Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions

Hassinger, Irene; Li, Xin; Zhao, He; Xu, Hongyi; Huang, Yanhui; Prasad, Arun; Schadler, Linda S.; Chen, Wei; Brinson, L. Catherine

doi:10.1007/s10853-015-9698-1

Cited by 53 publications

(35 citation statements)

References 61 publications

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“…This outcome indicates a direct relationship between reinforcement concentration and aggregates amount in the polymer matrix. The reduction of total interfacial energy and wear adhesion between PC and TiO 2 nanofibers are the driving forces to generate those agglomerates in nanocomposites . Figure d shows AMS agglomerates in the polycarbonate matrix that indicate a poor miscible of AMS into PC.…”

Section: Resultsmentioning

confidence: 99%

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties

et al. 2016

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In this study, we explore a new idea to improve the mechanical properties and UV resistance of the polycarbonate (PC) using TiO2 electrospun nanofibers as polymer additive. Moreover, we describe a new method to functionalize TiO2 nanofibers using [3‐(2‐aminoethyl)aminopropyl]trimethoxysilane (AMS) by ultrasonic bath. The nanocomposites with several contents of TiO2 nanofibers (0–5 wt%) were characterized by X‐ray diffraction, differential scanning calorimetry, thermogravimetric analysis, uniaxial tensile testing and scanning electron microscopy. The XRD patterns suggest formation of semi‐crystalline polycarbonate regions in nanocomposites with nanofibers previously deagglomerated by ultrasound. Furthermore, the mechanical properties (tensile strength, rupture elongation, elastic modulus and yield strength) and thermal behavior from the nanocomposites are significantly affected by TiO2 nanofibers content and AMS. POLYM. COMPOS., 39:E780–E790, 2018. © 2016 Society of Plastics Engineers

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Section: Resultsmentioning

confidence: 99%

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties

et al. 2016

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“…First of all, it is usually difficult to drive the system to the point where the thermodynamic equilibrium dictates homogeneously mixed materials. Second, as the nanorods are often capable of migrating (under processing conditions), they are readily kinetically trapped in an interconnected network structure . The interconnection between the nanorods is only possible above a critical volume fraction or “percolation threshold” (ψ c ).…”

Section: Introductionmentioning

confidence: 99%

A refined model for the mechanical properties of polymer composites with nanorods having different length distributions

Sapkota

Gooneie

Shirole

et al. 2017

J of Applied Polymer Sci

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The aspect ratio of rod‐like nanofillers is an important factor that governs their percolation threshold in nanocomposites with a polymer of interest. Controlling the length distribution of a filler is an effective way to manipulate the onset of network formation and thereby materials properties that are related to the latter. The use of two or more different filler types with dissimilar aspect ratio is a special case to achieve such control. We here report the modification of an existing mechanical model to describe the mechanical properties of polymer nanocomposites with a binary length distribution of nanofillers. The model was exemplarily applied to materials comprised of two nanorod types having high and low aspect ratio, respectively, and used to explain why the properties of such nanocomposites are strongly weighted by the nanorod type characterized by a higher aspect ratio and stiffness. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45279.

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“…One tool, Niblack Binarization, adopts a dynamic local thresholding algorithm to convert input grayscale micrographs into a binary image with separated nanofiller and matrix phases. 52 As the threshold value that separates filler from matrix phase is only applied locally within fixed size windows, this local binarization algorithm ensures that the background noise and influence from uneven brightness are eliminated. Using this binary image, the Descriptor Characterization tool quantifies nanophase dispersion into statistical descriptors that capture the composition, geometry, and dispersion of nanofillers.…”

Section: B Analysis Toolsmentioning

confidence: 99%

“…For example, our work has shown that mixing energy associated with the processing steps can be statistically correlated with microstructure dispersion. 52 Given the nanocomposite constituents and mixing energy calculated from the extrusion procedures, microstructure descriptors can be obtained through matrix-dependent analytical expressions based on statistical learning. This process is informed by the predicted surface energy of the constituents.…”

Section: B Analysis Toolsmentioning

confidence: 99%

“…55 Two sub processes will be carried out to process the data: (1) microstructure and (2) interphase. Using statistical correlations, processing parameters from the extrusion processes 52 and the constituent surface energies 55 are related to descriptors and then the 3D Reconstruction tool is employed to generate a 3D morphology that serves as input to the FEA model. For the interphase input, an energy-interphase correlation (Interphase Tool) is applied to derive local interphase properties, which is then loaded into the FEA FIG.…”

Section: Property Prediction and Design Workflowmentioning

confidence: 99%

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Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design

Zhao

Zhang

et al. 2016

APL Materials

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Polymer nanocomposites are a designer class of materials where nanoscale particles, functional chemistry, and polymer resin combine to provide materials with unprecedented combinations of physical properties. In this paper, we introduce NanoMine, a data-driven web-based platform for analysis and design of polymer nanocomposite systems under the material genome concept. This open data resource strives to curate experimental and computational data on nanocomposite processing, structure, and properties, as well as to provide analysis and modeling tools that leverage curated data for material property prediction and design. With a continuously expanding dataset and toolkit, NanoMine encourages community feedback and input to construct a sustainable infrastructure that benefits nanocomposite material research and development. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

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Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions

Cited by 53 publications

References 61 publications

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties

A refined model for the mechanical properties of polymer composites with nanorods having different length distributions

Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design

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Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions

Cited by 53 publications

References 61 publications

Polycarbonate/TiO2 nanofibers nanocomposite: Preparation and properties

Polycarbonate/TiO2 nanofibers nanocomposite: Preparation and properties

A refined model for the mechanical properties of polymer composites with nanorods having different length distributions

Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design

Contact Info

Product

Resources

About

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties

Polycarbonate/TiO₂ nanofibers nanocomposite: Preparation and properties