A molecular dynamics study of water confined in between two graphene sheets under compression
Source abstract
Abstract
Abstract Several studies have demonstrated interest in creating surfaces with improved water interaction and adaptive properties because the behavior of water confined at the nanoscale plays a significant role in the synthesis of materials for technological applications. Remarkably, confinement at the nanoscale significantly modifies the characteristics of water. We determine the phase diagram of water contained by graphene stack sheets in slab form, at $$T = 300$$ K, and for a constant pressure using molecular dynamics simulations. We discover that, as shown in the simulation, water can exist in both the liquid and vapor phases depending on the confining geometry and compressibility ratio. We also pay attention to how stable the interacting liquid is in relation to the pressure of compression that is perpendicular to the graphene sheets. To build this system and analyze its surface interface properties, we also used analytical and electronic scale modeling approaches. The impact of nanoconfinement on internal pressure may be seen in water, and this can be used to create interfacial materials for the creation of environmentally friendly solar cell materials. Our research highlights the intricate, seemingly random behavior of nanoconfined water—behavior that is difficult for graphene to understand. The results obtained offer crucial direction for system design and configuration of materials at the graphene/water interface that can be utilized as a benchmark for other future designs.
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Research context
OpenAlex topics: Graphene research and applications · Nanopore and Nanochannel Transport Studies · Solar-Powered Water Purification Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (23)
Show source-provided references
1. Stankovich S, Dmitriy DA (2007) Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide. Carbon 45(7):1558–1565. https://doi.org/10.1016/j.carbon.2007.02.03410.1016/j.carbon.2007.02.034 ↗
2. Balandin A, Ghosh A (2008) Superior thermal conductivity of single-layer graphene. Nano Lett 8(90):2–7. https://doi.org/10.1021/nl073187210.1021/nl0731872 ↗
3. Akaishi A, Yonemaru T, Nakamura J (2017) Formation of water layers on graphene surfaces. ACS Omega 2(5):2184–2190. https://doi.org/10.1021/acsomega.7b0036510.1021/acsomega.7b00365 ↗
4. Tomita H, Nakamura J (2013) Ballistic phonon thermal conductance in graphene nanoribbons. Journal of Vacuum Science Technology: Microelectronics and Nanometre Structures 31. J Vac Sci Technol B 31(04D104). https://doi.org/10.1116/1.480461710.1116/1.4804617 ↗
5. Kozbial A, Li Z, Jianing S (2014) Understanding the intrinsic water wettability of graphite. Carbon 74:218–225. https://doi.org/10.1016/j.carbon.2014.03.02510.1016/j.carbon.2014.03.025 ↗
6. Munz M, Cristina EG (2015) Thickness-dependent hydrophobicity of epitaxial graphene. ACS Nano 9(8):8401–8411. https://doi.org/10.1021/acsnano.5b0322010.1021/acsnano.5b03220 ↗
7. Shih C, Hua Wang Q, Lin S (2012) The wetting transparency of graphene. Breakdown in Phys Rev Lett 109:176101. https://doi.org/10.1103/PhysRevLett.109.17610110.1103/PhysRevLett.109.176101 ↗
8. Mi X, Gullapalli H, Thomas AV (2012) J. Wetting transparency of graphene. Nature materials 3(11):217–222. https://doi.org/10.1038/nmat322810.1038/nmat3228 ↗
9. Snaith H (2013) Perovskites: the emergence of a new era for low-cost, high-efficiency solar cells. J Phys Chem Lett 4:3623–3630. https://doi.org/10.1021/jz402016210.1021/jz4020162 ↗
10. Lee M , Teuscher J , Miyasaka T , Murakami T (2012) Efficient hybrid solar cells based on meso-super structured organometal halide perovskites. Science 338(6107):643–647. https://iopscience.iop.org/article/10.1088/2053-1583/aa9ea9/meta10.1126/science.1228604 ↗
11. Kojima A, Tashima K, Shirai Y, Miyasaka T (2009) Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. J Am Chem Soc 131(17):6050–605110.1021/ja809598r ↗
12. Konagai M (2011) Present status and future prospects of silicon thin-film solar cells. Jpn J Appl Phys 50:030001. https://iopscience.iop.org/article/; https://doi.org/10.1143/JJAP.50.03000110.1143/JJAP.50.030001 ↗
13. Wu HA, Jayaram PN, Nair RR (2012) Unimpeded permeation of water through helium-leak-tight graphene-based membranes. Science (New York, N.Y.) 335(6067):442–444. https://doi.org/10.1126/science.121169410.1126/science.1211694 ↗
14. Abraham J, Kalangi SV (2017) Graphene oxide membranes Tuneable sieving of ions using. Nat Nanotechnol 12(6):546–550. https://doi.org/10.1038/nnano.2017.2110.1038/nnano.2017.21 ↗
15. Morozov S, Novoselov K, Mikhail K (2008) Giant intrinsic carrier mobilities in graphene and its bilayer. Phys Rev Lett 100:016602. https://doi.org/10.1103/100.01660210.1103/100.016602 ↗
16. Lin YM, Dimitrakopoulos C (2010) 100-ghz transistors from wafer-scale epitaxial graphene. Science 327(5966):662–662. https://doi.org/10.1126/science.118428910.1126/science.1184289 ↗
17. Li X, Cai W (2009) Large-area synthesis of high-quality and uniform graphene films on copper foils. Science 324:1312–1314. https://doi.org/10.1126/science.117124510.1126/science.1171245 ↗
18. Melios C, Cristina G (2018) Water on graphene: review of recent progress. 2D Materials 5(12):022001. https://doi.org/10.1088/2053-1583/aa9ea910.1088/2053-1583/aa9ea9 ↗
19. Thompson AP, Aktulga HM, Berger R, Bolintineanu DS (2022) Lammps a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys Comm 271:108171. https://doi.org/10.1016/j.cpc.2021.10817110.1016/j.cpc.2021.108171 ↗
20. Moldovan D , Andelković M , Peeters F (2020) pybinding v0.9.5: a Python package for tight-binding calculations. https://doi.org/10.5281/zenodo.4010216 ; https://zenodo.org/record/4010216#.ZAdJanbP1PY10.5281/zenodo.4010216 ↗
21. Ranathunga DTS , Shamir A, Dai X , Nielsen SO (2020) Molecular dynamics simulations of water condensation on surfaces with tunable wettability. ACS Publications 36(26):7383–7391. https://doi.org/10.1021/acs.langmuir.0c0091510.1021/acs.langmuir.0c00915 ↗
22. Pingpong S, Davidson Richards W (2013) Python materials genomics (pymatgen):a robust, open-source Python library for materials analysis. Comput Mater Sci 68:314–319. https://doi.org/10.1016/j.commatsci.2012.10.02810.1016/j.commatsci.2012.10.028 ↗
23. Stukowski A (2010) Ovito simulation. Modelling Simul. Mater Sci Eng (18):015012. https://www.ovito.org/.10.1088/0965-0393/18/1/015012 ↗