A simulation model study of the coupled field in the IMS drift tube View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-11-10

AUTHORS

Fenglei Han, He Zhang, Liying Peng, Haiyang Li

ABSTRACT

The performance of the IMS was influenced by many parameters, like temperature, gas flow rate, etc. in the drift tube. An exact and comprehensive simulation model was very useful for the IMS design and optimization. A combined simulation model was build up for the parameters simulation in the drift tube. Based on this simulation model, the heat transfer, velocity distribution, humidity and ion transportation inside the drift tube in bidirectional flow stream was simulated, and the impact on the IMS was studied. And the simulation was also validated using an IMS constructed in our laboratory. The experiment showed that the RIP intensity weakened as the humidity increasing, but the signal intensity of NO was enhanced first, and then decreased with the humidity increasing sequentially. This can be explained from the simulation results. The simulation results showed that the distribution of the velocity and temperature was not uniformed in the drift tube. And this phenomenon was more clearly when the gas flow velocity increased. It can be seen from the simulation that the humidity in the drift tube region was smaller than the sample moisture, and the resolution of the ion mobility spectrometry will be reduced by the humidity. But in the region rich in water molecules, ultraviolet photons re-acting with acetone would be obviously decreased and fewer re-agent ions were produced owing to the strong absorption of photons by water neutrals. The results showed that the coupled field simulation model can be used to study parameters effects on the IMS. More... »

PAGES

219-226

References to SciGraph publications

  • 2006-09-01. Ion trajectory simulation for electrode configurations with arbitrary geometries in JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
  • 2005-09-01. FAIMS operation for realistic gas flow profile and asymmetric waveforms including electronic noise and ripple in JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY
  • 2011-11-09. Separation principle and Monte Carlo studies for differential mobility spectrometry in INTERNATIONAL JOURNAL FOR ION MOBILITY SPECTROMETRY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12127-016-0209-0

    DOI

    http://dx.doi.org/10.1007/s12127-016-0209-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1021580361


    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/2008/09", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Engineering", 
            "type": "DefinedTerm"
          }, 
          {
            "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"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "China University of Petroleum (Huadong), 266580, Qingdao, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.497420.c", 
              "name": [
                "China University of Petroleum (Huadong), 266580, Qingdao, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Han", 
            "givenName": "Fenglei", 
            "id": "sg:person.0755337340.09", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755337340.09"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "China University of Petroleum (Huadong), 266580, Qingdao, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.497420.c", 
              "name": [
                "China University of Petroleum (Huadong), 266580, Qingdao, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhang", 
            "givenName": "He", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.423905.9", 
              "name": [
                "Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Peng", 
            "givenName": "Liying", 
            "id": "sg:person.01176152364.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176152364.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People\u2019s Republic of China", 
              "id": "http://www.grid.ac/institutes/grid.423905.9", 
              "name": [
                "Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People\u2019s Republic of China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Li", 
            "givenName": "Haiyang", 
            "id": "sg:person.01076721057.34", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076721057.34"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1016/j.jasms.2006.05.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047444382", 
              "https://doi.org/10.1016/j.jasms.2006.05.004"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1016/j.jasms.2005.04.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011044799", 
              "https://doi.org/10.1016/j.jasms.2005.04.003"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12127-011-0083-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041838064", 
              "https://doi.org/10.1007/s12127-011-0083-8"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-11-10", 
        "datePublishedReg": "2016-11-10", 
        "description": "The performance of the IMS was influenced by many parameters, like temperature, gas flow rate, etc. in the drift tube. An exact and comprehensive simulation model was very useful for the IMS design and optimization. A combined simulation model was build up for the parameters simulation in the drift tube. Based on this simulation model, the heat transfer, velocity distribution, humidity and ion transportation inside the drift tube in bidirectional flow stream was simulated, and the impact on the IMS was studied. And the simulation was also validated using an IMS constructed in our laboratory. The experiment showed that the RIP intensity weakened as the humidity increasing, but the signal intensity of NO was enhanced first, and then decreased with the humidity increasing sequentially. This can be explained from the simulation results. The simulation results showed that the distribution of the velocity and temperature was not uniformed in the drift tube. And this phenomenon was more clearly when the gas flow velocity increased. It can be seen from the simulation that the humidity in the drift tube region was smaller than the sample moisture, and the resolution of the ion mobility spectrometry will be reduced by the humidity. But in the region rich in water molecules, ultraviolet photons re-acting with acetone would be obviously decreased and fewer re-agent ions were produced owing to the strong absorption of photons by water neutrals. The results showed that the coupled field simulation model can be used to study parameters effects on the IMS.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s12127-016-0209-0", 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8150962", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1043860", 
            "issn": [
              "1435-6163", 
              "1865-4584"
            ], 
            "name": "International Journal for Ion Mobility Spectrometry", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "4", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "19"
          }
        ], 
        "keywords": [
          "simulation model", 
          "gas flow velocity", 
          "gas flow rate", 
          "field simulation model", 
          "simulation results", 
          "comprehensive simulation model", 
          "simulation model study", 
          "heat transfer", 
          "drift tube", 
          "parameter effects", 
          "parameter simulation", 
          "flow rate", 
          "flow stream", 
          "IMS drift tube", 
          "ion transportation", 
          "sample moisture", 
          "velocity distribution", 
          "flow velocity", 
          "tube region", 
          "simulations", 
          "humidity", 
          "tube", 
          "velocity", 
          "IMS design", 
          "temperature", 
          "strong absorption", 
          "ultraviolet photons", 
          "model studies", 
          "ion mobility spectrometry", 
          "moisture", 
          "model", 
          "optimization", 
          "design", 
          "performance", 
          "transportation", 
          "distribution", 
          "results", 
          "mobility spectrometry", 
          "parameters", 
          "water molecules", 
          "transfer", 
          "field", 
          "streams", 
          "absorption", 
          "intensity", 
          "resolution", 
          "experiments", 
          "phenomenon", 
          "acetone", 
          "neutrals", 
          "ions", 
          "region", 
          "IMS", 
          "signal intensity", 
          "NO", 
          "laboratory", 
          "photons", 
          "effect", 
          "rate", 
          "impact", 
          "study", 
          "spectrometry", 
          "molecules"
        ], 
        "name": "A simulation model study of the coupled field in the IMS drift tube", 
        "pagination": "219-226", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1021580361"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s12127-016-0209-0"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s12127-016-0209-0", 
          "https://app.dimensions.ai/details/publication/pub.1021580361"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:34", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_682.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s12127-016-0209-0"
      }
    ]
     

    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/s12127-016-0209-0'

    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/s12127-016-0209-0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s12127-016-0209-0'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s12127-016-0209-0'


     

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

    157 TRIPLES      21 PREDICATES      90 URIs      79 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s12127-016-0209-0 schema:about anzsrc-for:09
    2 anzsrc-for:0915
    3 schema:author N139284743d51498ba5fe0be8d159a8ff
    4 schema:citation sg:pub.10.1007/s12127-011-0083-8
    5 sg:pub.10.1016/j.jasms.2005.04.003
    6 sg:pub.10.1016/j.jasms.2006.05.004
    7 schema:datePublished 2016-11-10
    8 schema:datePublishedReg 2016-11-10
    9 schema:description The performance of the IMS was influenced by many parameters, like temperature, gas flow rate, etc. in the drift tube. An exact and comprehensive simulation model was very useful for the IMS design and optimization. A combined simulation model was build up for the parameters simulation in the drift tube. Based on this simulation model, the heat transfer, velocity distribution, humidity and ion transportation inside the drift tube in bidirectional flow stream was simulated, and the impact on the IMS was studied. And the simulation was also validated using an IMS constructed in our laboratory. The experiment showed that the RIP intensity weakened as the humidity increasing, but the signal intensity of NO was enhanced first, and then decreased with the humidity increasing sequentially. This can be explained from the simulation results. The simulation results showed that the distribution of the velocity and temperature was not uniformed in the drift tube. And this phenomenon was more clearly when the gas flow velocity increased. It can be seen from the simulation that the humidity in the drift tube region was smaller than the sample moisture, and the resolution of the ion mobility spectrometry will be reduced by the humidity. But in the region rich in water molecules, ultraviolet photons re-acting with acetone would be obviously decreased and fewer re-agent ions were produced owing to the strong absorption of photons by water neutrals. The results showed that the coupled field simulation model can be used to study parameters effects on the IMS.
    10 schema:genre article
    11 schema:isAccessibleForFree false
    12 schema:isPartOf N92b654102cfd4a7884a87412b01bb90b
    13 Nff6487058ce943aaa3807646446315cf
    14 sg:journal.1043860
    15 schema:keywords IMS
    16 IMS design
    17 IMS drift tube
    18 NO
    19 absorption
    20 acetone
    21 comprehensive simulation model
    22 design
    23 distribution
    24 drift tube
    25 effect
    26 experiments
    27 field
    28 field simulation model
    29 flow rate
    30 flow stream
    31 flow velocity
    32 gas flow rate
    33 gas flow velocity
    34 heat transfer
    35 humidity
    36 impact
    37 intensity
    38 ion mobility spectrometry
    39 ion transportation
    40 ions
    41 laboratory
    42 mobility spectrometry
    43 model
    44 model studies
    45 moisture
    46 molecules
    47 neutrals
    48 optimization
    49 parameter effects
    50 parameter simulation
    51 parameters
    52 performance
    53 phenomenon
    54 photons
    55 rate
    56 region
    57 resolution
    58 results
    59 sample moisture
    60 signal intensity
    61 simulation model
    62 simulation model study
    63 simulation results
    64 simulations
    65 spectrometry
    66 streams
    67 strong absorption
    68 study
    69 temperature
    70 transfer
    71 transportation
    72 tube
    73 tube region
    74 ultraviolet photons
    75 velocity
    76 velocity distribution
    77 water molecules
    78 schema:name A simulation model study of the coupled field in the IMS drift tube
    79 schema:pagination 219-226
    80 schema:productId N32aa3ba13b2342ab9e70e9f57e7a511c
    81 N6b995ddd21a748dabd12e7376da1b85e
    82 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021580361
    83 https://doi.org/10.1007/s12127-016-0209-0
    84 schema:sdDatePublished 2022-12-01T06:34
    85 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    86 schema:sdPublisher Nd14693b325534a82a3147515ca8a05f6
    87 schema:url https://doi.org/10.1007/s12127-016-0209-0
    88 sgo:license sg:explorer/license/
    89 sgo:sdDataset articles
    90 rdf:type schema:ScholarlyArticle
    91 N139284743d51498ba5fe0be8d159a8ff rdf:first sg:person.0755337340.09
    92 rdf:rest N17d5de4f044b461ebb8462ca26775138
    93 N17d5de4f044b461ebb8462ca26775138 rdf:first Necfeac4c31c846a7b993a38ad735b642
    94 rdf:rest Nc2e39c27595a4b899b44a08421879757
    95 N32aa3ba13b2342ab9e70e9f57e7a511c schema:name doi
    96 schema:value 10.1007/s12127-016-0209-0
    97 rdf:type schema:PropertyValue
    98 N6b995ddd21a748dabd12e7376da1b85e schema:name dimensions_id
    99 schema:value pub.1021580361
    100 rdf:type schema:PropertyValue
    101 N92b654102cfd4a7884a87412b01bb90b schema:issueNumber 4
    102 rdf:type schema:PublicationIssue
    103 Naf46673a54a74bbb81a1bc5eb3632464 rdf:first sg:person.01076721057.34
    104 rdf:rest rdf:nil
    105 Nc2e39c27595a4b899b44a08421879757 rdf:first sg:person.01176152364.68
    106 rdf:rest Naf46673a54a74bbb81a1bc5eb3632464
    107 Nd14693b325534a82a3147515ca8a05f6 schema:name Springer Nature - SN SciGraph project
    108 rdf:type schema:Organization
    109 Necfeac4c31c846a7b993a38ad735b642 schema:affiliation grid-institutes:grid.497420.c
    110 schema:familyName Zhang
    111 schema:givenName He
    112 rdf:type schema:Person
    113 Nff6487058ce943aaa3807646446315cf schema:volumeNumber 19
    114 rdf:type schema:PublicationVolume
    115 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    116 schema:name Engineering
    117 rdf:type schema:DefinedTerm
    118 anzsrc-for:0915 schema:inDefinedTermSet anzsrc-for:
    119 schema:name Interdisciplinary Engineering
    120 rdf:type schema:DefinedTerm
    121 sg:grant.8150962 http://pending.schema.org/fundedItem sg:pub.10.1007/s12127-016-0209-0
    122 rdf:type schema:MonetaryGrant
    123 sg:journal.1043860 schema:issn 1435-6163
    124 1865-4584
    125 schema:name International Journal for Ion Mobility Spectrometry
    126 schema:publisher Springer Nature
    127 rdf:type schema:Periodical
    128 sg:person.01076721057.34 schema:affiliation grid-institutes:grid.423905.9
    129 schema:familyName Li
    130 schema:givenName Haiyang
    131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01076721057.34
    132 rdf:type schema:Person
    133 sg:person.01176152364.68 schema:affiliation grid-institutes:grid.423905.9
    134 schema:familyName Peng
    135 schema:givenName Liying
    136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01176152364.68
    137 rdf:type schema:Person
    138 sg:person.0755337340.09 schema:affiliation grid-institutes:grid.497420.c
    139 schema:familyName Han
    140 schema:givenName Fenglei
    141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0755337340.09
    142 rdf:type schema:Person
    143 sg:pub.10.1007/s12127-011-0083-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041838064
    144 https://doi.org/10.1007/s12127-011-0083-8
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1016/j.jasms.2005.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011044799
    147 https://doi.org/10.1016/j.jasms.2005.04.003
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1016/j.jasms.2006.05.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047444382
    150 https://doi.org/10.1016/j.jasms.2006.05.004
    151 rdf:type schema:CreativeWork
    152 grid-institutes:grid.423905.9 schema:alternateName Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People’s Republic of China
    153 schema:name Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, People’s Republic of China
    154 rdf:type schema:Organization
    155 grid-institutes:grid.497420.c schema:alternateName China University of Petroleum (Huadong), 266580, Qingdao, People’s Republic of China
    156 schema:name China University of Petroleum (Huadong), 266580, Qingdao, People’s Republic of China
    157 rdf:type schema:Organization
     




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


    ...