scholarly journals Branched isomeric 1,2,3-triazolium-based ionic liquids: new insight into structure–property relationships

2015 ◽  
Vol 17 (44) ◽  
pp. 29834-29843 ◽  
Author(s):  
M. Lartey ◽  
J. Meyer-Ilse ◽  
J. D. Watkins ◽  
E. A. Roth ◽  
S. Bowser ◽  
...  
Keyword(s):  
Ionic Liquids ◽  
Physical Properties ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Insight Into

Series of branched isomeric 1,2,3-triazolium-based ionic liquids (ILs) were synthesized and characterized. The effect of branching on thermal and physical properties is investigated.

Structure/Property Relationships of Seashells

2004 ◽  
Vol 844 ◽  
Author(s):  
David J. Scurr ◽  
Stephen J. Eichhorn
Keyword(s):  
Raman Band ◽  
Surface Damage ◽  
Structure Property ◽  
X Ray Diffraction ◽  
X Ray ◽  
Structure Property Relationships ◽  
Energy Variable ◽  
Insight Into ◽  
Characterisation Techniques ◽  
Scanning Electron

ABSTRACTThis study uses various characterisation techniques on the razor shell (Ensis siliqua), to relate the shell's microstructure to its mechanical properties. Scanning electron microscopy (SEM) has shown that the outer and inner regions of the shell are composed of simple and complex crossed lamellar microstructures respectively. These layers are interspersed by prismatic layers of a completely different crystallographic orientation. Nanoindentation and microhardness measurements have shown that the structure is anisotropic, and Raman band shifts have been observed within these indented/deformed areas of shell, showing that the microstructure deforms rather than generating surface damage. The use of energy variable synchrotron X-ray diffraction has shown that the calcium carbonate crystals of the shell are preferentially orientated as a function of depth and that opposing residual stresses exist at the outer and inner regions of the shell. This study has analysed several microstructural features of the shell and provided an insight into how they prevent failure of the material.

Synthesis and Structure-Property Relationships in Herbicidal Ionic Liquids and their Double Salts

ChemPlusChem ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. 529-541 ◽  
Author(s):  
Michał Niemczak ◽  
Tomasz Rzemieniecki ◽  
Agnieszka Biedziak ◽  
Katarzyna Marcinkowska ◽  
Juliusz Pernak
Keyword(s):  
Ionic Liquids ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Double Salts

Structure-Property Relationships of Silylamine-Type Reversible Ionic Liquids for Use as a Switchable Electrolyte

2021 ◽  
Vol 168 (3) ◽  
pp. 036516
Author(s):  
Sungyup Jung ◽  
Showmik Podder ◽  
Josephine Chen ◽  
Elizabeth J. Biddinger
Keyword(s):  
Ionic Liquids ◽  
Structure Property ◽  
Structure Property Relationships

Crystal and electronic structure of the new ternary phosphide Ho5Pd19P12

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Olha Zhak ◽  
Oksana Karychort ◽  
Volodymyr Babizhetskyy ◽  
Chong Zheng
Keyword(s):  
Crystal Structure ◽  
Electronic Structure ◽  
Quantum Chemical Calculations ◽  
Quantum Chemical ◽  
Structure Property ◽  
X Ray Diffraction ◽  
Structure Property Relationships ◽  
Metallic Bonding ◽  
Crystal And Electronic Structure ◽  
Insight Into

Abstract The title compound was prepared from the pure elements by sintering. The crystal structure was investigated by means of powder X-ray diffraction data. Ho5Pd19P12 exhibits the hexagonal Ho5Ni19P12-type structure with space group P 6 ‾ 2 m $P‾{6}2m$ , a = 13.1342(2), c = 3.9839(1) Å, R I = 0.060, R p = 0.080. The crystal structure can be described as a combination of two types of the structural units, [HoPd6P3] and [Ho3Pd10P6], respectively, mutually displaced by 1/2 along the crystallographic c axis. Quantum chemical calculations have been performed to analyze the electronic structure and provide deeper insight into the structure-property relationships. The results of the quantum chemical calculations indicate that the material features metallic bonding between Ho and Pd and covalent bonding between Pd and P.

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.

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.

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