A new, automatic hydrothermal fluid sampler using a shape-memory alloy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1998-05

AUTHORS

Takeshi Naganuma, Masanori Kyo, Tatsuhiko Ueki, Kazuhiko Takeda, Jun-ichiro Ishibashi

ABSTRACT

A new hydrothermal fluid sampler has been developed to provide more maneuverability in underwater operation. The sampler characteristically employs a shape-memory alloy, which senses high temperature and actuates the suction mechanism. A shape-memory alloy is also used to switch the intake valve of the sampler, the intention being to avoid missampling when the inlet is in low temperature water. Prototype samplers were designed to collect the fluids hotter than 80°C. Test sampling was performed at hydrothermal vents (1372–1374 m deep) in the submarine volcano, Suiyo Seamount, Izu-Bonin Arc, northwestern Pacific. Observed fluid temperature was between 138 and 298°C, while the ambient seawater temperature was 3.1°C. Each prototype collected about 100 ml fluid as designed. The magnesium concentration in the samples indicated a seawater content of 47.5–90.8%, which indicates the entrainment of ambient seawater. Microscopic observatinn revealed the occurrence of microorganisms in the sample fluids at a population density of 105 to 106 cells ml−1, which is 2–3 orders of magnitude higher than those in seawater at that depth. The use of the newly developed fluid sampler will greatly facilitate the collection of vent-associated microorganisms, which are of potential biological and biotechnological interest. More... »

PAGES

241-246

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02751699

DOI

http://dx.doi.org/10.1007/bf02751699

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012723550


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/2008/0402", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geochemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Faculty of Applied Biological Science, Hiroshima University, 1-4-4 Kagamiyama, 739-8528, Higashi-hiroshima, Japan", 
            "Deep Sea Technology Department, Japan Marine Science and Technology Center, 2-15 Natsushima-cho, 237-0061, Yokosuka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Naganuma", 
        "givenName": "Takeshi", 
        "id": "sg:person.011214662431.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011214662431.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology", 
          "id": "https://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Deep Sea Technology Department, Japan Marine Science and Technology Center, 2-15 Natsushima-cho, 237-0061, Yokosuka, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kyo", 
        "givenName": "Masanori", 
        "id": "sg:person.01202515754.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202515754.20"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Furukawa Electric (Japan)", 
          "id": "https://www.grid.ac/institutes/grid.459529.6", 
          "name": [
            "Yokohama R&D Laboratories, Furukawa Electric Co., Ltd., 2-4-3 Okano, Nishi-ku, 220-0073, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ueki", 
        "givenName": "Tatsuhiko", 
        "id": "sg:person.015334573466.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015334573466.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hiroshima University", 
          "id": "https://www.grid.ac/institutes/grid.257022.0", 
          "name": [
            "Faculty of Integrated Arts and Sciences, Hiroshima University, 1-7-1 Kagamiyama, 739-8521, Higashi-hiroshima, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takeda", 
        "givenName": "Kazuhiko", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Tokyo", 
          "id": "https://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Laboratory for Earthquake Chemistry, University of Tokyo, Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ishibashi", 
        "givenName": "Jun-ichiro", 
        "id": "sg:person.011577742337.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011577742337.19"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0012-821x(94)90113-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009401251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0012-821x(94)90113-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009401251"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0198-0149(88)90030-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024624207"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0198-0149(88)90030-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024624207"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0198-0149(89)90089-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031168373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0198-0149(89)90089-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031168373"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1032384023", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1032384023", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0009-2541(95)00051-m", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035522863"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02123436", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040593617", 
          "https://doi.org/10.1007/bf02123436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02123436", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040593617", 
          "https://doi.org/10.1007/bf02123436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1843-3_13", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041040677", 
          "https://doi.org/10.1007/978-1-4615-1843-3_13"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4615-1843-3_13", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041040677", 
          "https://doi.org/10.1007/978-1-4615-1843-3_13"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0016-7037(85)90222-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052751269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0016-7037(85)90222-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052751269"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.229.4715.717", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062530629"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1126/science.274.5286.349", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1062554461"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1077158208", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078887497", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1079659020", 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1998-05", 
    "datePublishedReg": "1998-05-01", 
    "description": "A new hydrothermal fluid sampler has been developed to provide more maneuverability in underwater operation. The sampler characteristically employs a shape-memory alloy, which senses high temperature and actuates the suction mechanism. A shape-memory alloy is also used to switch the intake valve of the sampler, the intention being to avoid missampling when the inlet is in low temperature water. Prototype samplers were designed to collect the fluids hotter than 80\u00b0C. Test sampling was performed at hydrothermal vents (1372\u20131374 m deep) in the submarine volcano, Suiyo Seamount, Izu-Bonin Arc, northwestern Pacific. Observed fluid temperature was between 138 and 298\u00b0C, while the ambient seawater temperature was 3.1\u00b0C. Each prototype collected about 100 ml fluid as designed. The magnesium concentration in the samples indicated a seawater content of 47.5\u201390.8%, which indicates the entrainment of ambient seawater. Microscopic observatinn revealed the occurrence of microorganisms in the sample fluids at a population density of 105 to 106 cells ml\u22121, which is 2\u20133 orders of magnitude higher than those in seawater at that depth. The use of the newly developed fluid sampler will greatly facilitate the collection of vent-associated microorganisms, which are of potential biological and biotechnological interest.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf02751699", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1313776", 
        "issn": [
          "0916-8370", 
          "1573-868X"
        ], 
        "name": "Journal of Oceanography", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "54"
      }
    ], 
    "name": "A new, automatic hydrothermal fluid sampler using a shape-memory alloy", 
    "pagination": "241-246", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "3c9ccf0e648c95b61ccaa537aac2bf818b8e72d0bab27b9575cc53a16b915e6a"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02751699"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1012723550"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02751699", 
      "https://app.dimensions.ai/details/publication/pub.1012723550"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:34", 
    "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_8669_00000480.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF02751699"
  }
]
 

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/bf02751699'

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/bf02751699'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02751699'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02751699'


 

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

135 TRIPLES      21 PREDICATES      40 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02751699 schema:about anzsrc-for:04
2 anzsrc-for:0402
3 schema:author N934b6246c5f04b00b1c2bcb213b06ddd
4 schema:citation sg:pub.10.1007/978-1-4615-1843-3_13
5 sg:pub.10.1007/bf02123436
6 https://app.dimensions.ai/details/publication/pub.1032384023
7 https://app.dimensions.ai/details/publication/pub.1077158208
8 https://app.dimensions.ai/details/publication/pub.1078887497
9 https://app.dimensions.ai/details/publication/pub.1079659020
10 https://doi.org/10.1016/0009-2541(95)00051-m
11 https://doi.org/10.1016/0012-821x(94)90113-9
12 https://doi.org/10.1016/0016-7037(85)90222-4
13 https://doi.org/10.1016/0198-0149(88)90030-1
14 https://doi.org/10.1016/0198-0149(89)90089-7
15 https://doi.org/10.1126/science.229.4715.717
16 https://doi.org/10.1126/science.274.5286.349
17 schema:datePublished 1998-05
18 schema:datePublishedReg 1998-05-01
19 schema:description A new hydrothermal fluid sampler has been developed to provide more maneuverability in underwater operation. The sampler characteristically employs a shape-memory alloy, which senses high temperature and actuates the suction mechanism. A shape-memory alloy is also used to switch the intake valve of the sampler, the intention being to avoid missampling when the inlet is in low temperature water. Prototype samplers were designed to collect the fluids hotter than 80°C. Test sampling was performed at hydrothermal vents (1372–1374 m deep) in the submarine volcano, Suiyo Seamount, Izu-Bonin Arc, northwestern Pacific. Observed fluid temperature was between 138 and 298°C, while the ambient seawater temperature was 3.1°C. Each prototype collected about 100 ml fluid as designed. The magnesium concentration in the samples indicated a seawater content of 47.5–90.8%, which indicates the entrainment of ambient seawater. Microscopic observatinn revealed the occurrence of microorganisms in the sample fluids at a population density of 105 to 106 cells ml−1, which is 2–3 orders of magnitude higher than those in seawater at that depth. The use of the newly developed fluid sampler will greatly facilitate the collection of vent-associated microorganisms, which are of potential biological and biotechnological interest.
20 schema:genre research_article
21 schema:inLanguage en
22 schema:isAccessibleForFree true
23 schema:isPartOf N09affc5bee6f416fa4f08b68e9e021d9
24 Nb4123020fbd5485da603b46888d7673b
25 sg:journal.1313776
26 schema:name A new, automatic hydrothermal fluid sampler using a shape-memory alloy
27 schema:pagination 241-246
28 schema:productId N8f2975736c02453f8f68d5381fe751aa
29 Na533012eaa0148938b26253b7be91d6e
30 Nc97684047a7d43ca924017d9f4aa2ff8
31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012723550
32 https://doi.org/10.1007/bf02751699
33 schema:sdDatePublished 2019-04-10T16:34
34 schema:sdLicense https://scigraph.springernature.com/explorer/license/
35 schema:sdPublisher N9549afebbe8842b7a2519a13f38c3482
36 schema:url http://link.springer.com/10.1007/BF02751699
37 sgo:license sg:explorer/license/
38 sgo:sdDataset articles
39 rdf:type schema:ScholarlyArticle
40 N09affc5bee6f416fa4f08b68e9e021d9 schema:issueNumber 3
41 rdf:type schema:PublicationIssue
42 N60ce07f9b49546a79a3c9c348562ecb4 rdf:first Nb1cb37cdf55a421a887018cada001c1b
43 rdf:rest Na729826ac71b40ea8881369977a4ad10
44 N8f2975736c02453f8f68d5381fe751aa schema:name readcube_id
45 schema:value 3c9ccf0e648c95b61ccaa537aac2bf818b8e72d0bab27b9575cc53a16b915e6a
46 rdf:type schema:PropertyValue
47 N934b6246c5f04b00b1c2bcb213b06ddd rdf:first sg:person.011214662431.46
48 rdf:rest Nb6dd86e3715545889d392f94f4d9e221
49 N9549afebbe8842b7a2519a13f38c3482 schema:name Springer Nature - SN SciGraph project
50 rdf:type schema:Organization
51 Na533012eaa0148938b26253b7be91d6e schema:name dimensions_id
52 schema:value pub.1012723550
53 rdf:type schema:PropertyValue
54 Na729826ac71b40ea8881369977a4ad10 rdf:first sg:person.011577742337.19
55 rdf:rest rdf:nil
56 Nb1cb37cdf55a421a887018cada001c1b schema:affiliation https://www.grid.ac/institutes/grid.257022.0
57 schema:familyName Takeda
58 schema:givenName Kazuhiko
59 rdf:type schema:Person
60 Nb4123020fbd5485da603b46888d7673b schema:volumeNumber 54
61 rdf:type schema:PublicationVolume
62 Nb6dd86e3715545889d392f94f4d9e221 rdf:first sg:person.01202515754.20
63 rdf:rest Ne442528b80024d40a6310bb20ca016de
64 Nc97684047a7d43ca924017d9f4aa2ff8 schema:name doi
65 schema:value 10.1007/bf02751699
66 rdf:type schema:PropertyValue
67 Ne442528b80024d40a6310bb20ca016de rdf:first sg:person.015334573466.40
68 rdf:rest N60ce07f9b49546a79a3c9c348562ecb4
69 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
70 schema:name Earth Sciences
71 rdf:type schema:DefinedTerm
72 anzsrc-for:0402 schema:inDefinedTermSet anzsrc-for:
73 schema:name Geochemistry
74 rdf:type schema:DefinedTerm
75 sg:journal.1313776 schema:issn 0916-8370
76 1573-868X
77 schema:name Journal of Oceanography
78 rdf:type schema:Periodical
79 sg:person.011214662431.46 schema:affiliation https://www.grid.ac/institutes/grid.410588.0
80 schema:familyName Naganuma
81 schema:givenName Takeshi
82 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011214662431.46
83 rdf:type schema:Person
84 sg:person.011577742337.19 schema:affiliation https://www.grid.ac/institutes/grid.26999.3d
85 schema:familyName Ishibashi
86 schema:givenName Jun-ichiro
87 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011577742337.19
88 rdf:type schema:Person
89 sg:person.01202515754.20 schema:affiliation https://www.grid.ac/institutes/grid.410588.0
90 schema:familyName Kyo
91 schema:givenName Masanori
92 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01202515754.20
93 rdf:type schema:Person
94 sg:person.015334573466.40 schema:affiliation https://www.grid.ac/institutes/grid.459529.6
95 schema:familyName Ueki
96 schema:givenName Tatsuhiko
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015334573466.40
98 rdf:type schema:Person
99 sg:pub.10.1007/978-1-4615-1843-3_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041040677
100 https://doi.org/10.1007/978-1-4615-1843-3_13
101 rdf:type schema:CreativeWork
102 sg:pub.10.1007/bf02123436 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040593617
103 https://doi.org/10.1007/bf02123436
104 rdf:type schema:CreativeWork
105 https://app.dimensions.ai/details/publication/pub.1032384023 schema:CreativeWork
106 https://app.dimensions.ai/details/publication/pub.1077158208 schema:CreativeWork
107 https://app.dimensions.ai/details/publication/pub.1078887497 schema:CreativeWork
108 https://app.dimensions.ai/details/publication/pub.1079659020 schema:CreativeWork
109 https://doi.org/10.1016/0009-2541(95)00051-m schema:sameAs https://app.dimensions.ai/details/publication/pub.1035522863
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1016/0012-821x(94)90113-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009401251
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1016/0016-7037(85)90222-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052751269
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1016/0198-0149(88)90030-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024624207
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1016/0198-0149(89)90089-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031168373
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1126/science.229.4715.717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062530629
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1126/science.274.5286.349 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062554461
122 rdf:type schema:CreativeWork
123 https://www.grid.ac/institutes/grid.257022.0 schema:alternateName Hiroshima University
124 schema:name Faculty of Integrated Arts and Sciences, Hiroshima University, 1-7-1 Kagamiyama, 739-8521, Higashi-hiroshima, Japan
125 rdf:type schema:Organization
126 https://www.grid.ac/institutes/grid.26999.3d schema:alternateName University of Tokyo
127 schema:name Laboratory for Earthquake Chemistry, University of Tokyo, Hongo, Bunkyo-ku, 113-0033, Tokyo, Japan
128 rdf:type schema:Organization
129 https://www.grid.ac/institutes/grid.410588.0 schema:alternateName Japan Agency for Marine-Earth Science and Technology
130 schema:name Deep Sea Technology Department, Japan Marine Science and Technology Center, 2-15 Natsushima-cho, 237-0061, Yokosuka, Japan
131 Faculty of Applied Biological Science, Hiroshima University, 1-4-4 Kagamiyama, 739-8528, Higashi-hiroshima, Japan
132 rdf:type schema:Organization
133 https://www.grid.ac/institutes/grid.459529.6 schema:alternateName Furukawa Electric (Japan)
134 schema:name Yokohama R&D Laboratories, Furukawa Electric Co., Ltd., 2-4-3 Okano, Nishi-ku, 220-0073, Yokohama, Japan
135 rdf:type schema:Organization
 




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


...