Structure–property relationship in fluorene-based polymer films obtained by electropolymerization of 4,4′-(9-fluorenylidene)-dianiline

RSC Advances ◽  
2015 ◽  
Vol 5 (117) ◽  
pp. 97016-97026 ◽  
Author(s):  
Mariana-Dana Damaceanu ◽  
Luminita Marin
Keyword(s):  
Polymer Films ◽  
Chemical Properties ◽  
Structure Property ◽  
Property Relationship ◽  
Physico Chemical ◽  
Structure Property Relationship ◽  
First Time

For the first time, the electropolymerization of 4,4′-(9-fluorenylidene)-dianiline and physico-chemical properties of the obtained polymer films are reported.

Chitosan-Nanocellulose Composites for Regenerative Medicine Applications

2020 ◽  
Vol 27 (28) ◽  
pp. 4584-4592 ◽  
Author(s):  
Avik Khan ◽  
Baobin Wang ◽  
Yonghao Ni
Keyword(s):  
Regenerative Medicine ◽  
Preparation Method ◽  
Chemical Properties ◽  
Biological Properties ◽  
Next Generation ◽  
Structure Property ◽  
Physico Chemical ◽  
Structure Property Relationship ◽  
Attractive Candidate ◽  
Fundamental Understanding

Regenerative medicine represents an emerging multidisciplinary field that brings together engineering methods and complexity of life sciences into a unified fundamental understanding of structure-property relationship in micro/nano environment to develop the next generation of scaffolds and hydrogels to restore or improve tissue functions. Chitosan has several unique physico-chemical properties that make it a highly desirable polysaccharide for various applications such as, biomedical, food, nutraceutical, agriculture, packaging, coating, etc. However, the utilization of chitosan in regenerative medicine is often limited due to its inadequate mechanical, barrier and thermal properties. Cellulosic nanomaterials (CNs), owing to their exceptional mechanical strength, ease of chemical modification, biocompatibility and favorable interaction with chitosan, represent an attractive candidate for the fabrication of chitosan/ CNs scaffolds and hydrogels. The unique mechanical and biological properties of the chitosan/CNs bio-nanocomposite make them a material of choice for the development of next generation bio-scaffolds and hydrogels for regenerative medicine applications. In this review, we have summarized the preparation method, mechanical properties, morphology, cytotoxicity/ biocompatibility of chitosan/CNs nanocomposites for regenerative medicine applications, which comprises tissue engineering and wound dressing applications.

Quaternized poly(styrene-co-vinylbenzyl chloride) anion exchange membranes: role of different ammonium cations on structural, morphological, thermal and physio-chemical properties

2018 ◽  
Vol 42 (1) ◽  
pp. 380-387 ◽  
Author(s):  
Jeevanantham S. ◽  
Hosimin S. ◽  
Vengatesan S. ◽  
Sozhan G.
Keyword(s):  
Anion Exchange ◽  
Chemical Properties ◽  
Chloride Anion ◽  
Anion Exchange Membranes ◽  
Structure Property ◽  
Property Relationship ◽  
Structure Property Relationship

Investigating the effect of various ammonium cations on poly(styrene-co-vinylbenzyl chloride) based anion exchange membranes and correlating their structure–property relationship.

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>

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

Structure–property relationship of acceptor-substituted oligothiophenes

Tetrahedron ◽  
2010 ◽  
Vol 66 (45) ◽  
pp. 8729-8733 ◽  
Author(s):  
M.S. Wrackmeyer ◽  
M. Hummert ◽  
H. Hartmann ◽  
M.K. Riede ◽  
K. Leo
Keyword(s):  
Structure Property ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Of

Copolymerization of urea and murexide for efficient photocatalytic hydrogen evolution and tetracycline degradation

2021 ◽  
Author(s):  
Zhilu Du ◽  
Xinyu Zhao ◽  
Yingnan Zhao ◽  
Huiying Sun ◽  
Yingqi Li ◽  
...  
Keyword(s):  
Small Molecules ◽  
Hydrogen Evolution ◽  
Effective Strategy ◽  
Structure Property ◽  
Photocatalytic Hydrogen Evolution ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Tetracycline Degradation ◽  
Depth Study

Copolymerization of urea and small molecules is an effective strategy to modify g-C3N4. To in-depth study the important effects of the introduction of small molecular moiety on the structure-property relationship...

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