Determining electrical percolation threshold of randomly distributed conductor materials in polymer composites via pathfinding algorithms
Source abstract
Abstract
Recently, polymer composite-based flexible electronic designs and applications have been gradually increased. Production process flows cause cost in time and budget without predefined percolation threshold that is the most critical level of polymer conductor composites (PCC). Also, the onset of the percolation threshold is defined in this study which is the starting and avalanche point of the conduction regime. By knowing the percolation threshold, the composite ingredient ratio can be altered for proper conduction. This yields a lean conductive composite design with the potential to be utilized prior to fabrication. Determining the main recipe of composite becomes faster and easier. This article evaluates the methodologies for finding the onset of the percolation threshold and provides a reduction in the number of experiments to determine the recipe. Experimentally, materials containing different ratios of carbon black (CB) were tested. Simulation software is created, and the results are verified with the experimental findings. Accordingly, the percolation onset was around 5.6% by weight. To create a suitable methodology, models based on resistive networks were worked out. A*, A* Depth, Best First Search, Breadth-First Search, Dijkstra's Fastest Pathfinding Algorithms are run for different CB ratios. With this study, a feasible simulation was designed for all composites in which the percolation threshold changes depending on the tunneling distance.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Advanced Memory and Neural Computing · Physical Unclonable Functions (PUFs) and Hardware Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (60)
Show source-provided references
1. An ultra-stretchable, highly sensitive and biocompatible capacitive strain sensor from an ionic nanocomposite for on-skin monitoring10.1039/C8NR08589G ↗
2. Flexible sliding sensor for simultaneous monitoring deformation and displacement on a robotic hand/arm10.1016/j.nanoen.2020.104764 ↗
3. Wearable magnetic field sensors for flexible electronics10.1002/adma.201405027 ↗
4. Analysis and design of stopband FSS unit cell on textile substrates10.1109/LEMCPA.2020.3031985 ↗
5. Modeling percolation in polymer nanocomposites by stochastic microstructuring10.3390/ma8105334 ↗
6. Electrical conductivity modeling of carbon-filled liquid-crystalline polymer composites10.1002/app.24748 ↗
7. Assessment of percolation threshold simulation for individual and hybrid nanocomposites of carbon nanotubes and carbon black10.1590/1980-5373-mr-2016-1084 ↗
8. Correlated electrical conductivity and mechanical property analysis of high-density polyethylene filled with graphite and carbon fiber10.1016/S0032-3861(02)00043-5 ↗
9. Synergistic effects of carbon fillers in electrically conductive nylon 6,6 and polycarbonate based resins10.1002/pc.10488 ↗
10. A review and analysis of electrical percolation in carbon nanotube polymer composites10.1016/j.compscitech.2008.06.018 ↗
11. F. Kazemi, Z. Mohammadpour, S. M. Naghib, Y. Zare, and K. Y. Rhee, “Percolation onset and electrical conductivity for a multiphase system containing carbon nanotubes and nanoclay,” J. Mater. Res. Technol., vol. 15, pp. 1777–1788, Nov. 2021, doi: 10.1016/J.JMRT.2021.08.131.10.1016/j.jmrt.2021.08.131 ↗
12. Kanoun · 2014
13. Review on conductive polymer/CNTs nanocomposites based flexible and stretchable strain and pressure sensors10.3390/s21020341 ↗
14. The role of conductive dopants in polymer cholesteric liquid crystals10.20450/mjcce.2014.605 ↗
15. Developments and Modeling of Electrical Conductivity in Composites
16. Universal resistivity-strain dependence of carbon nanotube/polymer composites10.1103/PhysRevB.76.195433 ↗
17. A study of finite size effects and periodic boundary conditions to simulations of a novel theoretical self-consistent mean-field approach10.1002/mats.201900023 ↗
18. Micromechanics modeling of the electrical conductivity of carbon nanotube (CNT)-polymer nanocomposites10.1016/j.compositesa.2012.12.008 ↗
19. An analytical model of effective electrical conductivity of carbon nanotube composites10.1063/1.2857468 ↗
20. Modeling and characterization of the electrical conductivity of carbon nanotube-based polymer composites10.1016/j.polymer.2011.06.046 ↗
21. Electrical percolation of nanoparticle-polymer composites10.1016/j.commatsci.2018.03.051 ↗
22. Monte Carlo simulations of effective electrical conductivity in short-fiber composites10.1063/1.2828180 ↗
23. A three-dimensional Monte Carlo model for electrically conductive polymer matrix composites filled with curved fibers10.1016/j.polymer.2008.07.034 ↗
24. Characteristics of the electrical percolation in carbon nanotubes/polymer nanocomposites10.1021/jp207388n ↗
25. A numerical simulation of the electrical resistivity of carbon black filled rubber10.1016/S0032-3861(99)00894-0 ↗
26. Percolation and conduction10.1103/RevModPhys.45.574 ↗
27. A. A. Saberi, “Recent Advances in Percolation Theory and its Applications,” Physics Reports, vol. vol. 578. Elsevier, pp. 1–32, May 24, 2015, doi: 10.1016/j.physrep.2015.03.003.10.1016/j.physrep.2015.03.003 ↗
28. Experimental and Monte Carlo simulation studies on percolation behaviour of a shape memory polyurethane carbon black nanocomposite10.1088/1361-665X/ab083b ↗
29. Study of optimal path finding techniques
30. Hierarchical optimization of optimal path finding for transportation applications
31. L. Lu and G. Wang, “A study on multi-agent supply chain framework based on network economy,” Comput. Ind. Eng., vol. 54, no. 2, pp. 288–300, Mar. 2008, doi: 10.1016/J.CIE.2007.07.010.10.1016/j.cie.2007.07.010 ↗
32. PATHFINDING IN MULTI-ROBOT SYSTEMS: SOLUTION AND APPLICATIONS
33. Path planning in construction sites: performance evaluation of the Dijkstra, A∗, and GA search algorithms10.1016/S1474-0346(03)00018-1 ↗
34. Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks10.1109/6979.994796 ↗
35. Pathfinding of 2D & 3D game real-time strategy with Depth Direction A* algorithm for multi-layer
36. Generalized best-first search strategies and the optimality of A10.1145/3828.3830 ↗
37. Mason · 2011
38. Some applications of the percolation theory: brief review of the century beginning
39. Percolation theory10.1088/0034-4885/43/7/001 ↗
40. Direction-optimizing breadth-first search
41. Electrical percolation threshold of carbon black in a polymer matrix and its application to antistatic fibre
42. Novel theoretical self-consistent mean-field approach to describe the conductivity of carbon fiber filled thermoplastics—PART I—theory10.1002/mats.201700104 ↗
43. Percolation and transport properties in the mechanically deformed composites filled with carbon nanotubes
44. A. Allaoui, S. V. Hoa, and M. D. Pugh, “The electronic transport properties and microstructure of carbon nanofiber/epoxy composites,” Compos. Sci. Technol., vol. 68, no. 2, pp. 410–416, Feb. 2008, doi: 10.1016/J.COMPSCITECH.2007.06.028.10.1016/j.compscitech.2007.06.028 ↗
45. Highly sensitive ultrathin flexible thermoplastic polyurethane/carbon black fibrous film strain sensor with adjustable scaffold networks10.1007/s40820-021-00592-9 ↗
46. Tunneling conductivity and piezoresistivity of composites containing randomly dispersed conductive nano-platelets10.3390/ma7042501 ↗
47. S. A. E. Kassim, M. E. Achour, L. C. Costa, and F. Lahjomri, “Modelling the DC electrical conductivity of polymer/carbon black composites,” J. Electrost., vol. 72, no. 3, pp. 187–191, Jun. 2014, doi: 10.1016/j.elstat.2014.02.002.10.1016/j.elstat.2014.02.002 ↗
48. Contemporary applications of carbon black-filled polymer composites: an overview of essential aspects10.1177/8756087917725773 ↗
49. Electrical conductivity and percolation threshold of hybrid carbon/polymer composites10.1002/app.41744 ↗
50. AC and DC percolative conductivity of single wall carbon nanotube polymer composites10.1002/polb.20597 ↗
51. Conductive network formation in the melt of carbon nanotube/thermoplastic polyurethane composite10.1016/j.compscitech.2008.11.039 ↗
52. Progress on the morphological control of conductive network in conductive polymer composites and the use as electroactive multifunctional materials10.1016/j.progpolymsci.2013.07.007 ↗
53. Rubber base adhesives
54. A new insight in determining the percolation threshold of electrical conductivity for extrinsically conducting polymer composites through different sigmoidal models10.3390/polym9100527 ↗
55. A numerical study on electrical percolation of polymer-matrix composites with hybrid fillers of carbon nanotubes and carbon black10.1155/2014/614797 ↗
56. Effect of polymer-filler interface interactions on percolation conductivity of thermoplastics filled with carbon black10.1163/156855497X00145 ↗
57. Electrical conductivity modeling of polypropylene composites filled with carbon black and acetylene black
58. Analysis of DC electrical conductivity models of carbon nanotube-polymer composites with potential application to nanometric electronic devices10.1155/2013/179538 ↗
59. Analytical functions for the dc and ac conductivity of conductor-insulator composites10.1023/A:1009954017351 ↗
60. A flexible and low-cost tactile sensor produced by screen printing of carbon black/PVA composite on cellulose paper10.3390/s20102908 ↗