Prediction and optimization of electrical conductivity for polymer-based composites using design of experiment and artificial neural networks

Razavi, Seyed Morteza; Sadollah, Ali; Al-Shamiri, Abobakr Khalil

doi:10.1007/s00521-021-06798-7

Cited by 11 publications

(6 citation statements)

References 47 publications

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“…As a res electrical conductivity after this threshold increased significantly and reached about 0. S/m at a 3 wt.% CNT content due to the formation of electrical pathways within the na composite films, as has been reported in other references [20].…”

Section: Nanocomposite Film Conductivitysupporting

confidence: 78%

“…The uniform dispersion of CNTs increases their effectiveness and aims to achieve desired functional properties that depend on forming connected and uniformly distributed nanotube networks. For instance, the electrical conductivity of CNT/polymer nanocomposites results from direct charge transfer along the CNTs' percolation paths [20].…”

Section: Introductionmentioning

confidence: 99%

“…The minimum filler content required to create a conductive path is called the percolation threshold. The conductivity of a network increases with filler content according to a power law [20], as shown in the following equation for a model of isotropically and homogeneously distributed hard filler particles (1):…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Electrical and Mechanical Properties of Polystyrene (PS)/Untreated SWCNT Nanocomposite Films

Parnian,

D’Amore

2024

J. Compos. Sci.

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This paper presents a study of the electrical and mechanical properties of polystyrene (PS)/carbon nanotube (CNT) composites prepared using the doctor blade technique. The nanocomposite films of PS/CNT were prepared by casting a composite solution of PS/CNT in tetrahydrofuran (THF) on a glass substrate using a doctor blade and drying in an oven. The nanocomposite films were then characterized using a tensile test and the four-point probe method to evaluate their mechanical properties and electrical conductivity. The experimental results were used to analyze the unpredicted behavior of the nanocomposite films. The experimental results showed that the electrical conductivity of the nanocomposite films became almost insensitive or unmeasurable with increasing CNT content for very dilute PS–THF solutions. In contrast, at higher PS concentrations, film conductivity increased to a given CNT threshold and then decreased. Based on PS–THF viscosity–concentration data, a discussion is elaborated that partially justifies the experimental results.

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Section: Nanocomposite Film Conductivitysupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

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Investigating the Electrical and Mechanical Properties of Polystyrene (PS)/Untreated SWCNT Nanocomposite Films

Parnian,

D’Amore

2024

J. Compos. Sci.

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“…Considering the range of ∆E AC and their frequency dependency, Prediction is compatible with actual experimental measurements. Figures 9(a ´-2.764 10 , 6 and ´-6.14 10 , 7 respectively. Consider figures 9(c), (d), these demonstrate the training error during the modeling of s ln AC versus 1000/T for films with thicknesses 320.7 nm, and 417.2 nm, respectively.…”

Section: Anfis Modeling Resultsmentioning

confidence: 98%

“…A successful model is characterized by its ease of understanding and use, and it should accurately represent the system's behavior. Different mathematical models exist, and they are used in a wide variety of applications [4][5][6][7][8][9][10][11][12][13]. ANFIS is an intelligent technique utilized to model and predict complex systems.…”

Section: Introductionmentioning

confidence: 99%

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Mohamed,

Atyia

2024

Phys. Scr.

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The current research investigates the conductivity σ AC of glassy As4Ge24Te72 thin films through a combined experimental and theoretical approach. It sheds light on the complex relationship between σ AC , frequency, temperature, and film thickness. Analysis reveals that σ AC follows ω r law and exhibits hopping behavior. To effectively model and predict σ AC , an adaptive neuro-fuzzy inference system (ANFIS) is utilized. The ANFIS model successfully simulates the experimental data, showing high accuracy. Additionally, the prediction of experimentally measured values of σ AC is processed as a testing step and provides acceptable results. That enables ANFIS to enlarge the scale and complete the missing parts in the trained datasets by predictions for unmeasured σ AC values. The ANFIS network was built using MATLAB-R2017a. It consists of two inputs (frequency and temperature) and one output (AC conductivity). The precision is confirmed by calculating the modeling errors across the training process. The mean absolute errors MAE through the modeling process are not exceeding 10 − 5 . This average error value demonstrates the model’s effective ability. The alignment between experimental and ANFIS modeling results open up exciting possibilities for future research. By predicting the properties of the understudy material using ANFIS model, we can pave the way for more compact and efficient electrical devices.

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Prediction of Turkish mutual funds’ net asset value using the fund portfolio distribution

YILMAZ>¹,

Orbak²

2023

Neural Comput & Applic

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Prediction and optimization of electrical conductivity for polymer-based composites using design of experiment and artificial neural networks

Cited by 11 publications

References 47 publications

Investigating the Electrical and Mechanical Properties of Polystyrene (PS)/Untreated SWCNT Nanocomposite Films

Investigating the Electrical and Mechanical Properties of Polystyrene (PS)/Untreated SWCNT Nanocomposite Films

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Prediction of Turkish mutual funds’ net asset value using the fund portfolio distribution

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