Analyzing of the diffusion constant on the nano-scale systems by using artificial neural networks
| dc.authorid | 0000-0002-6179-5746 | en_US |
| dc.contributor.author | Işık, Esme | |
| dc.date.accessioned | 2021-12-02T08:32:20Z | |
| dc.date.available | 2021-12-02T08:32:20Z | |
| dc.date.issued | 2021 | en_US |
| dc.department | MTÖ Üniversitesi, Darende Meslek Yüksekokulu, Tıbbi Hizmetler ve Teknikler Bölümü | en_US |
| dc.description.abstract | The study concerning nano-scale systems is considered to highly contribute to the developments in the field of nano-technology where many models have been proposed in the literature. The information is carried by molecules in the diffusion medium of the models. Channel parameters such as the diffusion constant are very important for communication of the molecules between the transmitter and the receiver. The physical properties of the carriers and the density of the medium are also very important for the transfer of information. In this study, the number of received molecules is analyzed with respect to the environmental parameters of the channel such as viscosity and the diffusion constant. First, the diffusion constant is obtained analytically by using the Stokes–Einstein equation, and then a new model was developed in Matlab and analyzed in terms of performance of the system concerning channel parameters such as the diffusion constant. Second, the diffusion constant of the medium was predicted by using an artificial neural network and compared with the simulation results. The different diffusion constant values have been used in the environment contrary to the literature to obtain the number of received molecules. The predicted values of the number of received molecules for D = 75 µm2/s and D = 150 µm2/s were also obtained for mobile and fixed system models. The difference between predicted and simulation values is obtained as ±0.5 by using residual analysis. | en_US |
| dc.identifier.citation | Isik, E. (2021). Analyzing of the diffusion constant on the nano-scale systems by using artificial neural networks. AIP Advances, 11(10), 105105. | en_US |
| dc.identifier.doi | 10.1063/5.0067795 | |
| dc.identifier.endpage | 8 | en_US |
| dc.identifier.issn | 2158-3226 | en_US |
| dc.identifier.issue | 10 | en_US |
| dc.identifier.scopus | 2-s2.0-85117114538 | en_US |
| dc.identifier.scopusquality | Q2 | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | https://doi.org/10.1063/5.0067795 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12899/507 | |
| dc.identifier.volume | 11 | en_US |
| dc.identifier.wos | WOS:000721712000002 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Işık, Esme | |
| dc.language.iso | en | en_US |
| dc.publisher | American Institute of Physics | en_US |
| dc.relation.ispartof | AIP Advances | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Analyzing of the diffusion constant on the nano-scale systems by using artificial neural networks | en_US |
| dc.type | Article | en_US |
