Monte Carlo Simulation Studies of Conformational Properties of Polyelectrolytes with Maleic Acid Units View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1995-05

AUTHORS

Yuji Hirose, Minoru Onodera, Seigou Kawaguchi, Koichi Ito

ABSTRACT

Conformational properties of polyelectrolyte chain with maleic acid units (MA polyelectrolyte) are investigated by a mean of Monte Carlo simulation. The polyelectrolyte chains are modeled as a self-avoiding walk on tetrahedral lattice with charges fixed. The each charge interacts through Debye–Hückel potential and attraction energy from hydrogen bonding between un-ionized and ionized carboxyl groups in short-range. Mean-square end-to-end distance, 〈R2〉, mean-square radius of gyration, 〈S2〉, and mean conformational energy, 〈E〉, are simulated as a function of degree of polymerization (N) and dissociation (α), and salt concentration (CS). The dependence of 〈R2〉 and 〈S2〉 on N shows that MA polyelectrolyte chain assumes a rod like conformation at high α and low CS. The simulation results provide an interpretation for characteristic viscometric behavior of MA polyelectrolytes which show a maximum in an intrinsic viscosity nearly at α=0.5. The polymer dimensions in the region of α≤0.5 increases with the energy of the hydrogen bonding assumed. The characteristic viscometric behavior of MA polyelectrolytes is deduced to result from the balance between repulsion from the electrostatic interaction and attraction from the hydrogen bonding in short-range. More... »

PAGES

pj199569

Identifiers

URI

http://scigraph.springernature.com/pub.10.1295/polymj.27.519

DOI

http://dx.doi.org/10.1295/polymj.27.519

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016926893


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": [
      {
        "familyName": "Hirose", 
        "givenName": "Yuji", 
        "id": "sg:person.07513273103.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07513273103.95"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Onodera", 
        "givenName": "Minoru", 
        "type": "Person"
      }, 
      {
        "familyName": "Kawaguchi", 
        "givenName": "Seigou", 
        "id": "sg:person.014177203151.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014177203151.25"
        ], 
        "type": "Person"
      }, 
      {
        "familyName": "Ito", 
        "givenName": "Koichi", 
        "id": "sg:person.014344077306.74", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014344077306.74"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/pol.1973.170110208", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006530165"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pol.1978.180160405", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008965483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/pol.1953.120110503", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017391966"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/j100699a005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055676546"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/j100867a070", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1055683507"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00011a025", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056172828"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00022a020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056173372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00030a014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056173791"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00064a014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056175519"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00173a028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056181490"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00176a030", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056181685"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00205a007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056183662"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma00224a010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056184485"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1021/ma60059a011", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1056201040"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1672157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057748618"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.1699114", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1057769646"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.431156", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058009188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.455368", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058033380"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.457478", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058035490"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.458204", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058036216"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1063/1.461578", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058039589"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1995-05", 
    "datePublishedReg": "1995-05-01", 
    "description": "Conformational properties of polyelectrolyte chain with maleic acid units (MA polyelectrolyte) are investigated by a mean of Monte Carlo simulation. The polyelectrolyte chains are modeled as a self-avoiding walk on tetrahedral lattice with charges fixed. The each charge interacts through Debye\u2013H\u00fcckel potential and attraction energy from hydrogen bonding between un-ionized and ionized carboxyl groups in short-range. Mean-square end-to-end distance, \u3008R2\u3009, mean-square radius of gyration, \u3008S2\u3009, and mean conformational energy, \u3008E\u3009, are simulated as a function of degree of polymerization (N) and dissociation (\u03b1), and salt concentration (CS). The dependence of \u3008R2\u3009 and \u3008S2\u3009 on N shows that MA polyelectrolyte chain assumes a rod like conformation at high \u03b1 and low CS. The simulation results provide an interpretation for characteristic viscometric behavior of MA polyelectrolytes which show a maximum in an intrinsic viscosity nearly at \u03b1=0.5. The polymer dimensions in the region of \u03b1\u22640.5 increases with the energy of the hydrogen bonding assumed. The characteristic viscometric behavior of MA polyelectrolytes is deduced to result from the balance between repulsion from the electrostatic interaction and attraction from the hydrogen bonding in short-range.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1295/polymj.27.519", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1043822", 
        "issn": [
          "0032-3896", 
          "1349-0540"
        ], 
        "name": "Polymer Journal", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "27"
      }
    ], 
    "name": "Monte Carlo Simulation Studies of Conformational Properties of Polyelectrolytes with Maleic Acid Units", 
    "pagination": "pj199569", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "50a5d5b894f2ef736e082c37ab715ab72241a3c63de2648e4f6a635a89817662"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1295/polymj.27.519"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016926893"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1295/polymj.27.519", 
      "https://app.dimensions.ai/details/publication/pub.1016926893"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T13:55", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8660_00000424.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://www.nature.com/articles/pj199569"
  }
]
 

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.10.1295/polymj.27.519'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1295/polymj.27.519'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1295/polymj.27.519'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1295/polymj.27.519'


 

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

137 TRIPLES      21 PREDICATES      48 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1295/polymj.27.519 schema:about anzsrc-for:03
2 anzsrc-for:0303
3 schema:author Nc08a46ed659f422380fe3738d22d2ef2
4 schema:citation https://doi.org/10.1002/pol.1953.120110503
5 https://doi.org/10.1002/pol.1973.170110208
6 https://doi.org/10.1002/pol.1978.180160405
7 https://doi.org/10.1021/j100699a005
8 https://doi.org/10.1021/j100867a070
9 https://doi.org/10.1021/ma00011a025
10 https://doi.org/10.1021/ma00022a020
11 https://doi.org/10.1021/ma00030a014
12 https://doi.org/10.1021/ma00064a014
13 https://doi.org/10.1021/ma00173a028
14 https://doi.org/10.1021/ma00176a030
15 https://doi.org/10.1021/ma00205a007
16 https://doi.org/10.1021/ma00224a010
17 https://doi.org/10.1021/ma60059a011
18 https://doi.org/10.1063/1.1672157
19 https://doi.org/10.1063/1.1699114
20 https://doi.org/10.1063/1.431156
21 https://doi.org/10.1063/1.455368
22 https://doi.org/10.1063/1.457478
23 https://doi.org/10.1063/1.458204
24 https://doi.org/10.1063/1.461578
25 schema:datePublished 1995-05
26 schema:datePublishedReg 1995-05-01
27 schema:description Conformational properties of polyelectrolyte chain with maleic acid units (MA polyelectrolyte) are investigated by a mean of Monte Carlo simulation. The polyelectrolyte chains are modeled as a self-avoiding walk on tetrahedral lattice with charges fixed. The each charge interacts through Debye–Hückel potential and attraction energy from hydrogen bonding between un-ionized and ionized carboxyl groups in short-range. Mean-square end-to-end distance, 〈R2〉, mean-square radius of gyration, 〈S2〉, and mean conformational energy, 〈E〉, are simulated as a function of degree of polymerization (N) and dissociation (α), and salt concentration (CS). The dependence of 〈R2〉 and 〈S2〉 on N shows that MA polyelectrolyte chain assumes a rod like conformation at high α and low CS. The simulation results provide an interpretation for characteristic viscometric behavior of MA polyelectrolytes which show a maximum in an intrinsic viscosity nearly at α=0.5. The polymer dimensions in the region of α≤0.5 increases with the energy of the hydrogen bonding assumed. The characteristic viscometric behavior of MA polyelectrolytes is deduced to result from the balance between repulsion from the electrostatic interaction and attraction from the hydrogen bonding in short-range.
28 schema:genre research_article
29 schema:inLanguage en
30 schema:isAccessibleForFree true
31 schema:isPartOf N02b36effc6374fb5811ce1100fe711a8
32 N944016a7188148729425dbd1b7b73078
33 sg:journal.1043822
34 schema:name Monte Carlo Simulation Studies of Conformational Properties of Polyelectrolytes with Maleic Acid Units
35 schema:pagination pj199569
36 schema:productId N483e4d3f5aa0467a85c9f27a6ab52d10
37 Na6df84accf3b4625b38aaa6656ab2da0
38 Nc6ec97776c474a049bc400175b841191
39 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016926893
40 https://doi.org/10.1295/polymj.27.519
41 schema:sdDatePublished 2019-04-10T13:55
42 schema:sdLicense https://scigraph.springernature.com/explorer/license/
43 schema:sdPublisher N1d2bb7183920473cb4ba32816f31f7b7
44 schema:url http://www.nature.com/articles/pj199569
45 sgo:license sg:explorer/license/
46 sgo:sdDataset articles
47 rdf:type schema:ScholarlyArticle
48 N02b36effc6374fb5811ce1100fe711a8 schema:volumeNumber 27
49 rdf:type schema:PublicationVolume
50 N1d2bb7183920473cb4ba32816f31f7b7 schema:name Springer Nature - SN SciGraph project
51 rdf:type schema:Organization
52 N483e4d3f5aa0467a85c9f27a6ab52d10 schema:name readcube_id
53 schema:value 50a5d5b894f2ef736e082c37ab715ab72241a3c63de2648e4f6a635a89817662
54 rdf:type schema:PropertyValue
55 N635c185fc396462b9b8769379956b131 schema:familyName Onodera
56 schema:givenName Minoru
57 rdf:type schema:Person
58 N8df14a48eed942f7956187b457c62f4b rdf:first N635c185fc396462b9b8769379956b131
59 rdf:rest Nd5bcbe7bb8a44ff9b03ba56b510da815
60 N8fce5a9b47504e3cb0d589677d6cef33 rdf:first sg:person.014344077306.74
61 rdf:rest rdf:nil
62 N944016a7188148729425dbd1b7b73078 schema:issueNumber 5
63 rdf:type schema:PublicationIssue
64 Na6df84accf3b4625b38aaa6656ab2da0 schema:name dimensions_id
65 schema:value pub.1016926893
66 rdf:type schema:PropertyValue
67 Nc08a46ed659f422380fe3738d22d2ef2 rdf:first sg:person.07513273103.95
68 rdf:rest N8df14a48eed942f7956187b457c62f4b
69 Nc6ec97776c474a049bc400175b841191 schema:name doi
70 schema:value 10.1295/polymj.27.519
71 rdf:type schema:PropertyValue
72 Nd5bcbe7bb8a44ff9b03ba56b510da815 rdf:first sg:person.014177203151.25
73 rdf:rest N8fce5a9b47504e3cb0d589677d6cef33
74 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
75 schema:name Chemical Sciences
76 rdf:type schema:DefinedTerm
77 anzsrc-for:0303 schema:inDefinedTermSet anzsrc-for:
78 schema:name Macromolecular and Materials Chemistry
79 rdf:type schema:DefinedTerm
80 sg:journal.1043822 schema:issn 0032-3896
81 1349-0540
82 schema:name Polymer Journal
83 rdf:type schema:Periodical
84 sg:person.014177203151.25 schema:familyName Kawaguchi
85 schema:givenName Seigou
86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014177203151.25
87 rdf:type schema:Person
88 sg:person.014344077306.74 schema:familyName Ito
89 schema:givenName Koichi
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014344077306.74
91 rdf:type schema:Person
92 sg:person.07513273103.95 schema:familyName Hirose
93 schema:givenName Yuji
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07513273103.95
95 rdf:type schema:Person
96 https://doi.org/10.1002/pol.1953.120110503 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017391966
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1002/pol.1973.170110208 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006530165
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1002/pol.1978.180160405 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008965483
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1021/j100699a005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055676546
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1021/j100867a070 schema:sameAs https://app.dimensions.ai/details/publication/pub.1055683507
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1021/ma00011a025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056172828
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1021/ma00022a020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056173372
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1021/ma00030a014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056173791
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1021/ma00064a014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056175519
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1021/ma00173a028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056181490
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1021/ma00176a030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056181685
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1021/ma00205a007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056183662
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1021/ma00224a010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056184485
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1021/ma60059a011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1056201040
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1063/1.1672157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057748618
125 rdf:type schema:CreativeWork
126 https://doi.org/10.1063/1.1699114 schema:sameAs https://app.dimensions.ai/details/publication/pub.1057769646
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1063/1.431156 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058009188
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1063/1.455368 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058033380
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1063/1.457478 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058035490
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1063/1.458204 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058036216
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1063/1.461578 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058039589
137 rdf:type schema:CreativeWork
 




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


...