Skip to main content

Advertisement

SpringerLink
Log in

Molecular dynamics simulation of the mechanical and thermal properties of phagraphene nanosheets and nanotubes: a review

  • Invited Review
  • Published:
Journal of Materials Science Aims and scope Submit manuscript

Abstract

Phagraphene is a newly proposed two-dimensional allotrope of carbon. Its structure resembles that of a defective graphene sheet. The unit cell structure of phagraphene consists of a 5–6–7 ring sequence due to which it possesses lower energy than most carbon allotropes. Phagraphene has properties, namely thermal conductivity of 218 ± 20 W/mK and 285 ± 29 W/mK, along armchair and zigzag directions, respectively, a tensile strength of 85 ± 2 GPa, the elastic modulus of 870 ± 15 GPa along armchair and 800 ± 14 GPa along zigzag direction, and fracture strain of the same order as that of pristine graphene. Density functional theory and molecular dynamics simulations have proved the unique electronic properties of phagraphene, namely direction-dependent Dirac cones, tunable Fermi velocities, quasi-direct band gap, to be comparable with pristine graphene. Based on molecular dynamics and density functional theory investigations, phagraphene is capable of being used in Li-based batteries and thermoelectric devices as anode material due to its good adsorption properties and remarkably good doping characteristics. Such characteristics can lead to a future trend of analyzing such allotropes because phagraphene can provide potential applications in atmospheric pollutant-detecting and drug-carrying properties in ailments like cancer. This paper is a nascent one discussing all the previous works on molecular dynamics simulations of phagraphene and tries to serve as the leading one for researchers interested in this field. This paper summarizes the mechanical and thermal properties of phagraphene estimated using molecular dynamics simulations as studied in previous works. The study shows that molecular dynamics has a good application in studying such a novel carbon allotrope. The discussions made in this paper thematize phagraphene’s properties as a next-generation material and can help the researchers in a qualitative overview.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15

Similar content being viewed by others

Data and code availability

The raw/processed data required to produce these findings cannot be shared at this time due to legal or ethical reasons. The data will be made available on request.

Abbreviations

PhaG:

Phagraphene

PhaNT:

Phagraphene nanotubes

CNT:

Carbon nanotubes

MD:

Molecular dynamics

VMD:

Visual molecular dynamics

NEMD:

Nonequilibrium molecular dynamics

FS:

Fracture strain

UTS:

Ultimate tensile strength

LAMMPS:

Large-scale atomic/molecular massively parallel simulator

PBC:

Periodic boundary conditions

SIF:

Stress intensity factor

FP:

First principles

DFT:

Density functional theory

TB:

Tight binding

DOS:

Density of states

SWCNT:

Single-walled carbon nanotubes

MWCNT:

Multi-walled carbon nanotubes

ReaxFF:

Reaction force field

LIB:

Lithium ion battery

P–K stress:

Piola–Kirchhoff stress

BZ:

Brillouin zone

LJ potential:

Lennard–Jones potential

MD-AIREBO:

Molecular dynamics-adaptive intermolecular reactive empirical bond order

PM3 :

Parametric method 3

MLIPs:

Machine learning interatomic potentials

References

  1. Frackowiak E (2007) Carbon materials for supercapacitor application. Phys Chem Chem Phys 9(15):1774–1785

    Article  CAS  Google Scholar 

  2. Yu C, Choi K, Yin L, Grunlan JC (2011) Light-weight flexible carbon nanotube based organic composites with large thermoelectric power factors. ACS Nano 5(10):7885–7892

    Article  CAS  Google Scholar 

  3. Candelaria SL et al (2012) Nanostructured carbon for energy storage and conversion. Nano Energy 1(2):195–220

    Article  CAS  Google Scholar 

  4. Zhai Y, Dou Y, Zhao D, Fulvio PF, Mayes RT, Dai S (2011) Carbon materials for chemical capacitive energy storage. Adv Mater 23(42):4828–4850

    Article  CAS  Google Scholar 

  5. Avouris P, Chen Z, Perebeinos V (2007) Carbon-based electronics. Nat Nanotechnol 2(10):605–615

    Article  CAS  Google Scholar 

  6. Stoller MD, Park S, Zhu Y, An J, Ruoff RS (2008) Graphene-based ultracapacitors. Nano Lett 8(10):3498–3502

    Article  CAS  Google Scholar 

  7. Cao D et al (2022) The effect of resin uptake on the flexural properties of compression molded sandwich composites. Wind Energy 25(1):71–93

    Article  Google Scholar 

  8. Wang X, et al (2021) The interfacial shear strength of carbon nanotube sheet modified carbon fiber composites. In: Challenges in mechanics of time dependent materials, volume 2: proceedings of the 2020 annual conference on experimental and applied mechanics, 2021: Springer, pp 25–32

  9. Cao D, Malakooti S, Kulkarni VN, Ren Y, Lu H (2021) Nanoindentation measurement of core–skin interphase viscoelastic properties in a sandwich glass composite. Mech Time-Depend Mater 25:353–363

    Article  CAS  Google Scholar 

  10. Cox K, Echtermeyer A (2012) Structural design and analysis of a 10MW wind turbine blade. Energy Procedia 24:194–201

    Article  Google Scholar 

  11. Han JH et al (2008) Monitoring multiwalled carbon nanotube exposure in carbon nanotube research facility. Inhalation Toxicol 20(8):741–749

    Article  Google Scholar 

  12. Han J, He X, Du S (1995) Oxidation and ablation of 3D carbon-carbon composite at up to 3000 C. Carbon 33(4):473–478

    Article  CAS  Google Scholar 

  13. Schlapbach L, Züttel A (2011) Hydrogen-storage materials for mobile applications. In: Materials for sustainable energy: a collection of peer-reviewed research and review articles from nature publishing group. World Scientific, pp 265–270

  14. Abioye AM, Ani FN (2015) Recent development in the production of activated carbon electrodes from agricultural waste biomass for supercapacitors: a review. Renew Sustain Energy Rev 52:1282–1293. https://doi.org/10.1016/j.rser.2015.07.129

    Article  CAS  Google Scholar 

  15. Kavaliauskas Z, Marcinauskas L, Valatkevicius P (2011) Formation and characterization of carbon and nickel oxide/carbon composites for supercapacitors. Acta Phys Pol A 119(2):253–255

    Article  CAS  Google Scholar 

  16. Ajayan PM, Zhou OZ (2001) Applications of carbon nanotubes. In: Carbon nanotubes: synthesis, structure, properties, and applications, pp 391–425

  17. Iijima S (2002) Carbon nanotubes: past, present, and future. Physica B 323(1–4):1–5

    Article  CAS  Google Scholar 

  18. Meyyappan M (2004) Carbon nanotubes: science and applications. CRC Press, Boca Raton

    Book  Google Scholar 

  19. Dresselhaus G, Dresselhaus MS, Saito R (1998) Physical properties of carbon nanotubes. World scientific, Singapore

    Google Scholar 

  20. Iijima S, Ichihashi T (1993) Single-shell carbon nanotubes of 1-nm diameter. Nature 363(6430):603–605

    Article  CAS  Google Scholar 

  21. Song Z et al (2019) Alginate-templated synthesis of CoFe/carbon fiber composite and the effect of hierarchically porous structure on electromagnetic wave absorption performance. Carbon 151:36–45

    Article  CAS  Google Scholar 

  22. Dichtel WR, Miljanić OŠ, Spruell JM, Heath JR, Stoddart JF (2006) Efficient templated synthesis of donor–acceptor rotaxanes using click chemistry. J Am Chem Soc 128(32):10388–10390

    Article  CAS  Google Scholar 

  23. Suzuki M (1994) Activated carbon fiber: fundamentals and applications. Carbon 32(4):577–586

    Article  CAS  Google Scholar 

  24. Moreno-Castilla C, Maldonado-Hódar F (2005) Carbon aerogels for catalysis applications: an overview. Carbon 43(3):455–465

    Article  CAS  Google Scholar 

  25. Pekala R et al (1998) Carbon aerogels for electrochemical applications. J Non-Cryst Solids 225:74–80

    Article  CAS  Google Scholar 

  26. Zhang LL, Zhao X (2009) Carbon-based materials as supercapacitor electrodes. Chem Soc Rev 38(9):2520–2531

    Article  CAS  Google Scholar 

  27. Krasheninnikov A, Banhart F (2007) Engineering of nanostructured carbon materials with electron or ion beams. Nat Mater 6(10):723–733

    Article  CAS  Google Scholar 

  28. Wang X et al (2021) The interfacial shear strength of carbon nanotube sheet modified carbon fiber composites. In: Cham MS, Amirkhizi A (eds) Challenges in mechanics of time dependent materials, vol 2. Springer, Berlin, pp 25–32

    Chapter  Google Scholar 

  29. Kroto HW, Heath JR, O’Brien SC, Curl RF, Smalley RE (1985) C60: buckminsterfullerene. Nature 318(6042):162–163

    Article  CAS  Google Scholar 

  30. Geim AK (2009) Graphene: status and prospects. Science 324(5934):1530–1534

    Article  CAS  Google Scholar 

  31. Iijima S (1991) Helical microtubules of graphitic carbon. Nature 354(6348):56–58

    Article  CAS  Google Scholar 

  32. Itkis ME, Yu A, Haddon RC (2008) Single-walled carbon nanotube thin film Emitter−detector integrated optoelectronic device. Nano Lett 8(8):2224–2228

    Article  CAS  Google Scholar 

  33. Liang F, Chen B (2010) A review on biomedical applications of single-walled carbon nanotubes. Curr Med Chem 17(1):10–24

    Article  CAS  Google Scholar 

  34. Ouyang M, Huang J-L, Lieber CM (2002) Fundamental electronic properties and applications of single-walled carbon nanotubes. Acc Chem Res 35(12):1018–1025

    Article  CAS  Google Scholar 

  35. Srivastava N, Li H, Kreupl F, Banerjee K (2009) On the applicability of single-walled carbon nanotubes as VLSI interconnects. IEEE Trans Nanotechnol 8(4):542–559

    Article  Google Scholar 

  36. Tkac J, Whittaker JW, Ruzgas T (2007) The use of single walled carbon nanotubes dispersed in a chitosan matrix for preparation of a galactose biosensor. Biosens Bioelectron 22(8):1820–1824

    Article  CAS  Google Scholar 

  37. Azharuddin M et al (2019) A repertoire of biomedical applications of noble metal nanoparticles. Chem Commun 55(49):6964–6996

    Article  CAS  Google Scholar 

  38. Meyyappan M, Delzeit L, Cassell A, Hash D (2023) Carbon nanotube growth by PECVD: a review. Plasma Sources Sci Technol 12(2):205–216. https://doi.org/10.1088/0963-0252/12/2/312

    Article  Google Scholar 

  39. Meyyappan M, Srivastava D (2003) Carbon nanotubes. In: Handbook of nanoscience," engineering, and technology”. CRC Press LLC, Boca Raton

  40. Balandin AA et al (2008) Superior thermal conductivity of single-layer graphene. Nano Lett 8(3):902–907

    Article  CAS  Google Scholar 

  41. Novoselov KS et al (2005) Two-dimensional gas of massless Dirac fermions in graphene. Nature 438(7065):197–200

    Article  CAS  Google Scholar 

  42. Zhu Y et al (2010) Graphene and graphene oxide: synthesis, properties, and applications. Adv Mater 22(35):3906–3924

    Article  CAS  Google Scholar 

  43. Lee C, Wei X, Kysar JW, Hone J (2008) Measurement of the elastic properties and intrinsic strength of monolayer graphene. Science 321(5887):385–388

    Article  CAS  Google Scholar 

  44. Potts JR, Dreyer DR, Bielawski CW, Ruoff RS (2011) Graphene-based polymer nanocomposites. Polymer 52(1):5–25

    Article  CAS  Google Scholar 

  45. Papageorgiou DG, Kinloch IA, Young RJ (2015) Graphene/elastomer nanocomposites. Carbon 95:460–484

    Article  CAS  Google Scholar 

  46. Schwierz F (2010) Graphene transistors. Nat Nanotechnol 5(7):487–496

    Article  CAS  Google Scholar 

  47. Bunch JS et al (2007) Electromechanical resonators from graphene sheets. Science 315(5811):490–493

    Article  CAS  Google Scholar 

  48. Raju APA et al (2014) Wide-area strain sensors based upon graphene-polymer composite coatings probed by Raman spectroscopy. Adv Func Mater 24(19):2865–2874

    Article  CAS  Google Scholar 

  49. Eda G, Chhowalla M (2010) Chemically derived graphene oxide: towards large-area thin-film electronics and optoelectronics. Adv Mater 22(22):2392–2415

    Article  CAS  Google Scholar 

  50. Yoo JJ et al (2011) Ultrathin planar graphene supercapacitors. Nano Lett 11(4):1423–1427

    Article  CAS  Google Scholar 

  51. Brownson DA, Banks CE (2012) Fabricating graphene supercapacitors: highlighting the impact of surfactants and moieties. Chem Commun 48(10):1425–1427

    Article  CAS  Google Scholar 

  52. Patchkovskii S, Tse JS, Yurchenko SN, Zhechkov L, Heine T, Seifert G (2005) Graphene nanostructures as tunable storage media for molecular hydrogen. Proc Natl Acad Sci 102(30):10439–10444

    Article  CAS  Google Scholar 

  53. Wang X, Zhi L, Müllen K (2008) Transparent, conductive graphene electrodes for dye-sensitized solar cells. Nano Lett 8(1):323–327

    Article  CAS  Google Scholar 

  54. Miao X et al (2012) High efficiency graphene solar cells by chemical doping. Nano Lett 12(6):2745–2750

    Article  CAS  Google Scholar 

  55. Shao Y, Wang J, Wu H, Liu J, Aksay IA, Lin Y (2010) Graphene based electrochemical sensors and biosensors: a review. Electroanal Int J Devot Fundam Pract Aspects Electroanal 22(10):1027–1036

    CAS  Google Scholar 

  56. Avouris P, Xia F (2012) Graphene applications in electronics and photonics. MRS Bull 37(12):1225–1234

    Article  CAS  Google Scholar 

  57. Kuila T, Bose S, Mishra AK, Khanra P, Kim NH, Lee JH (2012) Chemical functionalization of graphene and its applications. Prog Mater Sci 57(7):1061–1105

    Article  CAS  Google Scholar 

  58. Geim AK, Novoselov KS (201) The rise of graphene. In Nanoscience and technology: a collection of reviews from nature journals. World Scientific, pp 11–19

  59. Withers F, Dubois M, Savchenko AK (2010) Electron properties of fluorinated single-layer graphene transistors. Phys Rev B 82(7):073403–073406

    Article  Google Scholar 

  60. Fradkin E (1986) Critical behavior of disordered degenerate semiconductors. I. Models, symmetries, and formalism. Phys Rev B 33(5):3257–3262

    Article  CAS  Google Scholar 

  61. Hass J, De Heer W, Conrad E (2008) The growth and morphology of epitaxial multilayer graphene. J Phys: Condens Matter 20(32):323202

    Google Scholar 

  62. Júnior MP et al (2020) On the elastic properties of single-walled phagraphene nanotubes. Chem Phys Lett 756:137830

    Article  Google Scholar 

  63. De Sousa JM, Aguiar AL, Girao EC, Fonseca AF, Sousa Filho AG, Galvao DS (2018) Mechanical properties of phagraphene membranes: a fully atomistic molecular dynamics investigation. MRS Adv 3(1–2):67–72

    Article  Google Scholar 

  64. Wang Z et al (2015) Phagraphene: a low-energy graphene allotrope composed of 5–6–7 carbon rings with distorted dirac cones. Nano Lett 15(9):6182–6186

    Article  CAS  Google Scholar 

  65. Lopez-Bezanilla A (2016) Strain-mediated modification of phagraphene Dirac cones. J Phys Chem C 120(30):17101–17105

    Article  CAS  Google Scholar 

  66. Pereira LFC, Mortazavi B, Makaremi M, Rabczuk T (2016) Anisotropic thermal conductivity and mechanical properties of phagraphene: a molecular dynamics study. RSC Adv 6(63):57773–57779

    Article  CAS  Google Scholar 

  67. Sun H, Mukherjee S, Singh CV (2016) Mechanical properties of monolayer penta-graphene and phagraphene: a first-principles study. Phys Chem Chem Phys 18(38):26736–26742

    Article  CAS  Google Scholar 

  68. Shirazi AHN (2019) Molecular dynamics investigation of mechanical properties of single-layer phagraphene. Front Struct Civ Eng 13(2):495–503

    Article  Google Scholar 

  69. Podlivaev AI, Openov LA (2016) Possible nonplanar structure of phagraphene and its thermal stability. JETP Lett 103(3):185–189

    Article  CAS  Google Scholar 

  70. Openov L, Podlivaev A (2017) Negative Poisson’s ratio in a nonplanar phagraphene. Phys Solid State 59(6):1267–1269

    Article  CAS  Google Scholar 

  71. Baughman R, Eckhardt H, Kertesz M (1987) Structure-property predictions for new planar forms of carbon: layered phases containing sp2 and sp atoms. J Chem Phys 87(11):6687–6699

    Article  CAS  Google Scholar 

  72. Bhattacharya B, Seriani N, Sarkar U (2019) Raman and IR signature of pristine and BN-doped γ-graphyne from first-principle. Carbon 141:652–662

    Article  CAS  Google Scholar 

  73. Cranford SW, Buehler MJ (2011) Mechanical properties of graphyne. Carbon 49(13):4111–4121. https://doi.org/10.1016/j.carbon.2011.05.024

    Article  CAS  Google Scholar 

  74. Zhang S, Zhou J, Wang Q, Chen X, Kawazoe Y, Jena P (2015) Penta-graphene: a new carbon allotrope. Proc Natl Acad Sci 112(8):2372–2377

    Article  CAS  Google Scholar 

  75. Xiao B, Li Y-C, Yu X-F, Cheng J-B (2016) Penta-graphene: a promising anode material as the Li/Na-ion battery with both extremely high theoretical capacity and fast charge/discharge rate. ACS Appl Mater Interfaces 8(51):35342–35352

    Article  CAS  Google Scholar 

  76. Yin W-J et al (2013) R-graphyne: a new two-dimensional carbon allotrope with versatile Dirac-like point in nanoribbons. J Mater Chem A 1(17):5341–5346

    Article  CAS  Google Scholar 

  77. Majidi R (2017) Density functional theory study on structural and mechanical properties of graphene, T-graphene, and R-graphyne. Theor Chem Accounts 136(9):109. https://doi.org/10.1007/s00214-017-2148-1

    Article  CAS  Google Scholar 

  78. Liu Y, Wang G, Huang Q, Guo L, Chen X (2012) Structural and electronic properties of T graphene: a two-dimensional carbon allotrope with tetrarings. Phys Rev Lett 108(22):225505

    Article  Google Scholar 

  79. Xu P, Yang J, Wang K, Zhou Z, Shen P (2012) Porous graphene: properties, preparation, and potential applications. Chin Sci Bull 57(23):2948–2955

    Article  CAS  Google Scholar 

  80. Wang Y et al (2016) Porous graphene for high capacity lithium ion battery anode material. Appl Surf Sci 363:318–322

    Article  CAS  Google Scholar 

  81. Jiang D-E, Cooper VR, Dai S (2009) Porous graphene as the ultimate membrane for gas separation. Nano Lett 9(12):4019–4024

    Article  CAS  Google Scholar 

  82. Brunetto G, Autreto P, Machado L, Santos B, Dos Santos RP, Galvao DS (2012) Nonzero gap two-dimensional carbon allotrope from porous graphene. J Phys Chem C 116(23):12810–12813

    Article  CAS  Google Scholar 

  83. Du A, Zhu Z, Smith SC (2010) Multifunctional porous graphene for nanoelectronics and hydrogen storage: new properties revealed by first principle calculations. J Am Chem Soc 132(9):2876–2877

    Article  CAS  Google Scholar 

  84. Postma HWC (2010) Rapid sequencing of individual DNA molecules in graphene nanogaps. Nano Lett 10(2):420–425

    Article  CAS  Google Scholar 

  85. Bhattacharya D, Jana D (2020) Twin T-graphene: a new semiconducting 2D carbon allotrope. Phys Chem Chem Phys 22(18):10286–10294

    Article  CAS  Google Scholar 

  86. Jiang J-W et al (2017) Twin graphene: a novel two-dimensional semiconducting carbon allotrope. Carbon 118:370–375

    Article  CAS  Google Scholar 

  87. Li X, Wang Q, Jena P (2017) ψ-Graphene: a new metallic allotrope of planar carbon with potential applications as anode materials for lithium-ion batteries. J Phys Chem Lett 8(14):3234–3241

    Article  CAS  Google Scholar 

  88. Yu Y-X (2013) Graphenylene: a promising anode material for lithium-ion batteries with high mobility and storage. J Mater Chem A 1(43):13559–13566

    Article  CAS  Google Scholar 

  89. Chung D (2002) Review graphite. J Mater Sci 37(8):1475–1489

    Article  CAS  Google Scholar 

  90. Li M, Lu J, Chen Z, Amine K (2018) 30 years of lithium-ion batteries. Adv Mater 30(33):1800561

    Article  Google Scholar 

  91. Blomgren GE (2016) The development and future of lithium ion batteries. J Electrochem Soc 164(1):A5019

    Article  Google Scholar 

  92. Scrosati B, Hassoun J, Sun Y-K (2011) Lithium-ion batteries. A look into the future. Energy Environ Sci 4(9):3287–3295

    Article  CAS  Google Scholar 

  93. Li J, Daniel C, Wood D (2011) Materials processing for lithium-ion batteries. J Power Sources 196(5):2452–2460

    Article  CAS  Google Scholar 

  94. Ohzuku T, Brodd RJ (2007) An overview of positive-electrode materials for advanced lithium-ion batteries. J Power Sources 174(2):449–456

    Article  CAS  Google Scholar 

  95. Mishra A et al (2018) Electrode materials for lithium-ion batteries. Mater Sci Energy Technol 1(2):182–187

    Google Scholar 

  96. Kamali-Heidari E, Kamyabi-Gol A, Heydarzadeh Sohi M, Ataie A (2018) Electrode materials for lithium ion batteries: a review. J Ultrafine Grained Nanostruct Mater 51(1):1–12

    Google Scholar 

  97. Datta S, Jo C, De Volder M, Torrente-Murciano L (2020) Morphological control of nanostructured V2O5 by deep eutectic solvents. ACS Appl Mater Interfaces 12(16):18803–18812

    Article  CAS  Google Scholar 

  98. Huang X, Rui X, Hng HH, Yan Q (2015) Vanadium pentoxide-based cathode materials for lithium-ion batteries: morphology control, carbon hybridization, and cation doping. Part Part Syst Charact 32(3):276–294

    Article  CAS  Google Scholar 

  99. Yang Y, Shu D, Yu H, Xia X, Lin Z (1997) Investigations of lithium manganese oxide materials for lithium-ion batteries. J Power Sources 65(1–2):227–230

    Article  CAS  Google Scholar 

  100. Shu J et al (2010) A new look at lithium cobalt oxide in a broad voltage range for lithium-ion batteries. J Phys Chem C 114(7):3323–3328

    Article  CAS  Google Scholar 

  101. Nitta N, Wu F, Lee JT, Yushin G (2015) Li-ion battery materials: present and future. Mater Today 18(5):252–264

    Article  CAS  Google Scholar 

  102. Yan Z et al (2017) Co3O4/Co nanoparticles enclosed graphitic carbon as anode material for high performance Li-ion batteries. Chem Eng J 321:495–501

    Article  CAS  Google Scholar 

  103. Zheng H, Qu Q, Zhang L, Liu G, Battaglia VS (2012) Hard carbon: a promising lithium-ion battery anode for high temperature applications with ionic electrolyte. RSC Adv 2(11):4904–4912

    Article  CAS  Google Scholar 

  104. Ma C et al (2013) Synthesis and electrochemical properties of artificial graphite as an anode for high-performance lithium-ion batteries. Carbon 64:553–556

    Article  CAS  Google Scholar 

  105. Rajkamal A, Sinthika S, Andersson G, Thapa R (2018) Ring type and π electron occupancy decides the Li-ion storage properties of Phagraphene: An example of sp2 hybridized carbon structure. Carbon 129:775–784

    Article  CAS  Google Scholar 

  106. Ferguson D, Searles DJ, Hankel M (2017) Biphenylene and phagraphene as lithium ion battery anode materials. ACS Appl Mater Interfaces 9(24):20577–20584

    Article  CAS  Google Scholar 

  107. Bagheri R, Babazadeh M, Vessally E, Es’haghi M, Bekhradnia A (2018) Si-doped phagraphene as a drug carrier for adrucil anti-cancer drug: DFT studies. Inorg Chem Commun 90:8–14

    Article  CAS  Google Scholar 

  108. Dresselhaus M, Dresselhaus G, Saito R (1995) Physics of carbon nanotubes. Carbon 33(7):883–891

    Article  CAS  Google Scholar 

  109. Bethune D et al (1993) Cobalt-catalysed growth of carbon nanotubes with single-atomic-layer walls. Nature 363(6430):605–607

    Article  CAS  Google Scholar 

  110. Zhang Y, Iijima S (1999) Formation of single-wall carbon nanotubes by laser ablation of fullerenes at low temperature. Appl Phys Lett 75(20):3087–3089

    Article  CAS  Google Scholar 

  111. Thompson AP et al (2022) LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun 271:108171

    Article  CAS  Google Scholar 

  112. Van Duin AC, Dasgupta S, Lorant F, Goddard WA (2001) ReaxFF: a reactive force field for hydrocarbons. J Phys Chem A 105(41):9396–9409

    Article  Google Scholar 

  113. Chenoweth K, Van Duin AC, Goddard WA (2008) ReaxFF reactive force field for molecular dynamics simulations of hydrocarbon oxidation. J Phys Chem A 112(5):1040–1053

    Article  CAS  Google Scholar 

  114. Nosé S (1984) A molecular dynamics method for simulations in the canonical ensemble. Mol Phys 52(2):255–268

    Article  Google Scholar 

  115. Nosé S (1984) A unified formulation of the constant temperature molecular dynamics methods. J Chem Phys 81(1):511–519

    Article  Google Scholar 

  116. Nosé S, Klein M (1983) Constant pressure molecular dynamics for molecular systems. Mol Phys 50(5):1055–1076

    Article  Google Scholar 

  117. Evans DJ, Morriss GP (1983) Isothermal-isobaric molecular dynamics. Chem Phys 77(1):63–66

    Article  CAS  Google Scholar 

  118. Holian BL, Evans DJ (1983) Shear viscosities away from the melting line: a comparison of equilibrium and nonequilibrium molecular dynamics. J Chem Phys 78(8):5147–5150

    Article  CAS  Google Scholar 

  119. Haile J, Gupta S (1983) Extensions of the molecular dynamics simulation method. II. Isothermal systems. J Chem Phys 79(6):3067–3076

    Article  CAS  Google Scholar 

  120. Andersen H, Hayes D, Michelson A, Todd A (1980) J. Chem Phys, 72:2384

  121. Parrinello M, Rahman A (1980) Crystal structure and pair potentials: a molecular-dynamics study. Phys Rev Lett 45(14):1196–1199

    Article  CAS  Google Scholar 

  122. Parrinello M, Rahman A (1981) Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys 52(12):7182–7190

    Article  CAS  Google Scholar 

  123. Sharma S (2019) Molecular dynamics simulation of nanocomposites using BIOVIA materials studio, lammps and gromacs. Elsevier, Amsterdam

    Google Scholar 

  124. Rapaport DC, Rapaport DCR (2004) The art of molecular dynamics simulation. Cambridge University Press, Cambridge

    Book  Google Scholar 

  125. Tersoff J (1988) New empirical approach for the structure and energy of covalent systems. Phys Rev B 37(12):6991

    Article  CAS  Google Scholar 

  126. Brenner DW (1990) Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films. Phys Rev B 42(15):9458–9471

    Article  CAS  Google Scholar 

  127. Morse PM (1929) Diatomic molecules according to the wave mechanics. II. Vibrational levels. Phys Rev 34(1):57–64

    Article  CAS  Google Scholar 

  128. Lindsay L, Broido D (2010) Optimized Tersoff and Brenner empirical potential parameters for lattice dynamics and phonon thermal transport in carbon nanotubes and graphene. Phys Rev B 81(20):205441

    Article  Google Scholar 

  129. Wan S, Sinclair RC, Coveney PV (2021) Uncertainty quantification in classical molecular dynamics. Phil Trans R Soc A 379(2197):20200082

    Article  CAS  Google Scholar 

  130. Chu L (2021) Uncertainty quantification of stochastic defects in materials. CRC Press, Boca Raton

    Book  Google Scholar 

  131. Wang H, Guilleminot J, Soize C (2019) Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis. Comput Methods Appl Mech Eng 354:37–55. https://doi.org/10.1016/j.cma.2019.05.020

    Article  Google Scholar 

  132. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. npj Comput Mater 3(1):54. https://doi.org/10.1038/s41524-017-0056-5

    Article  Google Scholar 

  133. Interatomic Potentials Repository (2010) https://www.ctcms.nist.gov/potentials/. Accessed 5 Oct 2010.

  134. Gehlen P (2012) Interatomic potentials and simulation of lattice defects. Springer, Berlin

    Google Scholar 

  135. Torrens I (2012) Interatomic potentials. Elsevier, Amsterdam

    Google Scholar 

  136. Stuart SJ, Tutein AB, Harrison JA (2000) A reactive potential for hydrocarbons with intermolecular interactions. J Chem Phys 112(14):6472–6486

    Article  CAS  Google Scholar 

  137. O’connor TC, Andzelm J, Robbins MO (2015) AIREBO-M: a reactive model for hydrocarbons at extreme pressures. J Chem Phys 142(2):024903

    Article  Google Scholar 

  138. Senftle TP et al (2016) The ReaxFF reactive force-field: development, applications and future directions. npj Computat Mater 2(1):1–14

    Google Scholar 

  139. Han Y, Jiang D, Zhang J, Li W, Gan Z, Gu J (2016) Development, applications and challenges of ReaxFF reactive force field in molecular simulations. Front Chem Sci Eng 10(1):16–38. https://doi.org/10.1007/s11705-015-1545-z

    Article  CAS  Google Scholar 

  140. Kański M, Maciazek D, Postawa Z, Ashraf CM, Van Duin AC, Garrison BJ (2018) Development of a charge-implicit ReaxFF potential for hydrocarbon systems. J Phys Chem Lett 9(2):359–363

    Article  Google Scholar 

  141. Semiromi DT, Azimian A (2011) Molecular dynamics simulation of nonodroplets with the modified Lennard-Jones potential function. Heat Mass Transf 47(5):579–588

    Article  CAS  Google Scholar 

  142. Nijmeijer M, Bakker A, Bruin C, Sikkenk J (1988) A molecular dynamics simulation of the Lennard-Jones liquid–vapor interface. J Chem Phys 89(6):3789–3792

    Article  CAS  Google Scholar 

  143. Smit B (1992) Phase diagrams of Lennard–Jones fluids. J Chem Phys 96(11):8639–8640

    Article  CAS  Google Scholar 

  144. Frenkel D, Smit B, Ratner MA (1996) Understanding molecular simulation: from algorithms to applications. Academic Press, San Diego

    Google Scholar 

  145. Allen MP, Tildesley DJ (2017) Computer simulation of liquids. Oxford University Press, Oxford

    Book  Google Scholar 

  146. Cornell WD et al (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117(19):5179–5197

    Article  CAS  Google Scholar 

  147. Becker CA, Tavazza F, Trautt ZT, de Macedo RAB (2013) Considerations for choosing and using force fields and interatomic potentials in materials science and engineering. Curr Opin Solid State Mater Sci 17(6):277–283

    Article  CAS  Google Scholar 

  148. Hale LM, Trautt ZT, Becker CA (2018) Evaluating variability with atomistic simulations: the effect of potential and calculation methodology on the modeling of lattice and elastic constants. Modell Simul Mater Sci Eng 26:055003

    Article  Google Scholar 

  149. Orio M, Pantazis DA, Neese F (2009) Density functional theory. Photosynth Res 102(2):443–453

    Article  CAS  Google Scholar 

  150. Kohn W (2019) Density functional theory. In: Introductory quantum mechanics with MATLAB: for atoms, molecules, clusters, and nanocrystals

  151. Engel E , Dreizler RM (2011) Density functional theory. In: Theoretical and mathematical physics, pp 351–399

  152. Hohenberg P, Kohn W (1964) Inhomogeneous electron gas. Phys Rev 136(3B):B864–B871. https://doi.org/10.1103/PhysRev.136.B864

    Article  Google Scholar 

  153. Maslov MM, Podlivaev AI, Katin KP (2016) Nonorthogonal tight-binding model with H–C–N–O parameterisation. Mol Simul 42(4):305–311

    Article  CAS  Google Scholar 

  154. Foulkes WMC, Haydock R (1989) Tight-binding models and density-functional theory. Phys Rev B 39(17):12520–12536

    Article  CAS  Google Scholar 

  155. Pilania G, Wang C, Jiang X, Rajasekaran S, Ramprasad R (2013) Accelerating materials property predictions using machine learning. Sci Rep 3(1):1–6

    Article  Google Scholar 

  156. Schleder GR, Padilha AC, Acosta CM, Costa M, Fazzio A (2019) From DFT to machine learning: recent approaches to materials science—A review. J Phys Mater 2(3):032001

    Article  CAS  Google Scholar 

  157. Mueller T, Hernandez A, Wang C (2020) Machine learning for interatomic potential models. J Chem Phys 152(5):050902

    Article  CAS  Google Scholar 

  158. Mishin Y (2021) Machine-learning interatomic potentials for materials science. Acta Mater 214:116980

    Article  CAS  Google Scholar 

  159. Zuo Y et al (2020) Performance and cost assessment of machine learning interatomic potentials. J Phys Chem A 124(4):731–745

    Article  CAS  Google Scholar 

  160. Behler J (2016) Perspective: machine learning potentials for atomistic simulations. J Chem Phys 145(17):170901

    Article  Google Scholar 

  161. Schmidt J, Marques MR, Botti S, Marques MA (2019) Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater 5(1):83

    Article  Google Scholar 

  162. Bartók AP, Kermode J, Bernstein N, Csányi G (2018) Machine learning a general-purpose interatomic potential for silicon. Phys Rev X 8(4):041048

    Google Scholar 

  163. Friederich P, Häse F, Proppe J, Aspuru-Guzik A (2021) Machine-learned potentials for next-generation matter simulations. Nat Mater 20(6):750–761

    Article  CAS  Google Scholar 

  164. Ko TW, Finkler JA, Goedecker S, Behler JR (2021) General-purpose machine learning potentials capturing nonlocal charge transfer. Accounts Chem Res 54(4):808–817

    Article  CAS  Google Scholar 

  165. Ghosh D et al (2008) Extremely high thermal conductivity of graphene: Prospects for thermal management applications in nanoelectronic circuits. Appl Phys Lett 92(15):151911

    Article  Google Scholar 

  166. Jauregui LA et al (2010) Thermal transport in graphene nanostructures: Experiments and simulations. ECS Trans 28(5):73–83

    Article  CAS  Google Scholar 

  167. Thomas JA, Iutzi RM, McGaughey AJ (2010) Thermal conductivity and phonon transport in empty and water-filled carbon nanotubes. Phys Rev B 81(4):045413

    Article  Google Scholar 

  168. Mortazavi B, Rabczuk T (2015) Multiscale modeling of heat conduction in graphene laminates. Carbon 85:1–7

    Article  CAS  Google Scholar 

  169. Lindsay L, Broido D, Mingo N (2010) Flexural phonons and thermal transport in graphene. Phys Rev B 82(11):115427

    Article  Google Scholar 

  170. Arabha S, Rajabpour A (2021) Thermo-mechanical properties of nitrogenated holey graphene (C2N): a comparison of machine-learning-based and classical interatomic potentials. Int J Heat Mass Transf 178:121589

    Article  CAS  Google Scholar 

  171. Behler J, Parrinello M (2007) Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett 98(14):146401

    Article  Google Scholar 

  172. Zhang L, Han J, Wang H, Car R, Weinan E (2018) Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys Rev Lett 120(14):143001

    Article  CAS  Google Scholar 

  173. Lubbers N, Smith JS, Barros K (2018) Hierarchical modeling of molecular energies using a deep neural network. J Chem Phys 148(24):241715

    Article  Google Scholar 

  174. Pun GP, Batra R, Ramprasad R, Mishin Y (2019) Physically informed artificial neural networks for atomistic modeling of materials. Nat Commun 10(1):2339

    Article  Google Scholar 

  175. Smith JS, Isayev O, Roitberg AE (2017) ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem Sci 8(4):3192–3203

    Article  CAS  Google Scholar 

  176. Bartók AP, Payne MC, Kondor R, Csányi G (2010) Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett 104(13):136403

    Article  Google Scholar 

  177. Deringer VL, Csányi G (2017) Machine learning based interatomic potential for amorphous carbon. Phys Rev B 95(9):094203

    Article  Google Scholar 

  178. Grisafi A, Wilkins DM, Csányi G, Ceriotti M (2018) Symmetry-adapted machine learning for tensorial properties of atomistic systems. Phys Rev Lett 120(3):036002

    Article  CAS  Google Scholar 

  179. Jinnouchi R, Karsai F, Kresse G (2019) On-the-fly machine learning force field generation: application to melting points. Phys Rev B 100(1):014105

    Article  CAS  Google Scholar 

  180. Szlachta WJ, Bartók AP, Csányi G (2014) Accuracy and transferability of Gaussian approximation potential models for tungsten. Phys Rev B 90(10):104108

    Article  Google Scholar 

  181. Wang H, Zhang L, Han J, Weinan E (2018) DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput Phys Commun 228:178–184

    Article  CAS  Google Scholar 

  182. "Deep Modeling" https://deepmodeling.com/. Accessed 20 April 2023

  183. Seko A, Togo A, Tanaka I (2019) Group-theoretical high-order rotational invariants for structural representations: application to linearized machine learning interatomic potential. Phys Rev B 99(21):214108

    Article  CAS  Google Scholar 

  184. MedeA Software (2023) https://www.materialsdesign.com/medea-software . Accessed 21 April 2023

  185. Mises RV (1913) Mechanik der festen Körper im plastisch-deformablen Zustand. In: Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, vol 1913, pp 582–592, (1913)

  186. De Sousa J et al (2019) Elastic properties of graphyne-based nanotubes. Comput Mater Sci 170:109153

    Article  Google Scholar 

  187. Zhang X et al (2014) Thermal conductivity of silicene calculated using an optimized Stillinger-Weber potential. Phys Rev B 89(5):054310

    Article  Google Scholar 

  188. De Sousa J, Botari T, Perim E, Bizao R, Galvao DS (2016) Mechanical and structural properties of graphene-like carbon nitride sheets. RSC Adv 6(80):76915–76921

    Article  Google Scholar 

  189. De Sousa J, Aguiar A, Girao E, Fonseca AF, Galvao DS (2017) Mechanical properties and fracture patterns of pentagraphene membranes. http://arxiv.org/abs/1703.03789

  190. Wei X, Fragneaud B, Marianetti CA, Kysar JW (2009) Nonlinear elastic behavior of graphene: Ab initio calculations to continuum description. Phys Rev B 80(20):205407

    Article  Google Scholar 

  191. Kokalj A (2003) Computer graphics and graphical user interfaces as tools in simulations of matter at the atomic scale. Comput Mater Sci 28(2):155–168

    Article  CAS  Google Scholar 

  192. Stone AJ, Wales DJ (1986) Theoretical studies of icosahedral C60 and some related species. Chem Phys Lett 128(5–6):501–503

    Article  CAS  Google Scholar 

  193. Galashev AEE, Rakhmanova OR (2014) Mechanical and thermal stability of graphene and graphene-based materials. Phys Usp 57(10):970

    Article  CAS  Google Scholar 

  194. Jiang J-W, Park HS (2016) Negative Poisson’s ratio in single-layer graphene ribbons. Nano Lett 16(4):2657–2662

    Article  CAS  Google Scholar 

  195. Brazhe R, Kochaev A, Nefedov V (2012) Young’s modulus and the Poisson’s ratio of planar and nanotubular supracrystalline structures. Phys Solid State 54(7):1430–1432

    Article  CAS  Google Scholar 

  196. Hoover WG, Ladd AJ, Moran B (1982) High-strain-rate plastic flow studied via nonequilibrium molecular dynamics. Phys Rev Lett 48(26):1818–1820

    Article  CAS  Google Scholar 

  197. Brandl C, Derlet PM, Van Swygenhoven H (2009) Strain rates in molecular dynamics simulations of nanocrystalline metals. Phil Mag 89(34–36):3465–3475

    Article  CAS  Google Scholar 

  198. Wagner NJ, Holian BL, Voter AF (1992) Molecular-dynamics simulations of two-dimensional materials at high strain rates. Phys Rev A 45(12):8457

    Article  CAS  Google Scholar 

  199. Wen Y-H, Zhu Z-Z, Zhu R-Z (2008) Molecular dynamics study of the mechanical behavior of nickel nanowire: strain rate effects. Comput Mater Sci 41(4):553–560

    Article  CAS  Google Scholar 

  200. Yang S, Chen Y, Jiang C (2021) Strain engineering of two-dimensional materials: methods, properties, and applications. InfoMat 3(4):397–420

    Article  Google Scholar 

  201. Müller P (2011) Fundamentals of stress and strain at the nanoscale level: towards nanoelasticity, ed

  202. Gianola DS, Eberl C (2009) Micro-and nanoscale tensile testing of materials. Jom 61:24–35

    Article  Google Scholar 

  203. Talebi H, Silani M, Bordas SP, Kerfriden P, Rabczuk T (2014) A computational library for multiscale modeling of material failure. Comput Mech 53:1047–1071

    Article  Google Scholar 

  204. Budarapu PR, Gracie R, Yang S-W, Zhuang X, Rabczuk T (2014) Efficient coarse graining in multiscale modeling of fracture. Theoret Appl Fract Mech 69:126–143

    Article  Google Scholar 

  205. Ju J, Lee H-K (2001) A micromechanical damage model for effective elastoplastic behavior of partially debonded ductile matrix composites. Int J Solids Struct 38(36–37):6307–6332

    Article  Google Scholar 

  206. Christman T, Needleman A, Suresh S (1989) An experimental and numerical study of deformation in metal-ceramic composites. Acta Metall 37(11):3029–3050

    Article  CAS  Google Scholar 

  207. Gumbsch P, Zhou S, Holian B (1997) Molecular dynamics investigation of dynamic crack stability. Phys Rev B 55(6):3445

    Article  CAS  Google Scholar 

  208. Komanduri R, Raff L (2001) A review on the molecular dynamics simulation of machining at the atomic scale. Proc Inst Mech Eng Part B J Eng Manuf 215(12):1639–1672

    Article  Google Scholar 

  209. Abraham FF, Brodbeck D, Rudge WE, Xu X (1997) A molecular dynamics investigation of rapid fracture mechanics. J Mech Phys Solids 45(9):1595–1619

    Article  CAS  Google Scholar 

  210. Fish J, Yuan Z (2005) Multiscale enrichment based on partition of unity. Int J Numer Meth Eng 62(10):1341–1359

    Article  Google Scholar 

  211. Horstemeyer MF (2010) Multiscale modeling: a review. In: Practical aspects of computational chemistry: methods, concepts and applications, pp 87–135

  212. Dascalu C, Bilbie G, Agiasofitou E (2008) Damage and size effects in elastic solids: a homogenization approach. Int J Solids Struct 45(2):409–430

    Article  Google Scholar 

  213. Li X, Zhang S, Wang FQ, Guo Y, Liu J, Wang Q (2016) Tuning the electronic and mechanical properties of penta-graphene via hydrogenation and fluorination. Phys Chem Chem Phys 18(21):14191–14197

    Article  CAS  Google Scholar 

  214. Tison Y et al (2014) Grain boundaries in graphene on SiC (0001) substrate. Nano Lett 14(11):6382–6386

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors of this review article show their gratitude towards the institute and are greatly thankful to the Department of Mechanical Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, for allowing the resources to carry out this particular research.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Dr. BR Ambedkar National Institute of Technology, Jalandhar, 144027, India

    Aditya Sharma & Sumit Sharma

  2. Department of Mechanical Engineering, University of Maragheh, P.O. Box: 55136-553, Maragheh, East Azerbaijan Province, Iran

    Shahram Ajori

Authors
  1. Aditya Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sumit Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahram Ajori
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The administration of this article and concept development is credited to Dr. Sumit Sharma who put forward the idea of researching this allotrope and developed the research scope. Dr. Shahram Ajori took the responsibility of reviewing this article thoroughly at the pre-publishing stage and many corrections were made due to his contributions. The investigation and data collection along with writing the original draft of the article were carried out by Aditya Sharma.

Corresponding author

Correspondence to Sumit Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

Not Applicable.

Additional information

Handling Editor: Ghanshyam Pilania.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A., Sharma, S. & Ajori, S. Molecular dynamics simulation of the mechanical and thermal properties of phagraphene nanosheets and nanotubes: a review. J Mater Sci 58, 10222–10260 (2023). https://doi.org/10.1007/s10853-023-08672-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10853-023-08672-4

Search

Navigation