Ontology type: schema:Chapter
2011-08-04
AUTHORS ABSTRACTThis chapter discusses a number of conventional and advanced techniques in transmission electron microscopy used for the visualization of structural aspects of disorder and strain-induced complexity in a selection of real materials. Most examples relate to shape memory materials such as and and some to plasticity in bulk and thin films. The techniques are chosen in view of existing or potential quantitative output such as Geometric Phase Imaging based on atomic resolution images, statistical parameter estimation, tomography, and conical dark-field imaging. Clearly, this overview does not provide a complete list of present day methods for high-resolution imaging, but it should give the reader a flavour of the possibilities and potentials of transmission electron microscopy for the quantitative study of complex materials.The study of materials can be conducted on many length scales and by many different techniques and methods. For visualization techniques, despite efforts on multi-scale exercises, often the scale of the details aimed for relates closely to the dimensions of the device in mind or at most one order of magnitude smaller. A typical example of macroscopic imaging techniques is automated camera-assisted strain measurements using surface labelling techniques. Correlations between macroscopic properties and much smaller dimensions, e.g., at the nano-level, often still suffer from serious gaps in connecting results from different length scales. For functional materials, however, with properties sensitive to a change in the environment such as temperature, pressure, electric field, magnetic field, and chemical interactions, the working dimensions often immediately fall within the micro- or nano-scale so that no or little scale differences exist between the properties and the high-resolution imaging techniques. Moreover, the continuing evolution towards miniaturization of devices from functional materials even further calls for special imaging techniques with very high spatial resolution.In this chapter, the focus is on atomic or high-resolution transmission electron microscopy (HRTEM) used to collect data on a variety of real materials and problems, with the emphasis on shape memory materials. Some examples also include spectroscopic data from energy-dispersive X-ray analysis (EDX) or electron energy loss spectroscopy (EELS) and novel TEM techniques. More... »
PAGES135-149
Disorder and Strain-Induced Complexity in Functional Materials
ISBN
978-3-642-20942-0
978-3-642-20943-7
http://scigraph.springernature.com/pub.10.1007/978-3-642-20943-7_8
DOIhttp://dx.doi.org/10.1007/978-3-642-20943-7_8
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1040819395
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/2008/0299",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Other Physical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Physical Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"EMAT, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium"
],
"type": "Organization"
},
"familyName": "Schryvers",
"givenName": "D.",
"id": "sg:person.013773701247.34",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013773701247.34"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "University of Antwerp",
"id": "https://www.grid.ac/institutes/grid.5284.b",
"name": [
"EMAT, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium"
],
"type": "Organization"
},
"familyName": "Van Aert",
"givenName": "S.",
"id": "sg:person.0611163576.39",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0611163576.39"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.scriptamat.2008.10.025",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003482694"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.tsf.2009.06.062",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003514321"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1359-6454(03)00116-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005949197"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1359-6454(03)00116-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005949197"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.scriptamat.2010.01.048",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006756526"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ultramic.2009.05.010",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007427428"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.actamat.2004.10.049",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009955050"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/01418618308234902",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010103040"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s1359-6454(96)00180-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010827861"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/14786430903074755",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013079778"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0304-3991(90)90002-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014467432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0304-3991(90)90002-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014467432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0304-3991(01)00136-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014702880"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0966-9795(97)00091-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019191697"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1107/s0108768106036457",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019382804"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2320/matertrans1960.27.731",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019547639"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/01418619808241907",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1021598466"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.scriptamat.2005.02.013",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025671072"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.actamat.2010.04.046",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1032407887"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/s0304-3991(98)00035-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038531199"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ijplas.2010.05.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1039287112"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/adma.201004160",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040076504"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ultramic.2005.03.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041935182"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.actamat.2006.08.050",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042261760"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1088/0964-1726/18/11/115018",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042420605"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1088/0964-1726/18/11/115018",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1042420605"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmat2488",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044380527",
"https://doi.org/10.1038/nmat2488"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmat2488",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044380527",
"https://doi.org/10.1038/nmat2488"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ultramic.2007.03.004",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046111675"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmat1593",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047067684",
"https://doi.org/10.1038/nmat1593"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nmat1593",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1047067684",
"https://doi.org/10.1038/nmat1593"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.actamat.2007.10.001",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048170941"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1107/s0108767397010489",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049101301"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.actamat.2009.05.034",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051102702"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/j.ultramic.2009.06.002",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051821941"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0038-1098(85)90576-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053650432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1016/0038-1098(85)90576-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1053650432"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1093/oxfordjournals.jmicro.a023805",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1059962128"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.14.4030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060521697"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.14.4030",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060521697"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.39.1535",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060549031"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.39.1535",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060549031"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.41.11319",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060553071"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.41.11319",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060553071"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.44.9301",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060560278"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevb.44.9301",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060560278"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.100.165707",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060753328"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.100.165707",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060753328"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.96.096106",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060831880"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1103/physrevlett.96.096106",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1060831880"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1109/jmems.2009.2020380",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1061290698"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.2320/jinstmet1952.54.8_861",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1084977085"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9780470173862",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098661788"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/9780470173862",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1098661788"
],
"type": "CreativeWork"
}
],
"datePublished": "2011-08-04",
"datePublishedReg": "2011-08-04",
"description": "This chapter discusses a number of conventional and advanced techniques in transmission electron microscopy used for the visualization of structural aspects of disorder and strain-induced complexity in a selection of real materials. Most examples relate to shape memory materials such as and and some to plasticity in bulk and thin films. The techniques are chosen in view of existing or potential quantitative output such as Geometric Phase Imaging based on atomic resolution images, statistical parameter estimation, tomography, and conical dark-field imaging. Clearly, this overview does not provide a complete list of present day methods for high-resolution imaging, but it should give the reader a flavour of the possibilities and potentials of transmission electron microscopy for the quantitative study of complex materials.The study of materials can be conducted on many length scales and by many different techniques and methods. For visualization techniques, despite efforts on multi-scale exercises, often the scale of the details aimed for relates closely to the dimensions of the device in mind or at most one order of magnitude smaller. A typical example of macroscopic imaging techniques is automated camera-assisted strain measurements using surface labelling techniques. Correlations between macroscopic properties and much smaller dimensions, e.g., at the nano-level, often still suffer from serious gaps in connecting results from different length scales. For functional materials, however, with properties sensitive to a change in the environment such as temperature, pressure, electric field, magnetic field, and chemical interactions, the working dimensions often immediately fall within the micro- or nano-scale so that no or little scale differences exist between the properties and the high-resolution imaging techniques. Moreover, the continuing evolution towards miniaturization of devices from functional materials even further calls for special imaging techniques with very high spatial resolution.In this chapter, the focus is on atomic or high-resolution transmission electron microscopy (HRTEM) used to collect data on a variety of real materials and problems, with the emphasis on shape memory materials. Some examples also include spectroscopic data from energy-dispersive X-ray analysis (EDX) or electron energy loss spectroscopy (EELS) and novel TEM techniques.",
"editor": [
{
"familyName": "Kakeshita",
"givenName": "Tomoyuki",
"type": "Person"
},
{
"familyName": "Fukuda",
"givenName": "Takashi",
"type": "Person"
},
{
"familyName": "Saxena",
"givenName": "Avadh",
"type": "Person"
},
{
"familyName": "Planes",
"givenName": "Antoni",
"type": "Person"
}
],
"genre": "chapter",
"id": "sg:pub.10.1007/978-3-642-20943-7_8",
"inLanguage": [
"en"
],
"isAccessibleForFree": false,
"isPartOf": {
"isbn": [
"978-3-642-20942-0",
"978-3-642-20943-7"
],
"name": "Disorder and Strain-Induced Complexity in Functional Materials",
"type": "Book"
},
"name": "High-Resolution Visualization Techniques: Structural Aspects",
"pagination": "135-149",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1040819395"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/978-3-642-20943-7_8"
]
},
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"c272a50cd83d37ef20726b59c7e95d81f995822ae832e716ae5a4f1b027f52fe"
]
}
],
"publisher": {
"location": "Berlin, Heidelberg",
"name": "Springer Berlin Heidelberg",
"type": "Organisation"
},
"sameAs": [
"https://doi.org/10.1007/978-3-642-20943-7_8",
"https://app.dimensions.ai/details/publication/pub.1040819395"
],
"sdDataset": "chapters",
"sdDatePublished": "2019-04-16T09:36",
"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/v90/0000000373_0000000373/records_13099_00000001.jsonl",
"type": "Chapter",
"url": "https://link.springer.com/10.1007%2F978-3-642-20943-7_8"
}
]
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.10.1007/978-3-642-20943-7_8'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20943-7_8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20943-7_8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20943-7_8'
This table displays all metadata directly associated to this object as RDF triples.
212 TRIPLES
23 PREDICATES
67 URIs
19 LITERALS
8 BLANK NODES