Statistical analysis of effective electro-mechanical properties and percolation behavior of aligned carbon nanotube/polymer nanocomposites via computational micromechanics

Talamadupula, Krishna Kiran; Seidel, Gary D.

doi:10.1016/j.commatsci.2021.110616

Cited by 19 publications

(44 citation statements)

References 97 publications

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“…Considerable effort has also been devoted to the development of piezoresistivity modelscomputational and/or analytical means of predicting how conductivity changes for a prescribed strain. These efforts can be broadly categorized as (i) equivalent resistor network models [260][261][262], (ii) computational micro-mechanics models [254,[263][264][265], or (iii) homogenized macroscale models [266][267][268]. In (i) equivalent resistor network models, high aspect ratio fillers such as CNTs are represented as either straight or wavy/curved sticks in a micro-domain [260][261][262].…”

Section: (A) Piezoresistive Nanocompositesmentioning

confidence: 99%

“…Second, (ii) computational micro-mechanics models use computational means to simulate both phases of the composite-the non-conductive matrix and the conductive fillers [254,[263][264][265]. Because of this, and unlike equivalent resistor network models, computational micro-mechanics models can incorporate nuanced effects such as nanofiller-to-matrix debonding, nanofiller deformation, etc.…”

Section: (A) Piezoresistive Nanocompositesmentioning

confidence: 99%

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Structural engineering from an inverse problems perspective

et al. 2022

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The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.—characterizing a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

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Section: (A) Piezoresistive Nanocompositesmentioning

confidence: 99%

Section: (A) Piezoresistive Nanocompositesmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

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“…The estimates for piezoresistive coefficients are validated by comparison with experimentally reported gage factors. The present study builds upon a closely related initial work by Talamadupula and Seidel [ 8 ] that focused on the statistical characterization of these same properties for the special case of aligned, straight CNTs. Results from Talamadupula and Seidel [ 8 ] are presented here for comparison purposes with the imperfectly aligned, randomly oriented results and also results with varying degrees of waviness.…”

Section: Introduction Motivation and Backgroundmentioning

confidence: 97%

“…The present study builds upon a closely related initial work by Talamadupula and Seidel [ 8 ] that focused on the statistical characterization of these same properties for the special case of aligned, straight CNTs. Results from Talamadupula and Seidel [ 8 ] are presented here for comparison purposes with the imperfectly aligned, randomly oriented results and also results with varying degrees of waviness. A few modeling details repeated from [ 8 ] and some Supplementary Data with discussion are presented in the Supplementary Materials .…”

Section: Introduction Motivation and Backgroundmentioning

confidence: 97%

“…The mean of the reported percolation values was found to be 0.61 wt.%. A primary motivating factor for the study of electrical percolation is its importance in determining CNT/polymer nanocomposite effective piezoresistivity [ 8 ]. There have been several works to this effect using theoretical, computational or experimental analysis methods [ 5 , 6 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introduction Motivation and Backgroundmentioning

confidence: 99%

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Computational Micromechanics Investigation of Percolation and Effective Electro-Mechanical Properties of Carbon Nanotube/Polymer Nanocomposites using Stochastically Generated Realizations: Effects of Orientation and Waviness

Talamadupula

Seidel

2022

Polymers

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The electrical and mechanical properties of carbon nanotube/polymer nanocomposites depend strongly upon several factors such as CNT volume fraction, CNT alignment, CNT dispersion and CNT waviness among others. This work focuses on obtaining estimates and distribution for the effective electrical conductivity, elastic constants and piezoresistive properties as a function of these factors using a stochastic approach with numerous CNT/polymer realizations coupled with parallel computation. Additionally, electrical percolation volume fraction and percolation transitional behavior is also studied. The effective estimates and percolation values were found to be in good agreement with experimental works in the literature. It was found that with increasing CNT volume fraction, the mechanical properties improved. However, due to the interaction of CNTs with one another through electrical tunneling, the conductivity and piezoresistivity properties evolved in a more complex manner. While the degree of alignment played a strong role in the effective properties making them anisotropic, the effect of waviness was found to be insubstantial.

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A multiscale modeling framework for predicting strain‐dependent electrical conductivity of carbon nanotube‐incorporated nanocomposites considering the electron tunneling effect

Kil,

Bae,

Park

et al. 2024

Polymer Composites

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The present work proposes a multiscale modeling framework for predicting the strain‐dependent electrical conductivity of carbon nanotube (CNT)‐incorporated nanocomposites considering the electron tunneling effect. A micromechanical model taking into account the waviness of the CNTs is utilized and molecular dynamics simulations estimating changes in distance between CNTs in the polymer matrix are conducted in an attempt to incorporate the electron tunneling effect. A series of numerical parametric studies are carried out to examine the influence of model parameters (e.g., CNT length, number of CNT segments, and intrinsic interfacial resistivity) on the strain‐dependent electrical conductivity of the nanocomposites. In addition, CNT/polydimethylsiloxane samples are fabricated and their strain‐dependent electrical conductivity is experimentally evaluated. Finally, to verify the predictive capability of the proposed modeling framework, the present predictions are compared with experimental results.Highlights A multiscale modeling framework of CNT‐incorporated nanocomposites is proposed. A micromechanics model and molecular dynamics simulations are used. The study includes a numerical parametric investigation. The present predictions are compared with those obtained experimentally.

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Statistical analysis of effective electro-mechanical properties and percolation behavior of aligned carbon nanotube/polymer nanocomposites via computational micromechanics

Cited by 19 publications

References 97 publications

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Computational Micromechanics Investigation of Percolation and Effective Electro-Mechanical Properties of Carbon Nanotube/Polymer Nanocomposites using Stochastically Generated Realizations: Effects of Orientation and Waviness

A multiscale modeling framework for predicting strain‐dependent electrical conductivity of carbon nanotube‐incorporated nanocomposites considering the electron tunneling effect

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