Structure-property comparison of sulfonated and carboxylated telechelic ionomers based on polyisoprene

1992 ◽  
Vol 25 (15) ◽  
pp. 3996-4001 ◽  
Author(s):  
L. N. Venkateshwaran ◽  
M. R. Tant ◽  
G. L. Wilkes ◽  
P. Charlier ◽  
R. Jerome
Keyword(s):  
Structure Property ◽  
Telechelic Ionomers

Structure-property relations of liquid crystalline polymers

1987 ◽  
Vol 45 ◽  
pp. 460-463
Author(s):  
Linda C. Sawyer
Keyword(s):  
Solid State ◽  
Liquid Crystalline ◽  
Structural Model ◽  
Crystalline Polymer ◽  
Liquid Crystalline Polymers ◽  
Mechanical Anisotropy ◽  
Structure Property ◽  
Preparation Methods ◽  
Crystalline Polymers ◽  
Extended Chain

Recent liquid crystalline polymer (LCP) research has sought to define structure-property relationships of these complex new materials. The two major types of LCPs, thermotropic and lyotropic LCPs, both exhibit effects of process history on the microstructure frozen into the solid state. The high mechanical anisotropy of the molecules favors formation of complex structures. Microscopy has been used to develop an understanding of these microstructures and to describe them in a fundamental structural model. Preparation methods used include microtomy, etching, fracture and sonication for study by optical and electron microscopy techniques, which have been described for polymers. The model accounts for the macrostructures and microstructures observed in highly oriented fibers and films.Rod-like liquid crystalline polymers produce oriented materials because they have extended chain structures in the solid state. These polymers have found application as high modulus fibers and films with unique properties due to the formation of ordered solutions (lyotropic) or melts (thermotropic) which transform easily into highly oriented, extended chain structures in the solid state.

Structure-property relationships of PE/PP sandwiched films

1987 ◽  
Vol 45 ◽  
pp. 454-455
Author(s):  
J. Petermann ◽  
G. Broza ◽  
U. Rieck ◽  
A. Jaballah ◽  
A. Kawaguchi
Keyword(s):  
Mechanical Tests ◽  
Polymer Materials ◽  
Ionic Crystals ◽  
Structure Property ◽  
Polymer Systems ◽  
Structure Property Relationships ◽  
Oriented Films ◽  
Materials Used ◽  
Polymer Laminates ◽  
Heated Glass

Oriented overgrowth of polymer materials onto ionic crystals is well known and recently it was demonstrated that this epitaxial crystallisation can also occur in polymer/polymer systems, under certain conditions. The morphologies and the resulting physical properties of such systems will be presented, especially the influence of epitaxial interfaces on the adhesion of polymer laminates and the mechanical properties of epitaxially crystallized sandwiched layers.Materials used were polyethylene, PE, Lupolen 6021 DX (HDPE) and 1810 D (LDPE) from BASF AG; polypropylene, PP, (PPN) provided by Höchst AG and polybutene-1, PB-1, Vestolen BT from Chemische Werke Hüls. Thin oriented films were prepared according to the method of Petermann and Gohil, by winding up two different polymer films from two separately heated glass-plates simultaneously with the help of a motor driven cylinder. One double layer was used for TEM investigations, while about 1000 sandwiched layers were taken for mechanical tests.

Probing vacancies and structural distortions at individual defects in ceramics using EELS

1996 ◽  
Vol 54 ◽  
pp. 678-679
Author(s):  
D. J. Wallis ◽  
N. D. Browning
Keyword(s):  
Structural Information ◽  
Core Loss ◽  
Nearest Neighbors ◽  
Ceramic Materials ◽  
Scanning Transmission Electron Microscope ◽  
Real Space ◽  
Atomic Scale ◽  
Structure Property ◽  
Scanning Transmission ◽  
Key Issues

In electron energy loss spectroscopy (EELS), the near-edge region of a core-loss edge contains information on high-order atomic correlations. These correlations give details of the 3-D atomic structure which can be elucidated using multiple-scattering (MS) theory. MS calculations use real space clusters making them ideal for use in low-symmetry systems such as defects and interfaces. When coupled with the atomic spatial resolution capabilities of the scanning transmission electron microscope (STEM), there therefore exists the ability to obtain 3-D structural information from individual atomic scale structures. For ceramic materials where the structure-property relationships are dominated by defects and interfaces, this methodology can provide unique information on key issues such as like-ion repulsion and the presence of vacancies, impurities and structural distortion.An example of the use of MS-theory is shown in fig 1, where an experimental oxygen K-edge from SrTiO3 is compared to full MS-calculations for successive shells (a shell consists of neighboring atoms, so that 1 shell includes only nearest neighbors, 2 shells includes first and second-nearest neighbors, and so on).

Approaches for TEM imaging of cavitation in toughened nylon

1989 ◽  
Vol 47 ◽  
pp. 352-353
Author(s):  
Barbara A. Wood
Keyword(s):  
Morphological Characteristics ◽  
Structure Property ◽  
Polymer Systems ◽  
Selective Staining ◽  
Structure Property Relationships ◽  
Rubber Toughened ◽  
Shear Yield ◽  
Controversial Topic ◽  
Selection Of ◽  
Diamond Knife

A controversial topic in the study of structure-property relationships of toughened polymer systems is the internal cavitation of toughener particles resulting from damage on impact or tensile deformation.Detailed observations of the influence of morphological characteristics such as particle size distribution on deformation mechanisms such as shear yield and cavitation could provide valuable guidance for selection of processing conditions, but TEM observation of damaged zones presents some experimental difficulties.Previously published TEM images of impact fractured toughened nylon show holes but contrast between matrix and toughener is lacking; other systems investigated have clearly shown cavitated impact modifier particles. In rubber toughened nylon, the physical characteristics of cavitated material differ from undamaged material to the extent that sectioning of heavily damaged regions by cryoultramicrotomy with a diamond knife results in sections of greater than optimum thickness (Figure 1). The detailed morphology is obscured despite selective staining of the rubber phase using the ruthenium trichloride route to ruthenium tetroxide.

Structure–property studies of self-assembling [n.n]paracyclophanes

2020 ◽  
Author(s):  
Will R Henderson ◽  
Danielle E. Fagnani ◽  
Yu Zhu ◽  
Guancen Liu ◽  
Ronald K. Castellano
Keyword(s):  
Structure Property ◽  
Self Assembling

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Catalyst Halogenation Enables Rapid and Efficient Polymerizations with Visible to Near-Infrared Light

2020 ◽  
Author(s):  
Alex Stafford ◽  
Dowon Ahn ◽  
Emily Raulerson ◽  
Kun-You Chung ◽  
Kaihong Sun ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transient Absorption ◽  
Reaction Times ◽  
Infrared Light ◽  
Structure Property ◽  
Light Emitting ◽  
Structure Property Relationships ◽  
Nir Light ◽  
Light Intensities ◽  
Emission Quenching

Driving rapid polymerizations with visible to near-infrared (NIR) light will enable nascent technologies in the emerging fields of bio- and composite-printing. However, current photopolymerization strategies are limited by long reaction times, high light intensities, and/or large catalyst loadings. Improving efficiency remains elusive without a comprehensive, mechanistic evaluation of photocatalysis to better understand how composition relates to polymerization metrics. With this objective in mind, a series of methine- and aza-bridged boron dipyrromethene (BODIPY) derivatives were synthesized and systematically characterized to elucidate key structure-property relationships that facilitate efficient photopolymerization driven by visible to NIR light. For both BODIPY scaffolds, halogenation was shown as a general method to increase polymerization rate, quantitatively characterized using a custom real-time infrared spectroscopy setup. Furthermore, a combination of steady-state emission quenching experiments, electronic structure calculations, and ultrafast transient absorption revealed that efficient intersystem crossing to the lowest excited triplet state upon halogenation was a key mechanistic step to achieving rapid photopolymerization reactions. Unprecedented polymerization rates were achieved with extremely low light intensities (< 1 mW/cm<sup>2</sup>) and catalyst loadings (< 50 μM), exemplified by reaction completion within 60 seconds of irradiation using green, red, and NIR light-emitting diodes.

Sign in / Sign up

Export Citation Format

Share Document