Atomistic Simulations of Wetting Dynamics of Water Nanodroplets on Nanotextured Titanium: Implications for Medical Implants
Source abstract
Abstract
The development of high-performance biomedical implants requires a deep understanding of the molecular interactions between water molecules and titanium (Ti) surfaces. In this study, fully atomistic molecular dynamics simulations were used to study the static and dynamic wetting behavior of water nanodroplets on both flat and femtosecond laser-induced nanotextured Ti surfaces. Our findings reveal a clear transition from Wenzel to Cassie–Baxter wetting states as surface roughness increases, significantly affecting droplet spreading. We also observe the damping of nanodroplet vibrations and a roughness-dependent shift toward hydrophobicity, driven by stronger atomic interactions between water molecules and surface atoms. Furthermore, the interaction energy between water droplets and nanotextured Ti surfaces decreases with increasing roughness, reinforcing the observed changes in wettability. The discrepancies observed between classical wetting models and nanoscale behavior emphasize the limitations of current theoretical approaches and the importance of developing more advanced models. This study provides valuable insights into optimizing Ti surface properties for improved implant performance through controlled wettability.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Diamond and Carbon-based Materials Research · Surface Modification and Superhydrophobicity · nanoparticles nucleation surface interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien
Université Jean Monnet
LEM3, CNRS─Université de Lorraine─Arts et Métiers ParisTech, 7 rue Félix Savart, 57070 Metz, France
Mines Saint-Étienne, Univ. Lyon, CNRS, UMR 5307 LGF, Centre SMS
Funding
H2020 Future and Emerging Technologies · Minist?re de l'?ducation Nationale · EUR Manutech Sleight · Labex Manutech-SISE
References (67)
Show source-provided references
1. 10.1007/s11837-008-0031-110.1007/s11837-008-0031-1 ↗
2. 10.3390/jcm1008164110.3390/jcm10081641 ↗
3. 10.1038/s41598-018-28454-210.1038/s41598-018-28454-2 ↗
4. 10.1002/lpor.20120001710.1002/lpor.201200017 ↗
5. 10.1002/admi.20187003310.1002/admi.201870033 ↗
6. 10.1038/lsa.2014.3010.1038/lsa.2014.30 ↗
7. 10.1016/j.actbio.2014.03.03210.1016/j.actbio.2014.03.032 ↗
8. 10.1146/annurev.matsci.38.060407.13243410.1146/annurev.matsci.38.060407.132434 ↗
9. 10.1016/j.apsusc.2009.07.06110.1016/j.apsusc.2009.07.061 ↗
10. 10.1016/j.apsusc.2012.11.08510.1016/j.apsusc.2012.11.085 ↗
11. 10.1016/j.msec.2016.06.07210.1016/j.msec.2016.06.072 ↗
12. 10.1016/j.ijleo.2020.16544310.1016/j.ijleo.2020.165443 ↗
13. 10.1021/ie50320a02410.1021/ie50320a024 ↗
14. 10.1039/tf944400054610.1039/tf9444000546 ↗
15. 10.1016/j.apsusc.2010.12.08910.1016/j.apsusc.2010.12.089 ↗
16. 10.1039/C3TA14711H10.1039/C3TA14711H ↗
17. 10.1016/j.apsusc.2019.07.12610.1016/j.apsusc.2019.07.126 ↗
18. 10.1016/j.apsusc.2015.10.10210.1016/j.apsusc.2015.10.102 ↗
19. 10.1002/jbm.a.3423910.1002/jbm.a.34239 ↗
20. 10.1016/j.matpr.2020.12.43310.1016/j.matpr.2020.12.433 ↗
21. 10.3390/coatings1102012410.3390/coatings11020124 ↗
22. 10.1007/s00170-013-5277-210.1007/s00170-013-5277-2 ↗
23. 10.1080/0892702080239888410.1080/08927020802398884 ↗
24. 10.1021/jp811257x10.1021/jp811257x ↗
25. 10.1016/j.jmgm.2022.10833510.1016/j.jmgm.2022.108335 ↗
26. 10.1021/j100308a03810.1021/j100308a038 ↗
27. 10.1016/j.commatsci.2019.10922310.1016/j.commatsci.2019.109223 ↗
28. 10.1016/0021-9991(77)90098-510.1016/0021-9991(77)90098-5 ↗
29. Hockney R. W. · 202110.1201/9780367806934 ↗
30. 10.1063/1.46439710.1063/1.464397 ↗
31. 10.1080/08927022.2012.75885410.1080/08927022.2012.758854 ↗
32. Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids Clarendon Press, 1987.
33. Allen M. P. · 201710.1093/oso/9780198803195.001.0001 ↗
34. 10.1016/S0167-7322(02)00094-610.1016/S0167-7322(02)00094-6 ↗
35. 10.1063/1.167640310.1063/1.1676403 ↗
36. 10.1021/jp034093t10.1021/jp034093t ↗
37. 10.1021/ja00051a04010.1021/ja00051a040 ↗
38. 10.1016/j.jpcs.2020.10986510.1016/j.jpcs.2020.109865 ↗
39. 10.1103/PhysRevB.70.17411310.1103/PhysRevB.70.174113 ↗
40. 10.1016/j.actamat.2017.04.05110.1016/j.actamat.2017.04.051 ↗
41. 10.1103/PhysRev.135.A48210.1103/PhysRev.135.A482 ↗
42. 10.1021/la304763a10.1021/la304763a ↗
43. Omeje, I. S.; Prada-Rodrigo, J.; Gamet, E.; Di Maio, Y.; Guillemet, R.; Itina, T.; Sedao, X. Prediction of Wetting Behaviours of Femtosecond Laser Texturized Surfaces J. Laser Micro/Nanoeng. 2024.
44. 10.1016/j.ijheatmasstransfer.2014.08.04710.1016/j.ijheatmasstransfer.2014.08.047 ↗
45. 10.1016/j.cplett.2021.13916110.1016/j.cplett.2021.139161 ↗
46. 10.1016/j.apsadv.2022.10025010.1016/j.apsadv.2022.100250 ↗
47. Frenkel D. · 2023
48. 10.1039/C8CP03762K10.1039/C8CP03762K ↗
49. 10.1073/pnas.090202710610.1073/pnas.0902027106 ↗
50. 10.1006/jcph.1995.103910.1006/jcph.1995.1039 ↗
51. 10.1088/0965-0393/18/1/01501210.1088/0965-0393/18/1/015012 ↗
52. 10.29026/oes.2022.22000510.29026/oes.2022.220005 ↗
53. 10.1038/s41598-017-08788-z10.1038/s41598-017-08788-z ↗
54. 10.1063/1.337661210.1063/1.3376612 ↗
55. 10.1016/j.jcis.2009.07.04810.1016/j.jcis.2009.07.048 ↗
56. 10.1021/acs.langmuir.8b0048010.1021/acs.langmuir.8b00480 ↗
57. 10.1088/0953-8984/24/46/46412110.1088/0953-8984/24/46/464121 ↗
58. 10.3390/e1509373410.3390/e15093734 ↗
59. 10.1016/j.geoderma.2011.02.00410.1016/j.geoderma.2011.02.004 ↗
60. 10.1038/nmeth.208910.1038/nmeth.2089 ↗
61. 10.2147/IJN.S31187210.2147/IJN.S311872 ↗
62. 10.1038/s41598-018-24590-x10.1038/s41598-018-24590-x ↗
63. 10.1016/j.ceramint.2014.04.11610.1016/j.ceramint.2014.04.116 ↗
64. 10.5185/amlett.2016.601410.5185/amlett.2016.6014 ↗
65. Alen, S. K.; Farhat, N.; Rahman, M. A. In AIP Conference Proceedings; AIP Publishing, 2016.
66. 10.1016/j.jcis.2008.06.00410.1016/j.jcis.2008.06.004 ↗
67. 10.1021/acs.langmuir.6b0237810.1021/acs.langmuir.6b02378 ↗