Prediction of glass transition temperatures of polyquinolines and polyquinoxalines

Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepw...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Yanli [verfasserIn] Tan, Zhengde Zhang, Shihua

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Glass Transition Temperature Polymer Science Series Multiple Linear Regression Model Molecular Descriptor Stepwise Multiple Linear Regression

Anmerkung:	© Pleiades Publishing, Ltd. 2012

Übergeordnetes Werk:	Enthalten in: Polymer science - Berlin : Springer, 2006, 54(2012), 1 vom: Jan., Seite 48-60
Übergeordnetes Werk:	volume:54 ; year:2012 ; number:1 ; month:01 ; pages:48-60

Links:	Volltext

DOI / URN:	10.1134/S0965545X12010099

Katalog-ID:	SPR020241291

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520			\|a Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures.
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912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
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912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
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912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
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912			\|a GBV_ILN_2010
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912			\|a GBV_ILN_2031
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_2548
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publishDate	2012
allfields	10.1134/S0965545X12010099 doi (DE-627)SPR020241291 (SPR)S0965545X12010099-e DE-627 ger DE-627 rakwb eng Liu, Yanli verfasserin aut Prediction of glass transition temperatures of polyquinolines and polyquinoxalines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2012 Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213 Tan, Zhengde aut Zhang, Shihua aut Enthalten in Polymer science Berlin : Springer, 2006 54(2012), 1 vom: Jan., Seite 48-60 (DE-627)509758509 (DE-600)2228442-4 1555-6107 nnns volume:54 year:2012 number:1 month:01 pages:48-60 https://dx.doi.org/10.1134/S0965545X12010099 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2012 1 01 48-60
spelling	10.1134/S0965545X12010099 doi (DE-627)SPR020241291 (SPR)S0965545X12010099-e DE-627 ger DE-627 rakwb eng Liu, Yanli verfasserin aut Prediction of glass transition temperatures of polyquinolines and polyquinoxalines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2012 Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213 Tan, Zhengde aut Zhang, Shihua aut Enthalten in Polymer science Berlin : Springer, 2006 54(2012), 1 vom: Jan., Seite 48-60 (DE-627)509758509 (DE-600)2228442-4 1555-6107 nnns volume:54 year:2012 number:1 month:01 pages:48-60 https://dx.doi.org/10.1134/S0965545X12010099 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2012 1 01 48-60
allfields_unstemmed	10.1134/S0965545X12010099 doi (DE-627)SPR020241291 (SPR)S0965545X12010099-e DE-627 ger DE-627 rakwb eng Liu, Yanli verfasserin aut Prediction of glass transition temperatures of polyquinolines and polyquinoxalines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2012 Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213 Tan, Zhengde aut Zhang, Shihua aut Enthalten in Polymer science Berlin : Springer, 2006 54(2012), 1 vom: Jan., Seite 48-60 (DE-627)509758509 (DE-600)2228442-4 1555-6107 nnns volume:54 year:2012 number:1 month:01 pages:48-60 https://dx.doi.org/10.1134/S0965545X12010099 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2012 1 01 48-60
allfieldsGer	10.1134/S0965545X12010099 doi (DE-627)SPR020241291 (SPR)S0965545X12010099-e DE-627 ger DE-627 rakwb eng Liu, Yanli verfasserin aut Prediction of glass transition temperatures of polyquinolines and polyquinoxalines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2012 Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213 Tan, Zhengde aut Zhang, Shihua aut Enthalten in Polymer science Berlin : Springer, 2006 54(2012), 1 vom: Jan., Seite 48-60 (DE-627)509758509 (DE-600)2228442-4 1555-6107 nnns volume:54 year:2012 number:1 month:01 pages:48-60 https://dx.doi.org/10.1134/S0965545X12010099 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2012 1 01 48-60
allfieldsSound	10.1134/S0965545X12010099 doi (DE-627)SPR020241291 (SPR)S0965545X12010099-e DE-627 ger DE-627 rakwb eng Liu, Yanli verfasserin aut Prediction of glass transition temperatures of polyquinolines and polyquinoxalines 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pleiades Publishing, Ltd. 2012 Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213 Tan, Zhengde aut Zhang, Shihua aut Enthalten in Polymer science Berlin : Springer, 2006 54(2012), 1 vom: Jan., Seite 48-60 (DE-627)509758509 (DE-600)2228442-4 1555-6107 nnns volume:54 year:2012 number:1 month:01 pages:48-60 https://dx.doi.org/10.1134/S0965545X12010099 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 54 2012 1 01 48-60
language	English
source	Enthalten in Polymer science 54(2012), 1 vom: Jan., Seite 48-60 volume:54 year:2012 number:1 month:01 pages:48-60
sourceStr	Enthalten in Polymer science 54(2012), 1 vom: Jan., Seite 48-60 volume:54 year:2012 number:1 month:01 pages:48-60
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Glass Transition Temperature Polymer Science Series Multiple Linear Regression Model Molecular Descriptor Stepwise Multiple Linear Regression
isfreeaccess_bool	false
container_title	Polymer science
authorswithroles_txt_mv	Liu, Yanli @@aut@@ Tan, Zhengde @@aut@@ Zhang, Shihua @@aut@@
publishDateDaySort_date	2012-01-01T00:00:00Z
hierarchy_top_id	509758509
id	SPR020241291
language_de	englisch
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author	Liu, Yanli
spellingShingle	Liu, Yanli misc Glass Transition Temperature misc Polymer Science Series misc Multiple Linear Regression Model misc Molecular Descriptor misc Stepwise Multiple Linear Regression Prediction of glass transition temperatures of polyquinolines and polyquinoxalines
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topic_title	Prediction of glass transition temperatures of polyquinolines and polyquinoxalines Glass Transition Temperature (dpeaa)DE-He213 Polymer Science Series (dpeaa)DE-He213 Multiple Linear Regression Model (dpeaa)DE-He213 Molecular Descriptor (dpeaa)DE-He213 Stepwise Multiple Linear Regression (dpeaa)DE-He213
topic	misc Glass Transition Temperature misc Polymer Science Series misc Multiple Linear Regression Model misc Molecular Descriptor misc Stepwise Multiple Linear Regression
topic_unstemmed	misc Glass Transition Temperature misc Polymer Science Series misc Multiple Linear Regression Model misc Molecular Descriptor misc Stepwise Multiple Linear Regression
topic_browse	misc Glass Transition Temperature misc Polymer Science Series misc Multiple Linear Regression Model misc Molecular Descriptor misc Stepwise Multiple Linear Regression
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title	Prediction of glass transition temperatures of polyquinolines and polyquinoxalines
ctrlnum	(DE-627)SPR020241291 (SPR)S0965545X12010099-e
title_full	Prediction of glass transition temperatures of polyquinolines and polyquinoxalines
author_sort	Liu, Yanli
journal	Polymer science
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author_browse	Liu, Yanli Tan, Zhengde Zhang, Shihua
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author-letter	Liu, Yanli
doi_str_mv	10.1134/S0965545X12010099
title_sort	prediction of glass transition temperatures of polyquinolines and polyquinoxalines
title_auth	Prediction of glass transition temperatures of polyquinolines and polyquinoxalines
abstract	Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. © Pleiades Publishing, Ltd. 2012
abstractGer	Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. © Pleiades Publishing, Ltd. 2012
abstract_unstemmed	Abstract Two molecular descriptors calculated directly from repeating units were used to predict the glass transition temperature (Tg) values of polyquinolines and polyquinoxalines. These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. The results indicate that chosen model containing only two molecular descriptors can be applied to predict Tg of polyquinolines and polyquinoxalines, although these polymers have complicated structures. © Pleiades Publishing, Ltd. 2012
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title_short	Prediction of glass transition temperatures of polyquinolines and polyquinoxalines
url	https://dx.doi.org/10.1134/S0965545X12010099
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author2	Tan, Zhengde Zhang, Shihua
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doi_str	10.1134/S0965545X12010099
up_date	2024-07-03T14:48:20.195Z
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These polymers were randomly divided into a training set (44 polymers) and a test set (19 polymers). By applying stepwise multiple linear regression analysis, the training set was used to construct a quantitative structure-property relationship model, which was evaluated externally with the test set. The descriptors used have definite physical meaning. Root mean square errors for the training set and the test set were 15.90 K and 17.33 K respectively, which were accurate and acceptable in comparison with existing models. 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Prediction of glass transition temperatures of polyquinolines and polyquinoxalines

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