Design of Ti composite with bioactive surface for dental implant

References

• Chen, Q.; Thouas, G. Biomaterials: A Basic Introduction; CRC Press: Boca Raton, FL, 2018.
   Google Scholar
• Mohammed, M. T.; Khan, Z. A.; Siddiquee, A. N. Beta Titanium Alloys: The Lowest Elastic Modulus for Biomedical Applications: A Review. Int. J. Chem. Nucl. Metall. Mater. Eng. 2014, 8, 822–827.
   Google Scholar
• Polmear, I. J.; StJihn, D.; Nie, J.-F.; Qian, M. Light Alloys: Metallurgy of the Light Metals; Butterworth-Heinemann: Oxford, UK, 2017.
   Google Scholar
• Lee, J. G. Computational Materials Science: An Introduction; CRC Press: Boca Raton, FL, 2011.
   Google Scholar
• Datta, S. Material Design Using Computational Intelligence Techniques; CRC Press: Boca Raton, FL, 2017.
   Google Scholar
• Mucalo, M. Hydroxyapatite (Hap) for Biomedical Applications; Woodhead Publishing: Cambridge, England, 2015.
   Google Scholar
• Sridhar:, T. M. Nanobioceramic Coatings for Biomedical Applications. Mater. Technol. 2010, 25, 184–195. DOI: 10.1179/175355510X12723642365449.
   Web of Science ®Google Scholar
• Manonmani, R.; Vinodhini, S. P.; Venkatachalapathy, B.; Sridhar, T. M. Electrochemical, Mechanical and Osseointegration Evaluation of NBPC-coated 316L SS by EPD. Surface Eng. 2018, 34, 511–519.
   Google Scholar
• Jansen, J. A.; van de Waerden, J. P. C. M.; Wolke, J. G. C.; de Groot, K. Histologic Evaluation of the Osseous Adaptation to Titanium and Hydroxyapatite-coated Titanium Implants. J. Biomed. Mater. Res. 1991, 25, 973–989. DOI: 10.1002/jbm.820250805.
   PubMed Web of Science ®Google Scholar
• Zhao, B.; van der Mei, H. C.; Rusteman-Abbing, M.; Busscher, H. J.; Ren, Y. Osteoblast Integration of Dental Implant Materials after Challenge by Sub-gingival Pathogens: A Co-culture Study in Vitro. Int. J. Oral Sci. 2015, 7, 250–258. DOI: 10.1038/ijos.2015.45.
   PubMed Web of Science ®Google Scholar
• Han, C.; Wang, Q.; Song, B.; Wei, L.; Wei, Q.; Wen, S.; Liu, J.; Shi, Y. Microstructure and Property Evolutions of Titanium/nano-hydroxyapatite Composites In-situ Prepared by Selective Laser Melting. J. Mech. Behav. Biomed. Mater. 2017, 71, 85–94. DOI: 10.1016/j.jmbbm.2017.02.021.
   PubMed Web of Science ®Google Scholar
• Datta, S.; Chattopadhyay, P. P. Soft Computing Techniques in Advancement of Structural Metals. Int. Mater. Rev. 2013, 58, 475–504. DOI: 10.1179/1743280413Y.0000000021.
   Web of Science ®Google Scholar
• Ghosal, S.; Dey, S.; Bhattacharyya, P. Partha Pratim Chattopadhyay and Shubhabrata Datta: Data-driven Design of Ternary Alloy Catalysts for Enhanced Oxide-based Gas Sensors’ Performance. Comput. Mater. Sci. 2019, 161, 255–260. DOI: 10.1016/j.commatsci.2019.01.053.
   Web of Science ®Google Scholar
• Mohanty, I.; Datta, S.; Bhattacharjee, D. Composition-processing-property Correlation of Cold Rolled IF Steel Sheets Using Neural Network. Mater. Manuf. Processes. 2009, 24(1), 100–105. DOI: 10.1080/10426910802543947.
   Web of Science ®Google Scholar
• Datta, S.; Sultana, N.; Zhang, Q.; Mahfouf, M. Optimal Design of Titanium Alloys for Prosthetic Applications Using a Multi-objective Evolutionary Algorithm. Mater. Manuf. Processes. 2013, 28, 741–745. DOI: 10.1080/10426914.2013.773020.
   Web of Science ®Google Scholar
• Chawla, K. K. Composite Materials; Springer-Verlag: New York, US, 2012.
   Google Scholar
• Datta, S.; Mahfouf, M.; Zhang, Q.; Chattopadhyay, P. P.; Sultana, N. Imprecise Knowledge Based Design and Development of Titanium Alloys for Prosthetic Applications. J. Mech. Behav. Biomed. Mater. 2016, 53, 350–365. DOI: 10.1016/j.jmbbm.2015.08.039.
   PubMed Web of Science ®Google Scholar
• Hassoun, M. H. Fundamentals of Artificial Neural Networks; MIT Press: Cambridge, MA, 2003.
   Google Scholar
• Takakuwa, O.; Kawaragi, Y.; Soyama, H. Estimation of the Yield Stress of Stainless Steel from the Vickers Hardness Taking Account of the Residual Stress. J. Surf. Eng. Mater. Adv. Technol. 2013, 3, 262–268. DOI: 10.4236/jsemat.2013.34035.
   Google Scholar
• Arifin, A.; Sulong, A. B.; Muhamad, N.; Syarif, J.; Ramli, M. I. Material Processing of Hydroxyapatite and Titanium Alloy (Ha/Ti) Composite as Implant Materials Using Powder Metallurgy: A Review. Mater. Des. 2014, 55, 165–175. DOI: 10.1016/j.matdes.2013.09.045.
   Web of Science ®Google Scholar
• Vladescu, A.; Padmanabhan, S. C.; Ak Azem, F.; Braic, M.; Titorencu, I.; Birlik, I.; Morris, M. A.; Braic, V. Mechanical Properties and Biocompatibility of the Sputtered Ti Doped Hydroxyapatite. J. Mech. Behav. Biomed. Mater. 2016, 63, 314–325. DOI: 10.1016/j.jmbbm.2016.06.025.
   PubMed Web of Science ®Google Scholar
• Niinomi, M.;. Recent Research and Development in Titanium Alloys for Biomedical Applications and Healthcare Goods. Sci. Technol. Adv. Mater. 2003, 4, 445–454. DOI: 10.1016/j.stam.2003.09.002.
   Web of Science ®Google Scholar
• Goldberg, D. E. Genetic Algorithm in Search, Optimization and Machine Learning; Pearson Education: Bengaluru, India, 2008.
   Google Scholar
• Deb, K. Multiobjective Optimization Using Evolutionary Algorithm; John Wiley & Sons: New York, NY, 2001.
   Google Scholar
• Deb, K.; Agrawal, S.; Pratap, A.; Meyarivan, T. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science; Schoenauer, M., Ed.; Springer: Berlin, Heidelberg, 1917; 849–858.
   Google Scholar
• Sarkar, K.; Sadhukhan, S.; Han, S. S.; Vyas, Y. M. Disruption of hSWI/SNF-Complexes in T Cells by WAS Mutations Distinguishes X-linked Thrombocytopenia from Wiskott-Aldrich Syndrome. Blood. 2014, 124, 3409–3419. DOI: 10.1182/blood-2014-07-587642.
   PubMed Web of Science ®Google Scholar
• Sarkar, K.; Sadhukhan, S.; Han, S. S.; Vyas, Y. M. SUMOylation-disrupting WAS Mutation Converts WASp from a Transcriptional Activator to a Repressor of NF-κB Response Genes in T Cells. Blood. 2015, 126, 1670–1682. DOI: 10.1182/blood-2015-05-646182.
   PubMed Web of Science ®Google Scholar
• Olden, J. D.; Joy, M. K.; Russell:, G. An Accurate Comparison of Methods for Quantifying Variable Importance in Artificial Neural Networks Using Simulated Data. Ecol. Modell. 2004, 178, 389–397. DOI: 10.1016/j.ecolmodel.2004.03.013.
   Web of Science ®Google Scholar