Three routes to superinsulating silica aerogel powder View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01-11

AUTHORS

Ana Stojanovic, Shanyu Zhao, Emanuele Angelica, Wim J. Malfait, Matthias M. Koebel

ABSTRACT

Silica aerogel is the archetypal thermal superinsulator and commonly applied to improve the thermal performance of on- and off-shore industrial infrastructure and buildings. Hitherto, the main products on the market are silica aerogel based blankets fabricated by casting a silica sol into a porous or fiber matrix, followed by gelation, hydrophobization, and (supercritical) drying. Considering the diffusion efficiency of the reagents and solvents used in the preparation, a reduced size of the gel bodies may accelerate and simplify the sol-gel, hydrophobization, and drying processes. Thus, particle based products, derived from silica aerogel granulate and powder additives and semi-finished products, are an attractive solution towards inexpensive aerogel applications. Here, we optimized the process parameters for silica aerogel powder production from three common silica precursors: waterglass (WG), ion-exchanged waterglass, and tetraethoxysilane (TEOS), including gelation pH, hydrophobization procedure, solvent system, processing temperatures, and ambient pressure drying protocol. Successful hydrophobization is confirmed by elemental analysis, FTIR and quantitative solid-state NMR spectroscopy. All three routes lead to silica aerogel powders with similar type IV isotherms, and BET surface areas above 700 m2/g. Importantly, the thermal conductivity of the packed powder beds does not exceed 20 mW/(m⋅K) for all three routes, implying even lower thermal conductivities of the aerogel phase itself. Total processing time, including gelation, aging, surface modification, and ambient pressure drying is between 2 and 4 h, depending on the selected route. Given that high quality silica aerogel powders can be produced from all investigated silica precursors and hydrophobization agents, the process selection for industrial upscaling can be based entirely on engineering and economic considerations. Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS.All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m·K).The precursor can thus be selected based on availability, cost and process complexity. Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS. All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m·K). The precursor can thus be selected based on availability, cost and process complexity. More... »

PAGES

1-10

References to SciGraph publications

Journal

TITLE

Journal of Sol-Gel Science and Technology

ISSUE

N/A

VOLUME

N/A

From Grant

  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10971-018-4879-4

    DOI

    http://dx.doi.org/10.1007/s10971-018-4879-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111367798


    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/2008/0904", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Chemical 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": {
              "name": [
                "Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, \u00dcberlandstrasse 129, 8600, D\u00fcbendorf, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Stojanovic", 
            "givenName": "Ana", 
            "id": "sg:person.014655665020.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014655665020.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, \u00dcberlandstrasse 129, 8600, D\u00fcbendorf, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Zhao", 
            "givenName": "Shanyu", 
            "id": "sg:person.015367575177.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015367575177.51"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, \u00dcberlandstrasse 129, 8600, D\u00fcbendorf, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Angelica", 
            "givenName": "Emanuele", 
            "id": "sg:person.010001033415.50", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010001033415.50"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, \u00dcberlandstrasse 129, 8600, D\u00fcbendorf, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Malfait", 
            "givenName": "Wim J.", 
            "id": "sg:person.012241750014.12", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012241750014.12"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, \u00dcberlandstrasse 129, 8600, D\u00fcbendorf, Switzerland"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Koebel", 
            "givenName": "Matthias M.", 
            "id": "sg:person.01304260225.51", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01304260225.51"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.apt.2015.01.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002987459"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1015309014546", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004372124", 
              "https://doi.org/10.1023/a:1015309014546"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jallcom.2008.09.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005423658"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.micromeso.2007.10.030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014770616"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0022-3093(98)00370-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016484041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compositesa.2015.04.014", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018772499"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/pc.20345", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019370819"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10971-012-2792-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019735647", 
              "https://doi.org/10.1007/s10971-012-2792-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12221-010-0731-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020364893", 
              "https://doi.org/10.1007/s12221-010-0731-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12221-010-0731-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020364893", 
              "https://doi.org/10.1007/s12221-010-0731-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7589-8_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021462309", 
              "https://doi.org/10.1007/978-1-4419-7589-8_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7589-8_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021462309", 
              "https://doi.org/10.1007/978-1-4419-7589-8_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7589-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022068021", 
              "https://doi.org/10.1007/978-1-4419-7589-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4419-7589-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022068021", 
              "https://doi.org/10.1007/978-1-4419-7589-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1155/2016/2495623", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1022781081"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.solidstatesciences.2007.11.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024635600"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0022-3093(95)00210-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024737767"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0001-8686(76)80004-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025446153"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0021-9797(92)90163-g", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025836295"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1020748727348", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026149393", 
              "https://doi.org/10.1023/a:1020748727348"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.rser.2014.03.017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030401722"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.matlet.2006.03.095", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032008157"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.micromeso.2016.11.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033142380"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.micromeso.2016.11.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033142380"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.micromeso.2016.11.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033142380"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s13233-014-2006-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039011145", 
              "https://doi.org/10.1007/s13233-014-2006-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.enbuild.2011.09.041", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043559682"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0022-3093(98)00102-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1047894542"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.matchemphys.2008.07.124", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049036684"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.micromeso.2015.07.019", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050108439"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/acs.chemmater.5b02801", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053957373"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/am302303b", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055142918"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/cm0101069", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055408402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/cm0101069", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055408402"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/j150331a003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055688888"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ja01145a126", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055773283"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ja01269a023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1055787826"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/jp5082643", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1056102954"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1115/1.2424237", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062079304"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10971-017-4312-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083537523", 
              "https://doi.org/10.1007/s10971-017-4312-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10971-017-4312-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083537523", 
              "https://doi.org/10.1007/s10971-017-4312-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/anie.201700836", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084008451"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.powtec.2017.10.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092239889"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.egypro.2017.09.607", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092335923"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1557/mre.2017.14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092441960"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/slct.201703000", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100714066"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jnoncrysol.2018.07.028", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105734911"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-01-11", 
        "datePublishedReg": "2019-01-11", 
        "description": "Silica aerogel is the archetypal thermal superinsulator and commonly applied to improve the thermal performance of on- and off-shore industrial infrastructure and buildings. Hitherto, the main products on the market are silica aerogel based blankets fabricated by casting a silica sol into a porous or fiber matrix, followed by gelation, hydrophobization, and (supercritical) drying. Considering the diffusion efficiency of the reagents and solvents used in the preparation, a reduced size of the gel bodies may accelerate and simplify the sol-gel, hydrophobization, and drying processes. Thus, particle based products, derived from silica aerogel granulate and powder additives and semi-finished products, are an attractive solution towards inexpensive aerogel applications. Here, we optimized the process parameters for silica aerogel powder production from three common silica precursors: waterglass (WG), ion-exchanged waterglass, and tetraethoxysilane (TEOS), including gelation pH, hydrophobization procedure, solvent system, processing temperatures, and ambient pressure drying protocol. Successful hydrophobization is confirmed by elemental analysis, FTIR and quantitative solid-state NMR spectroscopy. All three routes lead to silica aerogel powders with similar type IV isotherms, and BET surface areas above 700 m2/g. Importantly, the thermal conductivity of the packed powder beds does not exceed 20 mW/(m\u22c5K) for all three routes, implying even lower thermal conductivities of the aerogel phase itself. Total processing time, including gelation, aging, surface modification, and ambient pressure drying is between 2 and 4 h, depending on the selected route. Given that high quality silica aerogel powders can be produced from all investigated silica precursors and hydrophobization agents, the process selection for industrial upscaling can be based entirely on engineering and economic considerations.  Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS.All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m\u00b7K).The precursor can thus be selected based on availability, cost and process complexity. Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS. All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m\u00b7K). The precursor can thus be selected based on availability, cost and process complexity.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s10971-018-4879-4", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.5229403", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1048337", 
            "issn": [
              "0928-0707", 
              "1573-4846"
            ], 
            "name": "Journal of Sol-Gel Science and Technology", 
            "type": "Periodical"
          }
        ], 
        "name": "Three routes to superinsulating silica aerogel powder", 
        "pagination": "1-10", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "a1d0f25af2dbce2c2d8907dd785f078c0d173cb4328d52e77fe92e12e36fd6c3"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s10971-018-4879-4"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1111367798"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s10971-018-4879-4", 
          "https://app.dimensions.ai/details/publication/pub.1111367798"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T08:39", 
        "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/v90/0000000317_0000000317/records_116898_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs10971-018-4879-4"
      }
    ]
     

    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.10.1007/s10971-018-4879-4'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10971-018-4879-4'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10971-018-4879-4'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10971-018-4879-4'


     

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

    220 TRIPLES      21 PREDICATES      64 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s10971-018-4879-4 schema:about anzsrc-for:09
    2 anzsrc-for:0904
    3 schema:author N017022c86ff14d8b987629e39d26f81d
    4 schema:citation sg:pub.10.1007/978-1-4419-7589-8
    5 sg:pub.10.1007/978-1-4419-7589-8_21
    6 sg:pub.10.1007/s10971-012-2792-9
    7 sg:pub.10.1007/s10971-017-4312-4
    8 sg:pub.10.1007/s12221-010-0731-3
    9 sg:pub.10.1007/s13233-014-2006-0
    10 sg:pub.10.1023/a:1015309014546
    11 sg:pub.10.1023/a:1020748727348
    12 https://doi.org/10.1002/anie.201700836
    13 https://doi.org/10.1002/pc.20345
    14 https://doi.org/10.1002/slct.201703000
    15 https://doi.org/10.1016/0001-8686(76)80004-8
    16 https://doi.org/10.1016/0021-9797(92)90163-g
    17 https://doi.org/10.1016/0022-3093(95)00210-3
    18 https://doi.org/10.1016/j.apt.2015.01.002
    19 https://doi.org/10.1016/j.compositesa.2015.04.014
    20 https://doi.org/10.1016/j.egypro.2017.09.607
    21 https://doi.org/10.1016/j.enbuild.2011.09.041
    22 https://doi.org/10.1016/j.jallcom.2008.09.029
    23 https://doi.org/10.1016/j.jnoncrysol.2018.07.028
    24 https://doi.org/10.1016/j.matchemphys.2008.07.124
    25 https://doi.org/10.1016/j.matlet.2006.03.095
    26 https://doi.org/10.1016/j.micromeso.2007.10.030
    27 https://doi.org/10.1016/j.micromeso.2015.07.019
    28 https://doi.org/10.1016/j.micromeso.2016.11.037
    29 https://doi.org/10.1016/j.powtec.2017.10.022
    30 https://doi.org/10.1016/j.rser.2014.03.017
    31 https://doi.org/10.1016/j.solidstatesciences.2007.11.016
    32 https://doi.org/10.1016/s0022-3093(98)00102-1
    33 https://doi.org/10.1016/s0022-3093(98)00370-6
    34 https://doi.org/10.1021/acs.chemmater.5b02801
    35 https://doi.org/10.1021/am302303b
    36 https://doi.org/10.1021/cm0101069
    37 https://doi.org/10.1021/j150331a003
    38 https://doi.org/10.1021/ja01145a126
    39 https://doi.org/10.1021/ja01269a023
    40 https://doi.org/10.1021/jp5082643
    41 https://doi.org/10.1115/1.2424237
    42 https://doi.org/10.1155/2016/2495623
    43 https://doi.org/10.1557/mre.2017.14
    44 schema:datePublished 2019-01-11
    45 schema:datePublishedReg 2019-01-11
    46 schema:description Silica aerogel is the archetypal thermal superinsulator and commonly applied to improve the thermal performance of on- and off-shore industrial infrastructure and buildings. Hitherto, the main products on the market are silica aerogel based blankets fabricated by casting a silica sol into a porous or fiber matrix, followed by gelation, hydrophobization, and (supercritical) drying. Considering the diffusion efficiency of the reagents and solvents used in the preparation, a reduced size of the gel bodies may accelerate and simplify the sol-gel, hydrophobization, and drying processes. Thus, particle based products, derived from silica aerogel granulate and powder additives and semi-finished products, are an attractive solution towards inexpensive aerogel applications. Here, we optimized the process parameters for silica aerogel powder production from three common silica precursors: waterglass (WG), ion-exchanged waterglass, and tetraethoxysilane (TEOS), including gelation pH, hydrophobization procedure, solvent system, processing temperatures, and ambient pressure drying protocol. Successful hydrophobization is confirmed by elemental analysis, FTIR and quantitative solid-state NMR spectroscopy. All three routes lead to silica aerogel powders with similar type IV isotherms, and BET surface areas above 700 m2/g. Importantly, the thermal conductivity of the packed powder beds does not exceed 20 mW/(m⋅K) for all three routes, implying even lower thermal conductivities of the aerogel phase itself. Total processing time, including gelation, aging, surface modification, and ambient pressure drying is between 2 and 4 h, depending on the selected route. Given that high quality silica aerogel powders can be produced from all investigated silica precursors and hydrophobization agents, the process selection for industrial upscaling can be based entirely on engineering and economic considerations. Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS.All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m·K).The precursor can thus be selected based on availability, cost and process complexity. Silica aerogel powder was produced from waterglass, ion-exchanged waterglass and TEOS. All precursors lead to surface areas above 700 m2/g and thermal conductivities below 20 mW/(m·K). The precursor can thus be selected based on availability, cost and process complexity.
    47 schema:genre research_article
    48 schema:inLanguage en
    49 schema:isAccessibleForFree false
    50 schema:isPartOf sg:journal.1048337
    51 schema:name Three routes to superinsulating silica aerogel powder
    52 schema:pagination 1-10
    53 schema:productId N0b52a0e638664c39bd257ba35f74cabb
    54 N656495a8c1bd4a96bd0a2ced8d5331a2
    55 Na1adb01cc87549089e9259a7d65690a9
    56 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111367798
    57 https://doi.org/10.1007/s10971-018-4879-4
    58 schema:sdDatePublished 2019-04-11T08:39
    59 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    60 schema:sdPublisher N74ee6c8d110944429aac08dd39834785
    61 schema:url https://link.springer.com/10.1007%2Fs10971-018-4879-4
    62 sgo:license sg:explorer/license/
    63 sgo:sdDataset articles
    64 rdf:type schema:ScholarlyArticle
    65 N017022c86ff14d8b987629e39d26f81d rdf:first sg:person.014655665020.11
    66 rdf:rest Ne11464113f84433dbfb5ee38e99d66ff
    67 N0b52a0e638664c39bd257ba35f74cabb schema:name readcube_id
    68 schema:value a1d0f25af2dbce2c2d8907dd785f078c0d173cb4328d52e77fe92e12e36fd6c3
    69 rdf:type schema:PropertyValue
    70 N0dbe99fba9f9405287b9256b9ae83ee5 rdf:first sg:person.010001033415.50
    71 rdf:rest Nf601a1e3c8c541e0bf12f8b930edd88e
    72 N3f837c149b584ccb91c57b0efd2f8afd schema:name Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, Überlandstrasse 129, 8600, Dübendorf, Switzerland
    73 rdf:type schema:Organization
    74 N656495a8c1bd4a96bd0a2ced8d5331a2 schema:name dimensions_id
    75 schema:value pub.1111367798
    76 rdf:type schema:PropertyValue
    77 N74ee6c8d110944429aac08dd39834785 schema:name Springer Nature - SN SciGraph project
    78 rdf:type schema:Organization
    79 N80c72b7f9171433fa307d920573a1c4e schema:name Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, Überlandstrasse 129, 8600, Dübendorf, Switzerland
    80 rdf:type schema:Organization
    81 N85b9d92a42fc4a43a1f35c67592893bd schema:name Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, Überlandstrasse 129, 8600, Dübendorf, Switzerland
    82 rdf:type schema:Organization
    83 Na1adb01cc87549089e9259a7d65690a9 schema:name doi
    84 schema:value 10.1007/s10971-018-4879-4
    85 rdf:type schema:PropertyValue
    86 Nb5caa43d9fa14977a232437150ce4475 schema:name Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, Überlandstrasse 129, 8600, Dübendorf, Switzerland
    87 rdf:type schema:Organization
    88 Nc815acd0684f46abb8fe610746aefee8 rdf:first sg:person.01304260225.51
    89 rdf:rest rdf:nil
    90 Ne11464113f84433dbfb5ee38e99d66ff rdf:first sg:person.015367575177.51
    91 rdf:rest N0dbe99fba9f9405287b9256b9ae83ee5
    92 Ne5f42b24d05748faab1a495278f7db14 schema:name Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Science and Technology, Empa, Überlandstrasse 129, 8600, Dübendorf, Switzerland
    93 rdf:type schema:Organization
    94 Nf601a1e3c8c541e0bf12f8b930edd88e rdf:first sg:person.012241750014.12
    95 rdf:rest Nc815acd0684f46abb8fe610746aefee8
    96 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
    97 schema:name Engineering
    98 rdf:type schema:DefinedTerm
    99 anzsrc-for:0904 schema:inDefinedTermSet anzsrc-for:
    100 schema:name Chemical Engineering
    101 rdf:type schema:DefinedTerm
    102 sg:grant.5229403 http://pending.schema.org/fundedItem sg:pub.10.1007/s10971-018-4879-4
    103 rdf:type schema:MonetaryGrant
    104 sg:journal.1048337 schema:issn 0928-0707
    105 1573-4846
    106 schema:name Journal of Sol-Gel Science and Technology
    107 rdf:type schema:Periodical
    108 sg:person.010001033415.50 schema:affiliation N80c72b7f9171433fa307d920573a1c4e
    109 schema:familyName Angelica
    110 schema:givenName Emanuele
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010001033415.50
    112 rdf:type schema:Person
    113 sg:person.012241750014.12 schema:affiliation Nb5caa43d9fa14977a232437150ce4475
    114 schema:familyName Malfait
    115 schema:givenName Wim J.
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012241750014.12
    117 rdf:type schema:Person
    118 sg:person.01304260225.51 schema:affiliation Ne5f42b24d05748faab1a495278f7db14
    119 schema:familyName Koebel
    120 schema:givenName Matthias M.
    121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01304260225.51
    122 rdf:type schema:Person
    123 sg:person.014655665020.11 schema:affiliation N3f837c149b584ccb91c57b0efd2f8afd
    124 schema:familyName Stojanovic
    125 schema:givenName Ana
    126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014655665020.11
    127 rdf:type schema:Person
    128 sg:person.015367575177.51 schema:affiliation N85b9d92a42fc4a43a1f35c67592893bd
    129 schema:familyName Zhao
    130 schema:givenName Shanyu
    131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015367575177.51
    132 rdf:type schema:Person
    133 sg:pub.10.1007/978-1-4419-7589-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022068021
    134 https://doi.org/10.1007/978-1-4419-7589-8
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1007/978-1-4419-7589-8_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021462309
    137 https://doi.org/10.1007/978-1-4419-7589-8_21
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1007/s10971-012-2792-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019735647
    140 https://doi.org/10.1007/s10971-012-2792-9
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/s10971-017-4312-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083537523
    143 https://doi.org/10.1007/s10971-017-4312-4
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s12221-010-0731-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020364893
    146 https://doi.org/10.1007/s12221-010-0731-3
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s13233-014-2006-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039011145
    149 https://doi.org/10.1007/s13233-014-2006-0
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1023/a:1015309014546 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004372124
    152 https://doi.org/10.1023/a:1015309014546
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1023/a:1020748727348 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026149393
    155 https://doi.org/10.1023/a:1020748727348
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1002/anie.201700836 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084008451
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1002/pc.20345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019370819
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1002/slct.201703000 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100714066
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1016/0001-8686(76)80004-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025446153
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1016/0021-9797(92)90163-g schema:sameAs https://app.dimensions.ai/details/publication/pub.1025836295
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1016/0022-3093(95)00210-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024737767
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1016/j.apt.2015.01.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002987459
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1016/j.compositesa.2015.04.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018772499
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1016/j.egypro.2017.09.607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092335923
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1016/j.enbuild.2011.09.041 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043559682
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1016/j.jallcom.2008.09.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005423658
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1016/j.jnoncrysol.2018.07.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105734911
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1016/j.matchemphys.2008.07.124 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049036684
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1016/j.matlet.2006.03.095 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032008157
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1016/j.micromeso.2007.10.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014770616
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1016/j.micromeso.2015.07.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050108439
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1016/j.micromeso.2016.11.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033142380
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1016/j.powtec.2017.10.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092239889
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1016/j.rser.2014.03.017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030401722
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1016/j.solidstatesciences.2007.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024635600
    196 rdf:type schema:CreativeWork
    197 https://doi.org/10.1016/s0022-3093(98)00102-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047894542
    198 rdf:type schema:CreativeWork
    199 https://doi.org/10.1016/s0022-3093(98)00370-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016484041
    200 rdf:type schema:CreativeWork
    201 https://doi.org/10.1021/acs.chemmater.5b02801 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053957373
    202 rdf:type schema:CreativeWork
    203 https://doi.org/10.1021/am302303b schema:sameAs https://app.dimensions.ai/details/publication/pub.1055142918
    204 rdf:type schema:CreativeWork
    205 https://doi.org/10.1021/cm0101069 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055408402
    206 rdf:type schema:CreativeWork
    207 https://doi.org/10.1021/j150331a003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055688888
    208 rdf:type schema:CreativeWork
    209 https://doi.org/10.1021/ja01145a126 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055773283
    210 rdf:type schema:CreativeWork
    211 https://doi.org/10.1021/ja01269a023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055787826
    212 rdf:type schema:CreativeWork
    213 https://doi.org/10.1021/jp5082643 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056102954
    214 rdf:type schema:CreativeWork
    215 https://doi.org/10.1115/1.2424237 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062079304
    216 rdf:type schema:CreativeWork
    217 https://doi.org/10.1155/2016/2495623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022781081
    218 rdf:type schema:CreativeWork
    219 https://doi.org/10.1557/mre.2017.14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092441960
    220 rdf:type schema:CreativeWork
     




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


    ...