Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration
-
Martyna Sasiada
Abstract
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.
Author contributions: The authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This work was supported by the National Science Centre – Poland (NCN; grant no. UMO-2013/11/D/ST8/03272), “Carbon nanomaterial coatings on the metal surface as a potential systems for nerve cell regeneration and stimulation”, financed from NCN resources.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Ruoff RS, Qian D, Liu WK. Mechanical properties of carbon nanotubes: theoretical predictions and experimental measurements. C R Phys 2003;4:993–1008.10.1016/j.crhy.2003.08.001Suche in Google Scholar
2. Bandaru PR. Electrical properties and applications of carbon nanotube structures. J Nanosci Nanotechnol 2007;7:1–29.10.1166/jnn.2007.307Suche in Google Scholar PubMed
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;66:165440.10.1103/PhysRevB.66.165440Suche in Google Scholar
4. Pokharel P, Lee SH, Lee DS. Thermal, mechanical, and electrical properties of graphene nanoplatelet/graphene oxide/polyurethane hybrid nanocomposite. J Nanosci Nanotechnol 2015;15:211–4.10.1166/jnn.2015.8353Suche in Google Scholar PubMed
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;137:94–101.10.1016/j.compscitech.2016.10.020Suche in Google Scholar
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, 2014:279–312.10.1016/B978-0-08-098232-8.00012-7Suche in Google Scholar
7. Syama S, Mohanan PV. Safety and biocompatibility of graphene: a new generation nanomaterial for biomedical application. Int J Biol Macromol 2016;86:546–55.10.1016/j.ijbiomac.2016.01.116Suche in Google Scholar PubMed
8. Zhang B, Wang Y, Zhai G. Biomedical applications of the graphene-based materials. Mater Sci Eng C 2016;61:953–64.10.1016/j.msec.2015.12.073Suche in Google Scholar PubMed
9. Fakhrabadi MM, Rastgoo A, Ahmadian MT. Application of electrostatically actuated carbon nanotubes in nanofluidic and bio-nanofluidic sensors and actuators. Measurement 2015;73:127–36.10.1016/j.measurement.2015.05.009Suche in Google Scholar
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;132:410–6.10.1016/j.saa.2014.04.004Suche in Google Scholar PubMed PubMed Central
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, 2016:465–501.10.1016/B978-0-323-47347-7.00017-3Suche in Google Scholar
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;13:324–34.10.1016/j.actbio.2014.11.026Suche in Google Scholar
13. Lee W, Parpura V. Chapter 6 – Carbon nanotubes as substrates/scaffolds for neural cell growth. Prog Brain Res 2009;180:110–25.10.1016/S0079-6123(08)80006-4Suche in Google Scholar
14. Lee J, Kim J, Kim S, Min D-H. Biosensors based on graphene oxide and its biomedical application. Adv Drug Deliv Rev 2016;105:275–87.10.1016/j.addr.2016.06.001Suche in Google Scholar PubMed PubMed Central
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;37:131–42.10.1016/j.actbio.2016.04.008Suche in Google Scholar PubMed
16. Fraczek-Szczypta A. Carbon nanomaterials for nerve tissue stimulation and regeneration. Mater Sci Eng C 2014;34:35–49.10.1016/j.msec.2013.09.038Suche in Google Scholar PubMed
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.Suche in Google Scholar
18. Boccaccini AR, Cho J, Roether JA, Thomas BJ, Minay EJ, Shaffer MS. Electrophoretic deposition of carbon nanotubes. Carbon 2006;44:3149–60.10.1016/j.carbon.2006.06.021Suche in Google Scholar
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;30:1145–50.10.1016/j.jeurceramsoc.2009.08.016Suche in Google Scholar
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;18:238–45.10.1016/j.molstruc.2013.03.010Suche in Google Scholar
21. Tadeusiewicz R. Artificial intelligence as a tool to support the development and testing of biomaterials. Eng Biomater 2001;15–16:8–26.Suche in Google Scholar
22. Tadeusiewicz R. Neural networks as a tool for modeling of biological systems. Bio-Algorithms Med-Syst 2015;11:135–44.10.1515/bams-2015-0021Suche in Google Scholar
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.Suche in Google Scholar
24. Zhang Z, Friedrich K, Velten K. Prediction on tribological properties of short fibre composites using artificial neural networks. Wear 2002;252:668–75.10.1016/S0043-1648(02)00023-6Suche in Google Scholar
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;11:88–97.10.1016/j.jwpe.2016.03.011Suche in Google Scholar
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;73:33–42.10.1016/j.icheatmasstransfer.2016.02.010Suche in Google Scholar
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;660:172–80.10.1016/j.msea.2016.02.052Suche in Google Scholar
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;38:1569–73.10.1080/15567036.2014.934412Suche in Google Scholar
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;38:1339–44.10.1007/s12034-015-1019-4Suche in Google Scholar
©2017 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Review
- Drug generations that combat influenza A virus infection
- Research Articles
- Structure of hydrophobic core in plant carboxylesterase
- Correlated mutations in hydroxysteroid dehydrogenases family
- Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration
- Computational gait analysis using fuzzy logic for everyday clinical purposes – preliminary findings
- Sleep-related breathing biomarkers as a predictor of vital functions
Artikel in diesem Heft
- Frontmatter
- Review
- Drug generations that combat influenza A virus infection
- Research Articles
- Structure of hydrophobic core in plant carboxylesterase
- Correlated mutations in hydroxysteroid dehydrogenases family
- Efficiency testing of artificial neural networks in predicting the properties of carbon nanomaterials as potential systems for nervous tissue stimulation and regeneration
- Computational gait analysis using fuzzy logic for everyday clinical purposes – preliminary findings
- Sleep-related breathing biomarkers as a predictor of vital functions
