Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-27

AUTHORS

Helge S. Stein, Edwin Soedarmadji, Paul F. Newhouse, Dan Guevarra, John M. Gregoire

ABSTRACT

Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties. More... »

PAGES

9

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41597-019-0019-4

DOI

http://dx.doi.org/10.1038/s41597-019-0019-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113004922

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30918263


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": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stein", 
        "givenName": "Helge S.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Soedarmadji", 
        "givenName": "Edwin", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Newhouse", 
        "givenName": "Paul F.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Guevarra", 
        "givenName": "Dan", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "California Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.20861.3d", 
          "name": [
            "Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gregoire", 
        "givenName": "John M.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1039/c6ta01252c", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004948620"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cossms.2016.07.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005331034"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/minf.201400174", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008429557"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/npjcompumats.2015.10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025730500", 
          "https://doi.org/10.1038/npjcompumats.2015.10"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4812323", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027518534"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.drudis.2009.04.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032603777"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1966895.1966900", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035182105"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.drudis.2009.09.011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038849199"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4950995", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040034815"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4905365", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040083853"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/nl302992q", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051072323"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10618-014-0383-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051468562", 
          "https://doi.org/10.1007/s10618-014-0383-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10618-014-0383-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051468562", 
          "https://doi.org/10.1007/s10618-014-0383-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acscombsci.6b00053", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053831957"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/co500151u", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1054062987"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acs.chemmater.7b03591", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092668901"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/s41524-017-0056-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099656915", 
          "https://doi.org/10.1038/s41524-017-0056-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acscentsci.7b00572", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100346759"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.md.2018.04.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103446027"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8ee00179k", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1104478568"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8sc03077d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107845175"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c8sc03077d", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1107845175"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-03-27", 
    "datePublishedReg": "2019-03-27", 
    "description": "Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1038/s41597-019-0019-4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4321341", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1050678", 
        "issn": [
          "2052-4463"
        ], 
        "name": "Scientific Data", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "name": "Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides", 
    "pagination": "9", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "2e2c6675d775981be1581e1ca8bfb4af4b8cab2d99e655203257c6071031384c"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30918263"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101640192"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/s41597-019-0019-4"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113004922"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/s41597-019-0019-4", 
      "https://app.dimensions.ai/details/publication/pub.1113004922"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:50", 
    "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/v90/0000000371_0000000371/records_130794_00000006.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://www.nature.com/articles/s41597-019-0019-4"
  }
]
 

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.10.1038/s41597-019-0019-4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0019-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0019-4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41597-019-0019-4'


 

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

156 TRIPLES      21 PREDICATES      48 URIs      20 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/s41597-019-0019-4 schema:about anzsrc-for:09
2 anzsrc-for:0912
3 schema:author Nb3242661df4b4cc092472c043f126f1d
4 schema:citation sg:pub.10.1007/s10618-014-0383-9
5 sg:pub.10.1038/npjcompumats.2015.10
6 sg:pub.10.1038/s41524-017-0056-5
7 https://doi.org/10.1002/minf.201400174
8 https://doi.org/10.1016/j.cossms.2016.07.002
9 https://doi.org/10.1016/j.drudis.2009.04.005
10 https://doi.org/10.1016/j.drudis.2009.09.011
11 https://doi.org/10.1016/j.md.2018.04.003
12 https://doi.org/10.1021/acs.chemmater.7b03591
13 https://doi.org/10.1021/acscentsci.7b00572
14 https://doi.org/10.1021/acscombsci.6b00053
15 https://doi.org/10.1021/co500151u
16 https://doi.org/10.1021/nl302992q
17 https://doi.org/10.1039/c6ta01252c
18 https://doi.org/10.1039/c8ee00179k
19 https://doi.org/10.1039/c8sc03077d
20 https://doi.org/10.1063/1.4812323
21 https://doi.org/10.1063/1.4905365
22 https://doi.org/10.1063/1.4950995
23 https://doi.org/10.1145/1966895.1966900
24 schema:datePublished 2019-03-27
25 schema:datePublishedReg 2019-03-27
26 schema:description Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties.
27 schema:genre research_article
28 schema:inLanguage en
29 schema:isAccessibleForFree false
30 schema:isPartOf N2e4122f381cf4ac09ed5fcae236a76ba
31 Na1b296bfc3774aeb96579fa4b3440ce8
32 sg:journal.1050678
33 schema:name Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides
34 schema:pagination 9
35 schema:productId N074cf5408f1944219097d95f5809c133
36 N32ddf0c0b9e04fa7b3df5306dd2ae35c
37 N4281c2aa66e54adfb42421e7a5142e5a
38 Nb111af69f3b34414874948a56b96ebb8
39 Ne49eceea016d4653ba5ed6639a62dc2e
40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113004922
41 https://doi.org/10.1038/s41597-019-0019-4
42 schema:sdDatePublished 2019-04-11T13:50
43 schema:sdLicense https://scigraph.springernature.com/explorer/license/
44 schema:sdPublisher N3997116a3adc491ca3bc754fa6303984
45 schema:url https://www.nature.com/articles/s41597-019-0019-4
46 sgo:license sg:explorer/license/
47 sgo:sdDataset articles
48 rdf:type schema:ScholarlyArticle
49 N02a8e98f88454654b8104cf0c76f4737 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
50 schema:familyName Guevarra
51 schema:givenName Dan
52 rdf:type schema:Person
53 N02f6c683060345e79ea6d5cad6a7cfd1 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
54 schema:familyName Stein
55 schema:givenName Helge S.
56 rdf:type schema:Person
57 N074cf5408f1944219097d95f5809c133 schema:name nlm_unique_id
58 schema:value 101640192
59 rdf:type schema:PropertyValue
60 N2e4122f381cf4ac09ed5fcae236a76ba schema:volumeNumber 6
61 rdf:type schema:PublicationVolume
62 N32ddf0c0b9e04fa7b3df5306dd2ae35c schema:name doi
63 schema:value 10.1038/s41597-019-0019-4
64 rdf:type schema:PropertyValue
65 N3997116a3adc491ca3bc754fa6303984 schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 N4281c2aa66e54adfb42421e7a5142e5a schema:name pubmed_id
68 schema:value 30918263
69 rdf:type schema:PropertyValue
70 N6bb5f650e9e0430f884fba157efc5a9a rdf:first N7455c51fd2ab4b92819359d9a413d550
71 rdf:rest rdf:nil
72 N72ee6883c4a1496c9d4185766018e61a rdf:first N99085260ffd44cd5b416293de02dea89
73 rdf:rest N7d92f03d96b041689fdf7548d767a9fd
74 N7455c51fd2ab4b92819359d9a413d550 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
75 schema:familyName Gregoire
76 schema:givenName John M.
77 rdf:type schema:Person
78 N7d92f03d96b041689fdf7548d767a9fd rdf:first N02a8e98f88454654b8104cf0c76f4737
79 rdf:rest N6bb5f650e9e0430f884fba157efc5a9a
80 N99085260ffd44cd5b416293de02dea89 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
81 schema:familyName Newhouse
82 schema:givenName Paul F.
83 rdf:type schema:Person
84 Na1b296bfc3774aeb96579fa4b3440ce8 schema:issueNumber 1
85 rdf:type schema:PublicationIssue
86 Na71dfcf22cda4ad59f214be6c5598715 rdf:first Ne6411dba4e4942988c10271f7d1d1943
87 rdf:rest N72ee6883c4a1496c9d4185766018e61a
88 Nb111af69f3b34414874948a56b96ebb8 schema:name dimensions_id
89 schema:value pub.1113004922
90 rdf:type schema:PropertyValue
91 Nb3242661df4b4cc092472c043f126f1d rdf:first N02f6c683060345e79ea6d5cad6a7cfd1
92 rdf:rest Na71dfcf22cda4ad59f214be6c5598715
93 Ne49eceea016d4653ba5ed6639a62dc2e schema:name readcube_id
94 schema:value 2e2c6675d775981be1581e1ca8bfb4af4b8cab2d99e655203257c6071031384c
95 rdf:type schema:PropertyValue
96 Ne6411dba4e4942988c10271f7d1d1943 schema:affiliation https://www.grid.ac/institutes/grid.20861.3d
97 schema:familyName Soedarmadji
98 schema:givenName Edwin
99 rdf:type schema:Person
100 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
101 schema:name Engineering
102 rdf:type schema:DefinedTerm
103 anzsrc-for:0912 schema:inDefinedTermSet anzsrc-for:
104 schema:name Materials Engineering
105 rdf:type schema:DefinedTerm
106 sg:grant.4321341 http://pending.schema.org/fundedItem sg:pub.10.1038/s41597-019-0019-4
107 rdf:type schema:MonetaryGrant
108 sg:journal.1050678 schema:issn 2052-4463
109 schema:name Scientific Data
110 rdf:type schema:Periodical
111 sg:pub.10.1007/s10618-014-0383-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051468562
112 https://doi.org/10.1007/s10618-014-0383-9
113 rdf:type schema:CreativeWork
114 sg:pub.10.1038/npjcompumats.2015.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025730500
115 https://doi.org/10.1038/npjcompumats.2015.10
116 rdf:type schema:CreativeWork
117 sg:pub.10.1038/s41524-017-0056-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099656915
118 https://doi.org/10.1038/s41524-017-0056-5
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1002/minf.201400174 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008429557
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1016/j.cossms.2016.07.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005331034
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1016/j.drudis.2009.04.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032603777
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1016/j.drudis.2009.09.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038849199
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.md.2018.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103446027
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1021/acs.chemmater.7b03591 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092668901
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1021/acscentsci.7b00572 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100346759
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1021/acscombsci.6b00053 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053831957
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1021/co500151u schema:sameAs https://app.dimensions.ai/details/publication/pub.1054062987
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1021/nl302992q schema:sameAs https://app.dimensions.ai/details/publication/pub.1051072323
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1039/c6ta01252c schema:sameAs https://app.dimensions.ai/details/publication/pub.1004948620
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1039/c8ee00179k schema:sameAs https://app.dimensions.ai/details/publication/pub.1104478568
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1039/c8sc03077d schema:sameAs https://app.dimensions.ai/details/publication/pub.1107845175
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1063/1.4812323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027518534
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1063/1.4905365 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040083853
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1063/1.4950995 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040034815
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1145/1966895.1966900 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035182105
153 rdf:type schema:CreativeWork
154 https://www.grid.ac/institutes/grid.20861.3d schema:alternateName California Institute of Technology
155 schema:name Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA
156 rdf:type schema:Organization
 




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


...