Identification of current-carrying part of a random resistor network: electrical approaches vs. graph theory algorithms

Tarasevich, Yuri Yu.; Burmistrov, Andrei S.; Goltseva, Valeria A.; Gordeev, I I; Serbin, V. I.; Sizova, A A; Vodolazskaya, Irina V.; Zholobov, D A

doi:10.1088/1742-6596/955/1/012021

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…1c) and the effective current I eff flowing through the electrode. We define the conduction network (also denoted as 'electrical backbone', 16,20 'current carrying backbone', 15 or 'current carrying path' 7,21 in the literature) as the set of resistor elements actually carrying electric current. Fig.…”

Section: Numerical Realizations and Macroscopic Characterizationmentioning

confidence: 99%

“…10 Additional inhomogeneities are detectable at local scales, 11 in the order of NW length (tens of micrometers) or lower (NW diameter or NW junction size). Analytical 8,[12][13][14][15][16] and computational 8,9,[14][15][16][17][18][19][20][21][22][23] modeling strategies are commonly used to interpret and predict the response of NW electrodes. Modeling studies confirm that only a subset of the NWs, which is traditionally referred to as the conduction network, is involved in the electrical conduction process.…”

Section: Introductionmentioning

confidence: 99%

“…Modeling studies confirm that only a subset of the NWs, which is traditionally referred to as the conduction network, is involved in the electrical conduction process. 7,16,20,21 Numerical simulations [7][8][9]14,15,23 show that a uniform distribution of NWs does not imply a uniform distribution of the conduction network (and neither a uniform electrical current distribution). 15 An electrical homogeneity prediction based on the inspection of the observable geometry is therefore not recommended.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative electrical homogeneity assessment of nanowire transparent electrodes

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Section: Numerical Realizations and Macroscopic Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“…[For a review of different algorithms intended for backbone identification, see Ref. 58.] At the next stage, an adjacency matrix was formed for the geometrical backbone.…”

Section: Discrete Approach (Model I)mentioning

confidence: 99%

Electrical conductance of two-dimensional composites with embedded rodlike fillers: an analytical consideration and comparison of two computational approaches

Tarasevich,

Vodolazskaya,

Eserkepov

et al. 2019

Preprint

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Using Monte Carlo simulation, we studied the electrical conductance of two-dimensional films. The films consisted of a poorly conductive host matrix and highly conductive rodlike fillers (rods). The rods were of various lengths, obeying a log-normal distribution. They were allowed to be aligned along a given direction. The impacts of length dispersity and the extent of rod alignment on the insulator-to-conductor phase transition were studied. Two alternative computational approaches were compared. Within Model I, the films were transformed into resistor networks with regular structures and randomly distributed conductances. Within Model II, the films were transformed into resistor networks with irregular structures but with equal conductivities of the conductors. Comparison of the models evidenced similar behavior in both models when the concentration of fillers exceeded the percolation threshold. Some analytical results were obtained: (i) the relationship between the number of fillers per unit area and the transmittance of the film within Model I, (ii) the electrical conductance of the film for dense networks within Model II.

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Electrical conductance of two-dimensional composites with embedded rodlike fillers: An analytical consideration and comparison of two computational approaches

Tarasevich

Vodolazskaya

Eserkepov

et al. 2019

Journal of Applied Physics

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Using Monte Carlo simulation, we studied the electrical conductance of two-dimensional films. The films consisted of a poorly conductive host matrix and highly conductive rodlike fillers (rods). The rods were of various lengths, obeying a log-normal distribution. They were allowed to be aligned along a given direction. The impacts of the length dispersity and the extent of the rod alignment on the insulator-to-conductor phase transition were studied. Two alternative computational approaches were compared. Within Model I, the films were transformed into resistor networks with regular structures and randomly distributed conductances. Within Model II, the films were transformed into resistor networks with irregular structures but with equal conductivities of the conductors. A comparison of the models evidenced similar behavior in both models when the concentration of fillers exceeded the percolation threshold. However, a fairly fine mesh should be used in Model I to obtain a reasonable estimation of the electrical conductance. The electrical conductance is slightly overestimated in Model I. In anisotropic systems, the length dispersity of fillers has a more pronounced effect on the electrical conductance along the direction of the rod alignment. Some analytical results were obtained: (i) the relationship between the number of fillers per unit area and the transmittance of the films within Model I and (ii) the electrical conductance of the films for dense networks within Model II.

show abstract

Identification of current-carrying part of a random resistor network: electrical approaches vs. graph theory algorithms

Cited by 5 publications

References 23 publications

Quantitative electrical homogeneity assessment of nanowire transparent electrodes

Quantitative electrical homogeneity assessment of nanowire transparent electrodes

Electrical conductance of two-dimensional composites with embedded rodlike fillers: an analytical consideration and comparison of two computational approaches

Electrical conductance of two-dimensional composites with embedded rodlike fillers: An analytical consideration and comparison of two computational approaches

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