Modeling of Electrical Conductivity for Polymer–Carbon Nanofiber Systems

Arjmandi, Sajad Khalil; Yeganeh, Jafar Khademzadeh; Zare, Yasser; Naghdi, Samira

doi:10.3390/ma15197041

Cited by 6 publications

(2 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As presented in Equation ( 5), Deng and Zheng [26] developed an analytical model which considered random orientation of carbon-nanotube (f), filler and matrix conductivities (σ f , σ p ), and effect of contact resistance (µ 2 ). According to Arjmandi et al [27], Deng and Zheng model is insufficient to describe the effects of tunneling and contacts in polymer composite electrical conductivity.…”

Section: A Some Existing Models For Polymer Composites Electrical Con...mentioning

confidence: 99%

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Folorunso,

Olukanmi,

Shongwe

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Polymer nanocomposites are emerging hybrid materials for the production of energy storage electrodes, biomedical sensors, and building construction materials. However, experimentation cost and time can be unfavorable to their performance investigation. Therefore, using a modeling approach to predict the electrical conductivity of polymer nanocomposite is an effective approach in mitigating experimentation cost and time. Since the polymer nanocomposites' electrical conductivity depends on several factors, the engagement of efficient analytical models for predicting their properties, cannot be overemphasized. Herein, this study developed a series-parallel model, which incorporates the connection between the polymer and the nanofillers for the prediction of the electrical conductivity of graphene-polypyrrole (Gr-PPy) and reduced graphene oxide/polyvinyl alcohol/polypyrrole (RGO/PVA/PPy) nanocomposites. In addition to explicit modelling, an artificial intelligence approach (neural network) was also explored for the prediction tasks. The results of the models in an entity and when compared to an existing model, show flexibility and accuracy for the polymer nanocomposites electrical conductivity prediction. It can be inferred that the model can be suitable to predict the electrical conductivity of polymer nanocomposites.

show abstract

Section: A Some Existing Models For Polymer Composites Electrical Con...mentioning

confidence: 99%

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Folorunso,

Olukanmi,

Shongwe

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The electrical conductivity is mainly governed by charge tunneling as the current of electrons among adjacent nanosheets through tunneling mechanism 6 , 7 , 32 – 36 . Actually, the nanoparticles can contribute to the conductivity even once they are detached by short distance.…”

Section: Introductionmentioning

confidence: 99%

Effect of contact number among graphene nanosheets on the conductivities of tunnels and polymer composites

Zare

Kim

Gharib

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Simple equations are expressed for tunnel conductivity, tunnel resistance and conductivity of a graphene-filled composite by the number of contacts and interphase part. More specially, the active filler amount is suggested by interphase depth, which changes the contact number. The conductivity of nanocomposite is presented by filler content, filler dimensions, tunneling length and interphase depth. The innovative model is surveyed by the experimented conductivity of real examples. Too, the impacts of numerous issues on the tunnel resistance, tunnel conductivity and conductivity of nanocomposite are discussed to validate the novel equations. The estimates agree with the experimented data and the impacts of several terms on the tunnel resistance, tunnel conductivity and conductivity of system are sensible. Thin and big nanosheets positively affect the nanocomposite’s conductivity, but thick nanosheets improve the tunnel conductivity. High conductivity is found at short tunnels, while the nanocomposite’s conductivity directly depends on the tunneling length. The dissimilar effects of these features on the tunneling properties and conductivity are described.

show abstract

A Simple Model for Electrical Conductivity of Carbon Nanofiber Polymer Composites

Arjmandi

Yeganeh

Gharib

et al. 2023

JOM

View full text Add to dashboard Cite

Modeling of Electrical Conductivity for Polymer–Carbon Nanofiber Systems

Cited by 6 publications

References 79 publications

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Development of Series-Parallel and Neural-Network Based Models for Predicting Electrical Conductivity of Polymer Nanocomposite

Effect of contact number among graphene nanosheets on the conductivities of tunnels and polymer composites

A Simple Model for Electrical Conductivity of Carbon Nanofiber Polymer Composites

Contact Info

Product

Resources

About