The Relation between Structure and Physical Properties in Polyurethans

1962 ◽  
Vol 35 (4) ◽  
pp. 970-1012 ◽  
Author(s):  
Takehide Tanaka ◽  
Tetsuo Yokoyama
Keyword(s):  
Physical Properties ◽  
Polymer Structure ◽  
Structure And Properties ◽  
Structure Property ◽  
Chemical Structures ◽  
Structure Property Relationships ◽  
Polymer Properties ◽  
Wide Range ◽  
Number Of Publications ◽  
Broad Region

Abstract The field of polyurethans is rapidly growing in commercial importance, especially in form and elastomer applications. This group of polymers includes a very broad region of chemical structures and chain length. In many cases polyurethans are synthesized from polyester glycols or polyether glycols and diisocyanates so that the urethan groups are even fewer in number than other functional groups. This process of synthesis enables us to deal with a very wide range of polymer properties, adding interest to the studies of relation between structure and properties. Though a considerable number of publications dealing primarily with the properties of urethan polymers have been published, few of them contribute to better understanding of the relation between these properties and the corresponding polymer structure. Within the last few years information of value has become available, and Saunders has established some semi-quantitative relations by the use of such data. He also discusses in his reports general considerations concerning structure-property relationships. Although his considerations and conclusions show a marked progress, they are not theoretically satisfactory yet, especially from a quantitative viewpoint. The authors have investigated structure-property relationships in polyurethans for a few years and written some papers concerning synthesis, reaction kinetics, some physical properties, network structure, and dynamic behavior of polyurethans.

The Relations between Polymer Structure and Properties in Urethans

1960 ◽  
Vol 33 (5) ◽  
pp. 1259-1292 ◽  
Author(s):  
J. H. Saunders
Keyword(s):  
Polymer Structure ◽  
Future Research ◽  
Reliable Control ◽  
Structure And Properties ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Precise Knowledge ◽  
Polymer Properties ◽  
Wide Range ◽  
The Many

Abstract Sufficient data are available from studies of urethan foams and elastomers to draw semiquantitative conclusions regarding the effect of any gross structural change on most polymer properties. These relationships apply to other areas of application as well, e.g., coatings, adhesives and sealants. Future research may be expected to provide more reliable control of the many reactions involved in preparing urethans, thus better control over structure. Similarly a more quantitative and extensive knowledge of polymer properties may be expected. The result of these combined efforts will be a more precise knowledge of structure-property relationships and an improved ability to produce polymers having the properties desired for a wide range of applications.

Investigation of the Structure-Property Relationships of Improved Low Compression Set Nitrile Rubbers

1981 ◽  
Vol 54 (1) ◽  
pp. 170-180 ◽  
Author(s):  
D. M. Chang
Keyword(s):  
Molecular Weight ◽  
Physical Properties ◽  
Structure And Properties ◽  
Structure Property ◽  
Compression Set ◽  
Structure Property Relationships ◽  
Polymer Properties ◽  
Nitrile Rubbers ◽  
New Type ◽  
The Right

Abstract The effect of polymer structures on the rubber processing and physical properties of the improved Hycar 1090 low compression set nitrile rubbers was investigated. The molecular weight and acrylonitrile content of a polymer are important variables in determining the compound processing and vulcanizate physical properties. Within the range of 21 to 88 Mooney, a blend of high and low Mooney polymers has approximately the same properties as those from a single polymer of the same Mooney viscosity. The molecular weight distribution was not significantly broadened to become an important factor affecting the polymer properties. All polymers with Mooney viscosities from 21 to 88 showed good properties. An understanding of the structure and properties of this new type of NBR, will help in choosing the right kind of polymer for particular applications.

Structure–property relationships of aromatic polyamides and polythioamides: comparative consideration with those of analogous polyesters, polythioesters and polydithioesters

RSC Advances ◽  
2015 ◽  
Vol 5 (117) ◽  
pp. 96611-96622 ◽  
Author(s):  
Masayuki Nagasawa ◽  
Tatsuya Ishii ◽  
Daisuke Abe ◽  
Yuji Sasanuma
Keyword(s):  
Structure And Properties ◽  
Structure Property ◽  
Aromatic Polyamides ◽  
Structure Property Relationships

The structure and properties of aromatic polyamides and polythioamides were investigated and compared with those of analogous polyesters, polythioesters, and polydithioesters.

scholarly journals A general approach for retrosynthetic molecular core analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
J. Jesús Naveja ◽  
B. Angélica Pilón-Jiménez ◽  
Jürgen Bajorath ◽  
José L. Medina-Franco
Keyword(s):  
Single Molecule ◽  
Chemical Space ◽  
Significant Proportion ◽  
Bipartite Network ◽  
Data Sets ◽  
Structure Property ◽  
Data Set ◽  
Chemical Structures ◽  
Structure Property Relationships ◽  
Analog Series

Abstract Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

Structure and Properties of Polysilsesquioxanes and Copolymers for Ultra-Low Dielectric Films

2003 ◽  
Vol 766 ◽  
Author(s):  
Do Y. Yoon ◽  
Hyun Wook Ro ◽  
Eun Su Park ◽  
Jin-Kyu Lee ◽  
Hie-Joon Kim ◽  
...  
Keyword(s):  
Copper Interconnects ◽  
Dielectric Constants ◽  
Dielectric Films ◽  
Structure And Properties ◽  
Structure Property ◽  
Detailed Structure ◽  
Low Dielectric ◽  
Structure Property Relationships ◽  
Mechanical Modulus ◽  
Made In

AbstractPolysilsesquioxanes (PSSQs) with the empirical formula (RSiO3/2)n have become very important as low-dielectric insulators for copper interconnects in the next-generation logic devices, but the detailed structure-property relationships were completely lacking. We have investigated the microstructure and functional properties of PSSQs with varying alkyl substituents and also PSSQ copolymers. As a result, significant advances have been made in the scientific understanding of PSSQ structures and significant improvements of key properties such as the crack resistance, mechanical modulus and hardness, and incorporation of nanometer-sized (<4 nm) porosity for ultra-low dielectric constants (<2.0).

scholarly journals Predicting permeability via statistical learning on higher-order microstructural information

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Magnus Röding ◽  
Zheng Ma ◽  
Salvatore Torquato
Keyword(s):  
Neural Networks ◽  
Physical Properties ◽  
Specific Surface ◽  
Correlation Functions ◽  
Quantitative Structure ◽  
Structure Property ◽  
Complex Materials ◽  
Structure Property Relationships ◽  
Quantitative Structure Property Relationships ◽  
Point Correlation

Abstract Quantitative structure–property relationships are crucial for the understanding and prediction of the physical properties of complex materials. For fluid flow in porous materials, characterizing the geometry of the pore microstructure facilitates prediction of permeability, a key property that has been extensively studied in material science, geophysics and chemical engineering. In this work, we study the predictability of different structural descriptors via both linear regressions and neural networks. A large data set of 30,000 virtual, porous microstructures of different types, including both granular and continuous solid phases, is created for this end. We compute permeabilities of these structures using the lattice Boltzmann method, and characterize the pore space geometry using one-point correlation functions (porosity, specific surface), two-point surface-surface, surface-void, and void-void correlation functions, as well as the geodesic tortuosity as an implicit descriptor. Then, we study the prediction of the permeability using different combinations of these descriptors. We obtain significant improvements of performance when compared to a Kozeny-Carman regression with only lowest-order descriptors (porosity and specific surface). We find that combining all three two-point correlation functions and tortuosity provides the best prediction of permeability, with the void-void correlation function being the most informative individual descriptor. Moreover, the combination of porosity, specific surface, and geodesic tortuosity provides very good predictive performance. This shows that higher-order correlation functions are extremely useful for forming a general model for predicting physical properties of complex materials. Additionally, our results suggest that artificial neural networks are superior to the more conventional regression methods for establishing quantitative structure–property relationships. We make the data and code used publicly available to facilitate further development of permeability prediction methods.

Polypropylene based carbon nanotubes composites: structure and properties

e-Polymers ◽  
2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Daniela Tabuani ◽  
Walter Granelli ◽  
Giovanni Camino ◽  
Michael Claes
Keyword(s):  
Carbon Nanotubes ◽  
Thermal Behaviour ◽  
Polymer Nanocomposite ◽  
Structure And Properties ◽  
Polymer Properties ◽  
Wide Range ◽  
The Matrix ◽  
Nanotube Composites ◽  
Carbon Nanotube Composites ◽  
Comprehensive Study

AbstractIn the field of polymer nanocomposite materials, carbon nanotubes have attracted lots of research interests in the recent past for their potentialities in improving a wide range of polymer properties. We present here a comprehensive study on polypropylene/carbon nanotube composites evaluating the morphology as well as the thermal behaviour of the prepared systems. Pristine as well as -COOH functionalised carbon nanotubes were taken into account and melt mixed at different weight fractions with PP; the crystallisation characteristics of the material were evaluated by means of DSC and XRD and the thermal behaviour was assessed through TGA analyses. The nanotubes appear to affect significantly the properties of the matrix in a way notably dependent on the functionalization and on the filler amount.

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