Ontology type: schema:ScholarlyArticle
2014-02
AUTHORSNargess Mehdipour, Neda Mousavian, Hossein Eslami
ABSTRACTA new molecular dynamics simulation technique for simulating fluids in confinement [H. Eslami, F. Mozaffari, J. Moghadasi, F. Müller-Plathe, J. Chem. Phys. 129 (2008) 194702] is employed to simulate the diffusion coefficient of nanoconfined Lennard-Jones fluid. The diffusing fluid is liquid Ar and the confining surfaces are solid Ar fcc (100) surfaces, which are kept frozen during the simulation. In this simulation just the fluid in confinement is simulated at a constant temperature and a constant parallel component of pressure, which is assumed to be equal to the bulk pressure. It is shown that the calculated parallel (to the surfaces) component of the diffusion coefficients depends on the distance between the surfaces (pore size) and shows oscillatory behavior with respect to the intersurface separations. Our results show that on formation of well-organized layers between the surfaces, the parallel diffusion coefficients decrease considerably with respect to the bulk fluid. The effect of pressure on the parallel diffusion coefficients has also been studied. Better organized layers, and hence, lower diffusion coefficients are observed with increasing the pressure. More... »
PAGES47-52
http://scigraph.springernature.com/pub.10.1007/s13738-013-0274-9
DOIhttp://dx.doi.org/10.1007/s13738-013-0274-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1045489347
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/2008/0915",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Interdisciplinary 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": "Persian Gulf University",
"id": "https://www.grid.ac/institutes/grid.412491.b",
"name": [
"Department of Chemistry College of Sciences, Persian Gulf University, 75168, Boushehr, Iran"
],
"type": "Organization"
},
"familyName": "Mehdipour",
"givenName": "Nargess",
"id": "sg:person.0712722573.26",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0712722573.26"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Persian Gulf University",
"id": "https://www.grid.ac/institutes/grid.412491.b",
"name": [
"Department of Chemistry College of Sciences, Persian Gulf University, 75168, Boushehr, Iran"
],
"type": "Organization"
},
"familyName": "Mousavian",
"givenName": "Neda",
"type": "Person"
},
{
"affiliation": {
"alternateName": "Persian Gulf University",
"id": "https://www.grid.ac/institutes/grid.412491.b",
"name": [
"Department of Chemistry College of Sciences, Persian Gulf University, 75168, Boushehr, Iran"
],
"type": "Organization"
},
"familyName": "Eslami",
"givenName": "Hossein",
"id": "sg:person.0700465067.86",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0700465067.86"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1002/jcc.20689",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009925472"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/00018738600101851",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011606104"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/00018738600101851",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011606104"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0021-9991(77)90121-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014025009"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0010-4655(93)90144-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028403532"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-662-04283-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048344375",
"https://doi.org/10.1007/978-3-662-04283-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-662-04283-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048344375",
"https://doi.org/10.1007/978-3-662-04283-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/00268978200101411",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049992103"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1021/ci050126l",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1055402055"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1021/ci050126l",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1055402055"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1021/ja0441817",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1055836154"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1021/ja0441817",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1055836154"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.3009844",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057892757"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.3474951",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1057957967"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.439489",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058017518"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.448118",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058026138"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.449375",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058027394"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.453240",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058031256"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.453665",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058031681"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.458823",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058036834"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.466377",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058044383"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.466865",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058044871"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.470685",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058049072"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.476114",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058062012"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1063/1.481430",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058079252"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.41.6830",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060481651"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physreva.41.6830",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060481651"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.47.5603",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060566314"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.47.5603",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060566314"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.49.790",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060788056"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.49.790",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060788056"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.77.683",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060814570"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.77.683",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060814570"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1126/science.245.4923.1223",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062538320"
],
"type": "CreativeWork"
}
],
"datePublished": "2014-02",
"datePublishedReg": "2014-02-01",
"description": "A new molecular dynamics simulation technique for simulating fluids in confinement [H. Eslami, F. Mozaffari, J. Moghadasi, F. M\u00fcller-Plathe, J. Chem. Phys. 129 (2008) 194702] is employed to simulate the diffusion coefficient of nanoconfined Lennard-Jones fluid. The diffusing fluid is liquid Ar and the confining surfaces are solid Ar fcc (100) surfaces, which are kept frozen during the simulation. In this simulation just the fluid in confinement is simulated at a constant temperature and a constant parallel component of pressure, which is assumed to be equal to the bulk pressure. It is shown that the calculated parallel (to the surfaces) component of the diffusion coefficients depends on the distance between the surfaces (pore size) and shows oscillatory behavior with respect to the intersurface separations. Our results show that on formation of well-organized layers between the surfaces, the parallel diffusion coefficients decrease considerably with respect to the bulk fluid. The effect of pressure on the parallel diffusion coefficients has also been studied. Better organized layers, and hence, lower diffusion coefficients are observed with increasing the pressure.",
"genre": "research_article",
"id": "sg:pub.10.1007/s13738-013-0274-9",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1037017",
"issn": [
"1735-207X",
"1735-2428"
],
"name": "Journal of the Iranian Chemical Society",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "11"
}
],
"name": "Molecular dynamics simulation of the diffusion of nanoconfined fluids",
"pagination": "47-52",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"d8af44dc91b1853915f2cc0ceff87557d1153044433ad77bb6d7bad4efd6906d"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s13738-013-0274-9"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1045489347"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s13738-013-0274-9",
"https://app.dimensions.ai/details/publication/pub.1045489347"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-10T15:54",
"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_8664_00000524.jsonl",
"type": "ScholarlyArticle",
"url": "http://link.springer.com/10.1007%2Fs13738-013-0274-9"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
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/s13738-013-0274-9'
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/s13738-013-0274-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s13738-013-0274-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s13738-013-0274-9'
This table displays all metadata directly associated to this object as RDF triples.
153 TRIPLES
21 PREDICATES
53 URIs
19 LITERALS
7 BLANK NODES