Structure–property relationships on uniaxially oriented carbon nanotube/polyethylene composites

Polymer ◽  
2011 ◽  
Vol 52 (4) ◽  
pp. 1124-1132 ◽  
Author(s):  
Giuliana Gorrasi ◽  
Roberta Di Lieto ◽  
Giovanni Patimo ◽  
Salvatore De Pasquale ◽  
Andrea Sorrentino
Keyword(s):  
Carbon Nanotube ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Polyethylene Composites

Quantum Conductance of Copper–Carbon Nanotube Composites

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Yangchuan Li ◽  
Eric Fahrenthold
Keyword(s):  
Carbon Nanotube ◽  
Large Scale ◽  
Computational Cost ◽  
Specific Conductance ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Nanotube Composites ◽  
Simplifying Assumptions ◽  
Analytical Research ◽  
Quantum Modeling

Carbon nanotube (CNT)-based conductors are the focus of considerable ongoing experimental research, which has demonstrated their potential to offer increased current carrying capacity or higher specific conductance, as compared to conventional copper cabling. Complementary analytical research has been hindered by the high computational cost of large-scale quantum models. The introduction of certain simplifying assumptions, supported by critical comparisons to exact solutions and the published literature, allows for quantum modeling work to assist experiment in composite conductor development. Ballistic conductance calculations may be used to identify structure–property relationships and suggest the most productive avenues for future nanocomposite conductor research.

Key Factors Limiting Carbon Nanotube Yarn Strength: Exploring Processing-Structure-Property Relationships

ACS Nano ◽  
2014 ◽  
Vol 8 (11) ◽  
pp. 11454-11466 ◽  
Author(s):  
Allison M. Beese ◽  
Xiaoding Wei ◽  
Sourangsu Sarkar ◽  
Rajaprakash Ramachandramoorthy ◽  
Michael R. Roenbeck ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Structure Property ◽  
Key Factors ◽  
Yarn Strength ◽  
Structure Property Relationships

scholarly journals Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

2020 ◽  
Author(s):  
◽  
Taheg Hajilounezhad
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
Forest Growth ◽  
Structure Property ◽  
Deposition Parameter ◽  
Structure Property Relationships ◽  
In Situ Experiments ◽  
Process Structure

This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.

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.

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.

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