Shahbazi, F, Esfahani, MN, Jabbari, M et al. (1 more author) (2022) A Molecular Dynamics Model for Biomedical Sensor Evaluation: Nanoscale Numerical Simulation of an Aluminum-Based Biosensor. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 11-15 Jul 2022, Glasgow, United Kingdom. IEEE , pp. 613-616. ISBN 978-1-7281-2782-8
Abstract
Metallic nanostructured-based biosensors provide label-free, multiplexed, and real-time detections of chemical and biological targets. Aluminum-based biosensors are favored in this category, due to their enhanced stability and profitability. Despite the recent advances in nanotechnology and the significant improvement in development of these biosensors, some deficiencies restrict their utilization. Hence a detailed insight into their behavior in different conditions would be crucial, which can be achieved with nanoscale numerical simulation. With this aim, an Aluminum-based biosensor is chosen to be analyzed with the help of all-atom molecular dynamics model (AA-MD), using large-scale atomic/molecular massively parallel simulator (LAMMPS). The surface properties and adsorption process through different flow conditions and various concentration of the target, are investigated in this study. In the future work, the results of this study will be used for developing a predictive model for surface properties of the biosensor. Clinical Relevance— The role of biosensors in clinical applications and early diagnosis is evident. This work provides a model for predicting the binding behavior of the target molecules on the biosensor surface in different conditions. Results demonstrate an increase in the adsorption of ethanol on the biosensor surface of 7% up to 80% with changing the velocity from 0.001 m/s to 1 m/s Although for cases with higher concentration this trend becomes complicated necessitating the implementation of machine learning models in the future works.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Thermofluids, Surfaces & Interfaces (iETSI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Dec 2022 08:48 |
Last Modified: | 13 Dec 2022 08:48 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/EMBC48229.2022.9871498 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194272 |