Mathematical Modeling of Nanosensor Systems Based on Dynamic Light Scattering View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-07

AUTHORS

A. D. Levin, I. S. Filimonov, M. K. Alenichev, T. A. Goidina

ABSTRACT

A mathematical model describing optical nanosensors, the principle of action of which is based on an increase in the hydrodynamic diameters of functionalized nanoparticles (conjugates) under the influence of an analyte, is constructed. The formation of shells from analyte molecules around conjugates and the aggregation of conjugates to dimers at the expense of “bridges” represented by analyte molecules are considered. Antibodies capable of association with antibody footprints (epitopes) on analyte molecules are considered receptors, which are used for the functionalization of nanoparticles. The input parameters of the model are the sizes and concentrations of conjugates, kinetic constants of association and dissociation of receptors with epitopes, number of receptors per conjugate, and the concentration of the analyte. The model makes it possible to estimate the ranges of defined concentrations, as well as detect the limits during the development of nanosensors for specific analytes and optimizing parameters of these sensors, including the required incubation time for a mixture of conjugates with an analyte. More... »

PAGES

406-413

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s1995078018040092

DOI

http://dx.doi.org/10.1134/s1995078018040092

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111224450


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/2008/0303", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Macromolecular and Materials Chemistry", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Chemical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "All-Russian Research Institute for Optical and Physical Measurements", 
          "id": "https://www.grid.ac/institutes/grid.469899.3", 
          "name": [
            "All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Levin", 
        "givenName": "A. D.", 
        "id": "sg:person.012142476661.02", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012142476661.02"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "All-Russian Research Institute for Optical and Physical Measurements", 
          "id": "https://www.grid.ac/institutes/grid.469899.3", 
          "name": [
            "All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Filimonov", 
        "givenName": "I. S.", 
        "id": "sg:person.01067556517.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01067556517.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "All-Russian Research Institute for Optical and Physical Measurements", 
          "id": "https://www.grid.ac/institutes/grid.469899.3", 
          "name": [
            "All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alenichev", 
        "givenName": "M. K.", 
        "id": "sg:person.010562134434.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010562134434.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Bauman Moscow State Technical University", 
          "id": "https://www.grid.ac/institutes/grid.61569.3d", 
          "name": [
            "All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia", 
            "Bauman Moscow State Technical University, 105005, Moscow, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Goidina", 
        "givenName": "T. A.", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1186/1479-5876-10-44", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001195837", 
          "https://doi.org/10.1186/1479-5876-10-44"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c6cc02633h", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002233127"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c1an15303j", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003283578"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/ppsc.201400158", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012346344"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c5ay00674k", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013535731"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4172/2155-9821.1000287", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015637084"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.4892163", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023973374"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.bios.2014.11.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024366421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.aca.2011.04.001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025144555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/srep18293", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035713863", 
          "https://doi.org/10.1038/srep18293"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ac501536z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055017301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acsami.5b00371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055126343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja711298b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055857625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ja711298b", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055857625"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/la960326e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056167577"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/la960326e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056167577"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1039/c7nr03096g", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085432883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/acsinfecdis.7b00137", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091827223"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-07", 
    "datePublishedReg": "2018-07-01", 
    "description": "A mathematical model describing optical nanosensors, the principle of action of which is based on an increase in the hydrodynamic diameters of functionalized nanoparticles (conjugates) under the influence of an analyte, is constructed. The formation of shells from analyte molecules around conjugates and the aggregation of conjugates to dimers at the expense of \u201cbridges\u201d represented by analyte molecules are considered. Antibodies capable of association with antibody footprints (epitopes) on analyte molecules are considered receptors, which are used for the functionalization of nanoparticles. The input parameters of the model are the sizes and concentrations of conjugates, kinetic constants of association and dissociation of receptors with epitopes, number of receptors per conjugate, and the concentration of the analyte. The model makes it possible to estimate the ranges of defined concentrations, as well as detect the limits during the development of nanosensors for specific analytes and optimizing parameters of these sensors, including the required incubation time for a mixture of conjugates with an analyte.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1134/s1995078018040092", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1052540", 
        "issn": [
          "1995-0780", 
          "1995-0799"
        ], 
        "name": "Nanotechnologies in Russia", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7-8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "name": "Mathematical Modeling of Nanosensor Systems Based on Dynamic Light Scattering", 
    "pagination": "406-413", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "89170add870b8d48eb25e9a965229098d539ccf7a83a2cd249decf41411c8072"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1134/s1995078018040092"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1111224450"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1134/s1995078018040092", 
      "https://app.dimensions.ai/details/publication/pub.1111224450"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T08:36", 
    "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/v90/0000000314_0000000314/records_55853_00000000.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1134%2FS1995078018040092"
  }
]
 

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.10.1134/s1995078018040092'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1134/s1995078018040092'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1134/s1995078018040092'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1134/s1995078018040092'


 

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

135 TRIPLES      21 PREDICATES      43 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1134/s1995078018040092 schema:about anzsrc-for:03
2 anzsrc-for:0303
3 schema:author N8a66cc211a5343f3ace16d3447c7d20f
4 schema:citation sg:pub.10.1038/srep18293
5 sg:pub.10.1186/1479-5876-10-44
6 https://doi.org/10.1002/ppsc.201400158
7 https://doi.org/10.1016/j.aca.2011.04.001
8 https://doi.org/10.1016/j.bios.2014.11.016
9 https://doi.org/10.1021/ac501536z
10 https://doi.org/10.1021/acsami.5b00371
11 https://doi.org/10.1021/acsinfecdis.7b00137
12 https://doi.org/10.1021/ja711298b
13 https://doi.org/10.1021/la960326e
14 https://doi.org/10.1039/c1an15303j
15 https://doi.org/10.1039/c5ay00674k
16 https://doi.org/10.1039/c6cc02633h
17 https://doi.org/10.1039/c7nr03096g
18 https://doi.org/10.1063/1.4892163
19 https://doi.org/10.4172/2155-9821.1000287
20 schema:datePublished 2018-07
21 schema:datePublishedReg 2018-07-01
22 schema:description A mathematical model describing optical nanosensors, the principle of action of which is based on an increase in the hydrodynamic diameters of functionalized nanoparticles (conjugates) under the influence of an analyte, is constructed. The formation of shells from analyte molecules around conjugates and the aggregation of conjugates to dimers at the expense of “bridges” represented by analyte molecules are considered. Antibodies capable of association with antibody footprints (epitopes) on analyte molecules are considered receptors, which are used for the functionalization of nanoparticles. The input parameters of the model are the sizes and concentrations of conjugates, kinetic constants of association and dissociation of receptors with epitopes, number of receptors per conjugate, and the concentration of the analyte. The model makes it possible to estimate the ranges of defined concentrations, as well as detect the limits during the development of nanosensors for specific analytes and optimizing parameters of these sensors, including the required incubation time for a mixture of conjugates with an analyte.
23 schema:genre research_article
24 schema:inLanguage en
25 schema:isAccessibleForFree false
26 schema:isPartOf N18b3cf6348fa4108b4e278901df09dfe
27 Nc8845a0344224f3486f38fdcdafde407
28 sg:journal.1052540
29 schema:name Mathematical Modeling of Nanosensor Systems Based on Dynamic Light Scattering
30 schema:pagination 406-413
31 schema:productId N33fd935148534fce83ca5f6b14f08573
32 N5302eeb8a22a42b3bf31b9da0eace0d3
33 Ne5c946c7651a4d7c824131939dcc066f
34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111224450
35 https://doi.org/10.1134/s1995078018040092
36 schema:sdDatePublished 2019-04-11T08:36
37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
38 schema:sdPublisher N14f96cd20aeb44fdb89ba784ab0995cd
39 schema:url https://link.springer.com/10.1134%2FS1995078018040092
40 sgo:license sg:explorer/license/
41 sgo:sdDataset articles
42 rdf:type schema:ScholarlyArticle
43 N14f96cd20aeb44fdb89ba784ab0995cd schema:name Springer Nature - SN SciGraph project
44 rdf:type schema:Organization
45 N18b3cf6348fa4108b4e278901df09dfe schema:volumeNumber 13
46 rdf:type schema:PublicationVolume
47 N33fd935148534fce83ca5f6b14f08573 schema:name dimensions_id
48 schema:value pub.1111224450
49 rdf:type schema:PropertyValue
50 N5302eeb8a22a42b3bf31b9da0eace0d3 schema:name doi
51 schema:value 10.1134/s1995078018040092
52 rdf:type schema:PropertyValue
53 N60a7087245ee4a2d9988ffc2ac31bb1f schema:affiliation https://www.grid.ac/institutes/grid.61569.3d
54 schema:familyName Goidina
55 schema:givenName T. A.
56 rdf:type schema:Person
57 N612f3ed33e454c00bd58ca28fe8a965b rdf:first N60a7087245ee4a2d9988ffc2ac31bb1f
58 rdf:rest rdf:nil
59 N7f51730feaf747e8b01d7116ed00a637 rdf:first sg:person.010562134434.59
60 rdf:rest N612f3ed33e454c00bd58ca28fe8a965b
61 N8a66cc211a5343f3ace16d3447c7d20f rdf:first sg:person.012142476661.02
62 rdf:rest Nfcfb892146764a6d853b92524108edea
63 Nc8845a0344224f3486f38fdcdafde407 schema:issueNumber 7-8
64 rdf:type schema:PublicationIssue
65 Ne5c946c7651a4d7c824131939dcc066f schema:name readcube_id
66 schema:value 89170add870b8d48eb25e9a965229098d539ccf7a83a2cd249decf41411c8072
67 rdf:type schema:PropertyValue
68 Nfcfb892146764a6d853b92524108edea rdf:first sg:person.01067556517.07
69 rdf:rest N7f51730feaf747e8b01d7116ed00a637
70 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
71 schema:name Chemical Sciences
72 rdf:type schema:DefinedTerm
73 anzsrc-for:0303 schema:inDefinedTermSet anzsrc-for:
74 schema:name Macromolecular and Materials Chemistry
75 rdf:type schema:DefinedTerm
76 sg:journal.1052540 schema:issn 1995-0780
77 1995-0799
78 schema:name Nanotechnologies in Russia
79 rdf:type schema:Periodical
80 sg:person.010562134434.59 schema:affiliation https://www.grid.ac/institutes/grid.469899.3
81 schema:familyName Alenichev
82 schema:givenName M. K.
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010562134434.59
84 rdf:type schema:Person
85 sg:person.01067556517.07 schema:affiliation https://www.grid.ac/institutes/grid.469899.3
86 schema:familyName Filimonov
87 schema:givenName I. S.
88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01067556517.07
89 rdf:type schema:Person
90 sg:person.012142476661.02 schema:affiliation https://www.grid.ac/institutes/grid.469899.3
91 schema:familyName Levin
92 schema:givenName A. D.
93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012142476661.02
94 rdf:type schema:Person
95 sg:pub.10.1038/srep18293 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035713863
96 https://doi.org/10.1038/srep18293
97 rdf:type schema:CreativeWork
98 sg:pub.10.1186/1479-5876-10-44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001195837
99 https://doi.org/10.1186/1479-5876-10-44
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1002/ppsc.201400158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012346344
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1016/j.aca.2011.04.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025144555
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1016/j.bios.2014.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024366421
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1021/ac501536z schema:sameAs https://app.dimensions.ai/details/publication/pub.1055017301
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1021/acsami.5b00371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055126343
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1021/acsinfecdis.7b00137 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091827223
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1021/ja711298b schema:sameAs https://app.dimensions.ai/details/publication/pub.1055857625
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1021/la960326e schema:sameAs https://app.dimensions.ai/details/publication/pub.1056167577
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1039/c1an15303j schema:sameAs https://app.dimensions.ai/details/publication/pub.1003283578
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1039/c5ay00674k schema:sameAs https://app.dimensions.ai/details/publication/pub.1013535731
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1039/c6cc02633h schema:sameAs https://app.dimensions.ai/details/publication/pub.1002233127
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1039/c7nr03096g schema:sameAs https://app.dimensions.ai/details/publication/pub.1085432883
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1063/1.4892163 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023973374
126 rdf:type schema:CreativeWork
127 https://doi.org/10.4172/2155-9821.1000287 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015637084
128 rdf:type schema:CreativeWork
129 https://www.grid.ac/institutes/grid.469899.3 schema:alternateName All-Russian Research Institute for Optical and Physical Measurements
130 schema:name All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia
131 rdf:type schema:Organization
132 https://www.grid.ac/institutes/grid.61569.3d schema:alternateName Bauman Moscow State Technical University
133 schema:name All-Russian Research Institute for Optical and Physical Measurements, 119361, Moscow, Russia
134 Bauman Moscow State Technical University, 105005, Moscow, Russia
135 rdf:type schema:Organization
 




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


...