Taheri, M., ghane, F. (2025). Determining the critical time for two-dimensional manipulation of micro-nanoparticles using a regression equation. , (), -. doi: 10.22067/jacsm.2025.93715.1332
Moein Taheri; fereshte ghane. "Determining the critical time for two-dimensional manipulation of micro-nanoparticles using a regression equation". , , , 2025, -. doi: 10.22067/jacsm.2025.93715.1332
Taheri, M., ghane, F. (2025). 'Determining the critical time for two-dimensional manipulation of micro-nanoparticles using a regression equation', , (), pp. -. doi: 10.22067/jacsm.2025.93715.1332
Taheri, M., ghane, F. Determining the critical time for two-dimensional manipulation of micro-nanoparticles using a regression equation. , 2025; (): -. doi: 10.22067/jacsm.2025.93715.1332
Determining the critical time for two-dimensional manipulation of micro-nanoparticles using a regression equation
1Associate Professor, Department of Manufactring Engineering, Faculty of Engineering, Arak University, Arak, Iran.
2Bacholar Student, Department of Manufactring Engineering, Faculty of Engineering, Arak University, Arak, Iran
Abstract
Manipulation and relocation of particles at the nano- and microscale using Atomic Force Microscopy (AFM) is recognized as one of the advanced techniques in nanotechnology, commonly referred to as nanomanipulation. This process consists of two main stages: the initial phase and the secondary phase. One of the key factors in enhancing the accuracy and efficiency of this process is minimizing the critical manipulation time. Therefore, identifying and analyzing the factors affecting this time is of significant importance. In this study, the effect of the dimensional parameters of the AFM cantilever—including thickness, width, and length of the cantilever, as well as the tip radius and height—on the critical time of nanomanipulation is investigated. To this end, Response Surface Methodology (RSM) is employed for data analysis and the development of a mathematical model. The regression model derived from the experimental data demonstrates a prediction accuracy of 91.04%, indicating a high level of reliability. The results reveal that among the parameters studied, cantilever thickness has the most significant impact on the critical time.