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Title: Optimizing Optical Dielectrophoretic (ODEP) Performance: Position- and Size-Dependent Droplet Manipulation in an Open-Chamber Oil Medium
An optimization study is presented to enhance optical dielectrophoretic (ODEP) performance for effective manipulation of an oil-immersed droplet in the floating electrode optoelectronic tweezers (FEOET) device. This study focuses on understanding how the droplet’s position and size, relative to light illumination, affect the maximum ODEP force. Numerical simulations identified the characteristic length (Lc) of the electric field as a pivotal factor, representing the location of peak field strength. Utilizing 3D finite element simulations, the ODEP force is calculated through the Maxwell stress tensor by integrating the electric field strength over the droplet’s surface and then analyzed as a function of the droplet’s position and size normalized to Lc. Our findings reveal that the optimal position is xopt= Lc+ r, (with r being the droplet radius), while the optimal droplet size is ropt = 5Lc, maximizing light-induced field perturbation around the droplet. Experimental validations involving the tracking of droplet dynamics corroborated these findings. Especially, a droplet sized at r = 5Lc demonstrated the greatest optical actuation by performing the longest travel distance of 13.5 mm with its highest moving speed of 6.15 mm/s, when it was initially positioned at x0= Lc+ r = 6Lc from the light’s center. These results align well with our simulations, confirming the criticality of both the position (xopt) and size (ropt) for maximizing ODEP force. This study not only provides a deeper understanding of the position- and size-dependent parameters for effective droplet manipulation in FEOET systems, but also advances the development of low-cost, disposable, lab-on-a-chip (LOC) devices for multiplexed biological and biochemical analyses.  more » « less
Award ID(s):
2046134
PAR ID:
10491599
Author(s) / Creator(s):
;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Micromachines
Volume:
15
Issue:
1
ISSN:
2072-666X
Page Range / eLocation ID:
119
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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