsicas", 
          "id": "https://www.grid.ac/institutes/grid.418228.5", 
          "name": [
            "Centro Brasileiro de Pesquisas F\u00edsicas, Rua Dr. Xavier Sigaud, 150, CEP: 22290-180, Urca, Rio de Janeiro, RJ, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gomes", 
        "givenName": "Gabriela C.", 
        "id": "sg:person.013750143623.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013750143623.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute for the Chemistry of OrganoMetallic Compounds", 
          "id": "https://www.grid.ac/institutes/grid.473642.0", 
          "name": [
            "Institute of Chemistry of Organometallic Compounds, Research Area of National Research Council, Via G. Moruzzi, 1, 56124, Pisa, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Palleschi", 
        "givenName": "Vincenzo", 
        "id": "sg:person.01251353110.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251353110.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Centro Brasileiro de Pesquisas F\u00edsicas", 
          "id": "https://www.grid.ac/institutes/grid.418228.5", 
          "name": [
            "Centro Brasileiro de Pesquisas F\u00edsicas, Rua Dr. Xavier Sigaud, 150, CEP: 22290-180, Urca, Rio de Janeiro, RJ, Brazil"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mello", 
        "givenName": "Alexandre", 
        "id": "sg:person.0630203063.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0630203063.40"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.sab.2008.06.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007308666"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2009.11.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007413899"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.talanta.2011.01.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010444635"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2007.10.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011277770"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apsusc.2012.11.069", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011989597"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0584-8547(01)00398-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015231998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2006.10.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017076800"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0584-8547(02)00053-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018036854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/323533a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018367015", 
          "https://doi.org/10.1038/323533a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2009.11.006", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026426330"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00216-006-0413-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035156610", 
          "https://doi.org/10.1007/s00216-006-0413-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00216-006-0413-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035156610", 
          "https://doi.org/10.1007/s00216-006-0413-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.neunet.2013.03.015", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035895609"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apsusc.2007.01.113", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041908854"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.talanta.2013.02.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047491555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2005.10.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048609251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2012.11.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050999530"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sab.2010.04.008", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051897098"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003400050596", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052743608", 
          "https://doi.org/10.1007/s003400050596"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp9050947", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056116057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/jp9050947", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056116057"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/10-06079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065265574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/10-06079", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065265574"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/11-06335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065265705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/11-06335", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065265705"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/12-06916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065266004"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1366/12-06916", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065266004"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-10", 
    "datePublishedReg": "2014-10-01", 
    "description": "A fast and precise method for the determination of electron temperature and electron number density in laser-induced plasmas is presented. The method is based on the use of a simple artificial neural network (ANN), trained on a suitable set of laser-induced breakdown spectroscopy spectra. The training procedure is quite fast; once the ANN is set, the determination of plasma temperature and electron number density is almost instantaneous, allowing the possibility of measuring these parameters, with good precision, in real time. A direct application of this new method could be the characterization of plasmas generated during pulsed laser deposition process of thin films and nanoparticles generation. The plasma electronic parameters will help to tune the energies involved in the stoichiometry and crystallization control of those nanostructured materials. As an example, the characteristics of the plasma induced by a Nd:YAG laser on a pure titanium target are determined, at different laser fluences.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00340-014-5852-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1312262", 
        "issn": [
          "0946-2171", 
          "1432-0649"
        ], 
        "name": "Applied Physics B", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "117"
      }
    ], 
    "name": "A fast method for the calculation of electron number density and temperature in laser-induced breakdown spectroscopy plasmas using artificial neural networks", 
    "pagination": "437-444", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b67156e91405c693c1a960ca51a54de48d486e01d2146a2e1deace7d04eb478e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00340-014-5852-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1038298959"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00340-014-5852-8", 
      "https://app.dimensions.ai/details/publication/pub.1038298959"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T14:55", 
    "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_8663_00000490.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s00340-014-5852-8"
  }
]
 

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/s00340-014-5852-8'

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/s00340-014-5852-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00340-014-5852-8'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00340-014-5852-8'


 

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

164 TRIPLES      21 PREDICATES      49 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00340-014-5852-8 schema:about anzsrc-for:02
2 anzsrc-for:0202
3 schema:author Ne5976b6eaf2c4397ab624d72f14bc604
4 schema:citation sg:pub.10.1007/s00216-006-0413-6
5 sg:pub.10.1007/s003400050596
6 sg:pub.10.1038/323533a0
7 https://doi.org/10.1016/j.apsusc.2007.01.113
8 https://doi.org/10.1016/j.apsusc.2012.11.069
9 https://doi.org/10.1016/j.neunet.2013.03.015
10 https://doi.org/10.1016/j.sab.2005.10.011
11 https://doi.org/10.1016/j.sab.2006.10.015
12 https://doi.org/10.1016/j.sab.2007.10.005
13 https://doi.org/10.1016/j.sab.2008.06.010
14 https://doi.org/10.1016/j.sab.2009.11.005
15 https://doi.org/10.1016/j.sab.2009.11.006
16 https://doi.org/10.1016/j.sab.2010.04.008
17 https://doi.org/10.1016/j.sab.2012.11.007
18 https://doi.org/10.1016/j.talanta.2011.01.069
19 https://doi.org/10.1016/j.talanta.2013.02.026
20 https://doi.org/10.1016/s0584-8547(01)00398-6
21 https://doi.org/10.1016/s0584-8547(02)00053-8
22 https://doi.org/10.1021/jp9050947
23 https://doi.org/10.1366/10-06079
24 https://doi.org/10.1366/11-06335
25 https://doi.org/10.1366/12-06916
26 schema:datePublished 2014-10
27 schema:datePublishedReg 2014-10-01
28 schema:description A fast and precise method for the determination of electron temperature and electron number density in laser-induced plasmas is presented. The method is based on the use of a simple artificial neural network (ANN), trained on a suitable set of laser-induced breakdown spectroscopy spectra. The training procedure is quite fast; once the ANN is set, the determination of plasma temperature and electron number density is almost instantaneous, allowing the possibility of measuring these parameters, with good precision, in real time. A direct application of this new method could be the characterization of plasmas generated during pulsed laser deposition process of thin films and nanoparticles generation. The plasma electronic parameters will help to tune the energies involved in the stoichiometry and crystallization control of those nanostructured materials. As an example, the characteristics of the plasma induced by a Nd:YAG laser on a pure titanium target are determined, at different laser fluences.
29 schema:genre research_article
30 schema:inLanguage en
31 schema:isAccessibleForFree false
32 schema:isPartOf N1446100606b342d991f173659e9d7436
33 Nfffca99971074c96bda6ec0f58736863
34 sg:journal.1312262
35 schema:name A fast method for the calculation of electron number density and temperature in laser-induced breakdown spectroscopy plasmas using artificial neural networks
36 schema:pagination 437-444
37 schema:productId N197629f68803428580f59f0e3d5dccdf
38 N83abadb8f00047b693e3953d115a9d59
39 N84c87884fb794529b659d4cc0c5f8710
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038298959
41 https://doi.org/10.1007/s00340-014-5852-8
42 schema:sdDatePublished 2019-04-10T14:55
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher N07f2ca6ea7fa4e86b6ce3078e4cab50f
45 schema:url http://link.springer.com/10.1007/s00340-014-5852-8
46 sgo:license sg:explorer/license/
47 sgo:sdDataset articles
48 rdf:type schema:ScholarlyArticle
49 N07f2ca6ea7fa4e86b6ce3078e4cab50f schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 N1446100606b342d991f173659e9d7436 schema:issueNumber 1
52 rdf:type schema:PublicationIssue
53 N16edcde22b0549c9b9007966aad9e4df rdf:first sg:person.013750143623.33
54 rdf:rest N8de31c0a891d404999f3ef4baf90c48c
55 N1736e6c8aa4e47e183586178afc395d7 rdf:first sg:person.0630203063.40
56 rdf:rest rdf:nil
57 N197629f68803428580f59f0e3d5dccdf schema:name doi
58 schema:value 10.1007/s00340-014-5852-8
59 rdf:type schema:PropertyValue
60 N83abadb8f00047b693e3953d115a9d59 schema:name readcube_id
61 schema:value b67156e91405c693c1a960ca51a54de48d486e01d2146a2e1deace7d04eb478e
62 rdf:type schema:PropertyValue
63 N84c87884fb794529b659d4cc0c5f8710 schema:name dimensions_id
64 schema:value pub.1038298959
65 rdf:type schema:PropertyValue
66 N8de31c0a891d404999f3ef4baf90c48c rdf:first sg:person.01251353110.19
67 rdf:rest N1736e6c8aa4e47e183586178afc395d7
68 Ne5976b6eaf2c4397ab624d72f14bc604 rdf:first sg:person.015542524525.74
69 rdf:rest Nfede6ccb31b14dd2ae682aaec403c170
70 Nfede6ccb31b14dd2ae682aaec403c170 rdf:first sg:person.013431655051.27
71 rdf:rest N16edcde22b0549c9b9007966aad9e4df
72 Nfffca99971074c96bda6ec0f58736863 schema:volumeNumber 117
73 rdf:type schema:PublicationVolume
74 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
75 schema:name Physical Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0202 schema:inDefinedTermSet anzsrc-for:
78 schema:name Atomic, Molecular, Nuclear, Particle and Plasma Physics
79 rdf:type schema:DefinedTerm
80 sg:journal.1312262 schema:issn 0946-2171
81 1432-0649
82 schema:name Applied Physics B
83 rdf:type schema:Periodical
84 sg:person.01251353110.19 schema:affiliation https://www.grid.ac/institutes/grid.473642.0
85 schema:familyName Palleschi
86 schema:givenName Vincenzo
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01251353110.19
88 rdf:type schema:Person
89 sg:person.013431655051.27 schema:affiliation https://www.grid.ac/institutes/grid.411173.1
90 schema:familyName Cavalcanti
91 schema:givenName Gildo H.
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013431655051.27
93 rdf:type schema:Person
94 sg:person.013750143623.33 schema:affiliation https://www.grid.ac/institutes/grid.418228.5
95 schema:familyName Gomes
96 schema:givenName Gabriela C.
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013750143623.33
98 rdf:type schema:Person
99 sg:person.015542524525.74 schema:affiliation https://www.grid.ac/institutes/grid.411173.1
100 schema:familyName Borges
101 schema:givenName Fábio O.
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015542524525.74
103 rdf:type schema:Person
104 sg:person.0630203063.40 schema:affiliation https://www.grid.ac/institutes/grid.418228.5
105 schema:familyName Mello
106 schema:givenName Alexandre
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0630203063.40
108 rdf:type schema:Person
109 sg:pub.10.1007/s00216-006-0413-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035156610
110 https://doi.org/10.1007/s00216-006-0413-6
111 rdf:type schema:CreativeWork
112 sg:pub.10.1007/s003400050596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052743608
113 https://doi.org/10.1007/s003400050596
114 rdf:type schema:CreativeWork
115 sg:pub.10.1038/323533a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018367015
116 https://doi.org/10.1038/323533a0
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1016/j.apsusc.2007.01.113 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041908854
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1016/j.apsusc.2012.11.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011989597
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.neunet.2013.03.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035895609
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.sab.2005.10.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048609251
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.sab.2006.10.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017076800
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.sab.2007.10.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011277770
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1016/j.sab.2008.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007308666
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1016/j.sab.2009.11.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007413899
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1016/j.sab.2009.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026426330
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1016/j.sab.2010.04.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051897098
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1016/j.sab.2012.11.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050999530
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/j.talanta.2011.01.069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010444635
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.talanta.2013.02.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047491555
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1016/s0584-8547(01)00398-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015231998
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1016/s0584-8547(02)00053-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018036854
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1021/jp9050947 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056116057
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1366/10-06079 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065265574
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1366/11-06335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065265705
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1366/12-06916 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065266004
155 rdf:type schema:CreativeWork
156 https://www.grid.ac/institutes/grid.411173.1 schema:alternateName Fluminense Federal University
157 schema:name Instituto de Física, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, s/nº, Campus da Praia Vermelha, CEP 24210-346, Niterói, Rio de Janeiro, Brazil
158 rdf:type schema:Organization
159 https://www.grid.ac/institutes/grid.418228.5 schema:alternateName Centro Brasileiro de Pesquisas Físicas
160 schema:name Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud, 150, CEP: 22290-180, Urca, Rio de Janeiro, RJ, Brazil
161 rdf:type schema:Organization
162 https://www.grid.ac/institutes/grid.473642.0 schema:alternateName Institute for the Chemistry of OrganoMetallic Compounds
163 schema:name Institute of Chemistry of Organometallic Compounds, Research Area of National Research Council, Via G. Moruzzi, 1, 56124, Pisa, Italy
164 rdf:type schema:Organization
 




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


...