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May 17, 2026Chemical Product and Process Modeling0 citationsOpen Access

Optimizing performance of fluid based microcantilever sensor system subjected to chemical kinetics by employing response surface method

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AUAsad UllahSBSardar BilalMFMuhammad Farooq

Key Points

  • This research aims to improve the performance of fluid-based microcantilever sensors by analyzing the effects of nanoparticles and fluid dynamics.
  • Developed a nonlinear ordinary differential framework for sensor analysis.
  • Employed a response surface method to assess relationships between variables and skin friction.
  • Utilized MATLAB's BVP4C code for solving boundary value problems.
  • Achieved a 100% coefficient of determination for wall drag coefficient, indicating a perfect fit for the empirical model.
  • Demonstrated effective energy transmission enhancement using nanoparticles under specific conditions.

Abstract

Abstract This study presents the role of advancement in elevating the energy transmission in liquid passed over a microcantilever sensor domain enclosed in a compressed channel with the addition of three distinct nanoparticles. Materials with superior thermal attributes distributed over cantilever surface contains potential utilization in production of sheets made of plastics and fibers, maintenance of temperature during casting of metals, microprocessors cooling, photovoltaics, medicine therapies. To notice exclusive heat and mass exchange at the boundary, a Newtonian constraint is employed to inspect the transportation of species and thermal aspects. The physical aspects of the applied magnetic field are also considered. A framework is developed in view of nonlinear ordinary differential setup using similar transformations. To obtain solution of the coupled and highly nonlinear system, we have utilized MATLAB built in boundary value problem 4th order code (BVP4C). Furthermore, response surface approach is implemented to analyze relationship between the considered parametric factors and the response variable, which is skin friction in this case. The quality of fit for the empirical relation is evaluated from variance analysis table. Outcomes express that the coefficient of determination for wall drag coefficient is 100 %, signifying that the derived empirical relation exhibits an excellent fit.

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Cite This Study

Ullah et al. (2026) studied this question.

synapsesocial.com/papers/6a095c2c7880e6d24efe2294https://doi.org/10.1515/cppm-2025-0323
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