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Hypothesis: Symmetry breaking in an electric field-driven active particle system can be induced by applying a spatially uniform, but temporally non-uniform, alternating current (AC) signal. Regardless of the type of particles exposed to sawtooth AC signals, the unevenly induced polarization of the ionic charge layer leads to a major electrohydrodynamic effect of active propulsion, termed Asymmetric Field Electrophoresis (AFEP). Experiments: Suspensions containing latex microspheres of three sizes, as well as Janus and metal-coated particles were subjected to sawtooth AC signals of varying voltages, frequencies, and time asymmetries. Particle tracking via microscopy was used to analyze their motility as a function of the key parameters. Findings: The particles exhibit field-colinear active propulsion, and the temporal reversal of the AC signal results in a reversal of their direction of motion. The experimental velocity data as a function of field strength, frequency, and signal asymmetry are supported by models of asymmetric ionic concentration-polarization. The direction of particle migration exhibits a size-dependent crossover in the low frequency domain. This enables new approaches for simple and efficient on-chip sorting. Combining AFEP with other AC motility mechanisms, such as induced-charge electrophoresis, allows multiaxial control of particle motion and could enable development of novel AC field-driven active microsystems.
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Utilizing Steady-State Traveling Waves in a Quiescent Water Environment for Particle Propulsion
Abstract Using steady-state traveling waves as a propulsion mechanism emerges as a highly effective strategy for displacing particles, eliminating the need for an external fluid transfer pump. This experimental inquiry delves into the intricate application of traveling waves within a beam submerged in quiescent water, deploying two distinctive force input methods to govern particle movement acoustically. The complexity of this research lies in balancing the finesse of particle motion while concurrently imposing constraints on the number of control cycles implemented. To address this challenge comprehensively, we introduce a diverse range of control cycles tailored to manipulate particles of varying sizes. Navigating the nuanced dynamics of this system requires a sophisticated approach, prompting the adoption of the Reinforcement Learning Approach. This methodological choice empowers us to discern the characteristics of traveling waves necessary for facilitating the movement of particles with divergent sizes. The utilization of Reinforcement Learning not only refines our understanding of the intricate interplay between waves and particles but also enhances our ability to optimize control strategies in this particular context. The significance of this research extends beyond the confines of the laboratory, resonating in various applications, with particular prominence in advancing transportation mechanisms for cells and analogous entities. By elucidating the underlying principles governing the interaction between traveling waves and particles of different sizes, the findings offer invaluable insights that can be harnessed to optimize particle manipulation techniques. This holds potential implications in biotechnology, where the precision control of particle movement is pivotal for applications ranging from targeted drug delivery to the manipulation of biological cells. Furthermore, our exploration not only contributes to the theoretical understanding of particle manipulation through traveling waves but also yields tangible practical implications. The versatility of our approach, as exemplified through the successful manipulation of particles with varying sizes, underscores its potential applicability across a spectrum of scenarios, emphasizing its broader relevance within the burgeoning field of acoustic fluids. In conclusion, the utilization of steady-state traveling waves as a particle propulsion mechanism, as showcased in this experimental investigation, not only holds promise for the advancement of particle manipulation but also underscores its potential impact in diverse applications. Through the thorough exploration of control cycles and the strategic application of the Reinforcement Learning Approach, this research not only contributes to the theoretical knowledge underpinning acoustofluidics but also provides practical methodologies for precision particle manipulation. These advancements are poised to play a pivotal role in shaping the future landscape of biotechnology and related fields, where fine-tuned control over particle dynamics is a cornerstone for innovation and progress.
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- Award ID(s):
- 2301776
- PAR ID:
- 10562623
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8832-2
- Format(s):
- Medium: X
- Location:
- Atlanta, Georgia, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/SMASIS2024-140461
