Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration

• Autorzy: Sasiada M. , Fraczek-Szczypta A. , Tadeusiewicz R.

Identyfikatory

DOI

10.1515/bams-2016-0025

• Języki publikacji: EN

Abstrakty

EN

A new method of predicting the properties of carbon nanomaterials from carbon nanotubes and graphene oxide, using electrophoretic deposition (EPD) on a metal surface, was investigated. The main goal is to obtain the basis for nervous tissue stimulation and regeneration. Because of the many variations of the EPD method, costly and time-consuming experiments are necessary for optimization of the produced systems. To limit such costs and workload, we propose a neural network-based model that can predict the properties of selected carbon nanomaterial systems before they are produced. The choice of neural networks as predictive learning models is based on many studies in the literature that report neural models as good interpretations of real-life processes. The use of a neural network model can reduce experimentation with unpromising methods of systems processing and preparation. Instead, it allows a focus on experiments with these systems, which are promising according to the prediction given by the neural model. The performed tests showed that the proposed method of predictive learning of carbon nanomaterial properties is easy and effective. The experiments showed that the prediction results were consistent with those obtained in the real system.

Słowa kluczowe

EN

artificial neural networks; carbon nanotube; graphene; nanomaterials; nervous tissue regeneration; stimulation

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; De Gruyter
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2017
• Tom: Vol. 13, no. 1
• Strony: 25--35
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Sasiada M.

• Department of Control and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Fraczek-Szczypta A.

• Department of Biomaterials, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland

autor

Tadeusiewicz R.

• Department of Control and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland

Bibliografia

• 1. Ruoff RS, Qian D, Liu WK. Mechanical properties of carbon nanotubes: theoretical predictions and experimental measurements. C R Phys 2003;4:993–1008.
• 2. Bandaru PR. Electrical properties and applications of carbon nanotube structures. J Nanosci Nanotechnol 2007;7:1–29.
• 3. Yang DJ, Zhang Q, Chen G, Yoon SF, Ahn J, Wang SG, et al. Thermal conductivity of multiwalled carbon nanotubes. Phys Rev B 2002;66:165440.
• 4. Pokharel P, Lee SH, Lee DS. Thermal, mechanical, and electrical properties of graphene nanoplatelet/graphene oxide/polyurethane hybrid nanocomposite. J Nanosci Nanotechnol 2015;15:211–4.
• 5. Lu Y, Liu J, Hou G, Ma J, Wang W, Wei F, et al. From nano to giant? Designing carbon nanotubes for rubber reinforcement and their applications for high performance tires. Compos Sci Technol 2016;137:94–101.
• 6. Han ZJ, Rider AE, Fisher C, van der Laan T, Kumar S, Levchenko I, et al. Chapter 12 – Biological application of carbon nanotubes and graphene. In: Tanaka K, Iijima S, editors. Carbon nanotubes and graphene (second edition). Waltham, MA: Elsevier, 2014:279–312.
• 7. Syama S, Mohanan PV. Safety and biocompatibility of graphene: a new generation nanomaterial for biomedical application. Int J Biol Macromol 2016;86:546–55.
• 8. Zhang B, Wang Y, Zhai G. Biomedical applications of the graphene-based materials. Mater Sci Eng C 2016;61:953–64.
• 9. Fakhrabadi MM, Rastgoo A, Ahmadian MT. Application of electrostatically actuated carbon nanotubes in nanofluidic and bio-nanofluidic sensors and actuators. Measurement 2015;73:127–36.
• 10. Oyefusi A, Olanipekun O, Neelgund GM, Peterson D, Stone JM, Williams E, et al. Hydroxyapatite grafted carbon nanotubes and graphene nanosheets: promising bone implant materials. Spectrochim Acta Pt A Mol Biomol Spectrosc 2014;132:410–6.
• 11. Bhatt A, Jain A, Gurnany E, Jain R, Modi A, Jain A. 17 – Carbon nanotubes: a promising carrier for drug delivery and targeting. In: Nanoarchitectonics for smart delivery and drug targeting. Amsterdam, The Netherlands: Elsevier, 2016:465–501.
• 12. Ahn H-S, Hwang J-Y, Kim MS, Lee J-Y, Kim J-W, Kim H-S, et al. Carbon-nanotube-interfaced glass fiber scaffold for regeneration of transected sciatic nerve. Acta Biomater 2015;13:324–34.
• 13. Lee W, Parpura V. Chapter 6 – Carbon nanotubes as substrates/scaffolds for neural cell growth. Prog Brain Res 2009;180:110–25.
• 14. Lee J, Kim J, Kim S, Min D-H. Biosensors based on graphene oxide and its biomedical application. Adv Drug Deliv Rev 2016;105:275–87.
• 15. Zhang K, Zheng H, Liang S, Gao C. Aligned PLLA nanofibrous scaffolds coated with graphene oxide for promoting neural cell growth. Acta Biomater 2016;37:131–42.
• 16. Fraczek-Szczypta A. Carbon nanomaterials for nerve tissue stimulation and regeneration. Mater Sci Eng C 2014;34:35–49.
• 17. Sąsiada M. The use of neural networks for prediction the effects of nerve tissue regeneration due to carbon nanomaterials with different properties (in Polish). BSc Thesis prepared under supervising Frączek-Szczypta A., AGH University of Science and Technology, 2015.
• 18. Boccaccini AR, Cho J, Roether JA, Thomas BJ, Minay EJ, Shaffer MS. Electrophoretic deposition of carbon nanotubes. Carbon 2006;44:3149–60.
• 19. Mahajan SV, Cho J, Shaffer MS, Boccaccini AR, Dickerson JH. Electrophoretic deposition and characterization of Eu2O3 nanocrystal – carbon nanotube heterostructures. J Eur Ceram Soc 2010;30:1145–50.
• 20. Frączek-Szczypta A, Dlugon E, Weselucha-Birczynska A, Nocun M, Blazewicz M. Multi walled carbon nanotubes deposited on metal substrate using EPD technique: a spectroscopic study. J Mol Struct 2012;18:238–45.
• 21. Tadeusiewicz R. Artificial intelligence as a tool to support the development and testing of biomaterials. Eng Biomater 2001;15–16:8–26.
• 22. Tadeusiewicz R. Neural networks as a tool for modeling of biological systems. Bio-Algorithms Med-Syst 2015;11:135–44.
• 23. Tadeusiewicz R, Chaki R, Chaki N. Exploring neural networks with C#. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2014. ISBN 978-1-4822-3339-1.
• 24. Zhang Z, Friedrich K, Velten K. Prediction on tribological properties of short fibre composites using artificial neural networks. Wear 2002;252:668–75.
• 25. Giwa A, Daer S, Ahmed I, Marpu PR, Hasan SW. Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment. J Water Process Eng 2016;11:88–97.
• 26. Dalkilica AS, Çebia A, Celena A, Yıldızb O, Acikgoza O, Jumpholkulc C, et al. Prediction of graphite nanofluids’ dynamic viscosity by means of artificial neural networks. Int Commun Heat Mass Transfer 2016;73:33–42.
• 27. Ghamariana I, Hayesb B, Samimia P, Welkc BA, Fraserc HL, Collins PC. Developing a phenomenological equation to predict yield strength from composition and microstructure in β processed Ti-6Al-4V. Mater Sci Eng A 2016;660:172–80.
• 28. Li H, Yang S, Zhao W, Xu Z, Zhao S, Liu X. Prediction of the physicochemical properties of woody biomass using linear prediction and artificial neural networks. Energy Sour Pt A Recov Util Environ Effects 2016;38:1569–73.
• 29. Dlugon E, Simka W, Fraczek-Szczypta A, Niemiec W, Markowski J, Szymańska M, et al. Carbon nanotube-based coatings on titanium. Bull Mater Sci 2015;38:1339–44.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)