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Direct and Plasmonic Nanoparticle‐Mediated Infrared Neural Stimulation: Comprehensive Computational Modeling and Validation
Author(s) -
Ebtehaj Zahra,
Malekmohammad Mohammad,
Hatef Ali,
Soltanolkotabi Mahmood
Publication year - 2021
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202000214
Subject(s) - plasmon , computational model , monte carlo method , computer science , materials science , nanofluid , biological system , artificial neural network , nanoparticle , infrared , nanotechnology , artificial intelligence , optics , optoelectronics , physics , statistics , mathematics , biology
Infrared neural stimulation techniques have potential applications in the diagnosis and treatment of numerous neurological and psychiatric disorders. There has been little progress in the computational modeling of these techniques and further improvement is needed in this area. In this paper, a comprehensive computational model is presented for simulating the complete mechanism of direct and plasmonic nanoparticle‐mediated infrared neural stimulation techniques in schematic samples of experimental setups. The simulation process involves three phases: 1) Simulating the light transmission and absorption in setups containing pure water or a gold nanorod solution using developed 3D, time‐independent, and time‐dependent Monte Carlo models, 2) calculating the spatiotemporal evolutions of temperature within the setup using the finite difference method and a presented novel method, and 3) simulating the thermally induced responses of lipid membranes using an improved method compared to existing theoretical models. The model is validated by comparing the computational results with existing experimental data. The effect of the laser pulse characteristics, nanofluid properties, and some other related parameters on the thermally induced membrane responses is investigated. The computational results help to optimize the parameters selection and maximize the overall efficiency of the infrared neural stimulation techniques.