Pearl-Chain Formation of Discontinuous Carbon Fiber under an Electrical Field

Daniel, Justin; Ju, Licheng; Yang, Jinshan; Sun, Xiangzhen; Gupta, Nïkhil; Schrand, Amanda M.; Xu, Chengying

doi:10.3390/jmmp1020022

Cited by 9 publications

(9 citation statements)

References 34 publications

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“…Dielectrophoretic (DEP) forces assemble particles from aqueous suspensions into long electrically conductive nano/microstructures due to positioning, convection, and levitation. Electric field-induced dipole formation causes pearl chaining of particles that are aligned along an electric field [4,5]. Pearl chains are direct indicators of DEP forces and can be correlated to dielectric variations of a variety of microparticles, including biological cells [1][2][3]6,7].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Based Estimation of Dielectrophoretic Force

2021

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The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

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Section: Introductionmentioning

confidence: 99%

Deep-Learning Based Estimation of Dielectrophoretic Force

2021

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“…At low Reynolds numbers (<<1), the viscous term dominates the fluid dynamics. According to Stokes' law, the balanced angular velocity can be written as [24, 25]:

ω = \frac{f_{D}}{f_{s}} E^{2} \sin 2 θ

where,

f_{s} = \frac{2 π η (l^{2} + 4 r^{2}) l}{3 [2 \ln (\frac{l}{r}) - 1]}

, and η is the kinematic coefficient of the viscosity.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…At low Reynolds numbers (<< 1), the viscous term dominates the fluid dynamics. According to Stokes' law, the balanced angular velocity can be written as [20,21]…”

Section: Fig 1 Schematic Of a Rod-like Colloidal Particle In An Appli...mentioning

confidence: 99%

Analytical model for the transient permittivity of uncured TiO ₂ whisker/liquid silicone rubber composites under an AC electric field

et al. 2020

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The electric field grading of dielectric permittivity gradient devices is an effective way of enhancing their insulation properties. The in situ electric field‐driven assembly is an advanced method for the fabrication of insulation devices with adaptive permittivity gradients; however, there is no theoretical guidance for design. In this study, an analytical model with a time constant is developed to determine the transient permittivity of uncured composites under an applied AC electric field. This model is based on optical image and dielectric permittivity monitoring, which avoids the direct processing of complex electrodynamics. For a composite with given components, the increased filler content and electric field strength can accelerate the transient process. Compared with the finite element method based on differential equations, this statistical model is simple but efficient, and can be applied to any low‐viscosity uncured composites, which may contain multiple fillers. More importantly, when a voltage is applied to an uncured composite insulating device, the proposed model can be used to analyse the spatiotemporal permittivity characteristics of this device and optimise its permittivity gradient for electric field grading.

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“…The use of external fields to control the directionality or orientation of particles or fibers introduced into polymeric matrices have been sparingly investigated over the past decade. Materials with such capabilities have shown immense promise in the aspect of imparting properties such as self-healing, self-assembly and improved strength in localized areas of a composite part in a non-invasive manner during the manufacturing process [53,[57][58][59][60][61][62]. In addition, such composites have also seen applications in electronics for their controllable conductivity, capacitance and optical properties with an advantage of roll-to-roll on-demand production capabilities [63][64][65].…”

Section: How Composites Are Finding Their Way Into Traditional Ammentioning

confidence: 99%

“…No mechanical tests were performed on the prepared samples to validate improvement in structural behavior [85]. SWNT-polymer composites with controlled electrical and dielectric properties can be produced by actively introducing an AC field [62]. The use of a stereolithography system often raises issues with suspended filler material as higher weight percentages can lead to sedimentation and high fluid viscosity can impede rapid particle reorientation.…”

Section: Electric Field Assisted Additive Manufacturingmentioning

confidence: 99%

Composite Reinforcement Architectures: A Review of Field-Assisted Additive Manufacturing for Polymers

et al. 2019

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The demand for additively manufactured polymer composites with increased specific properties and functional microstructure has drastically increased over the past decade. The ability to manufacture complex designs that can maximize strength while reducing weight in an automated fashion has made 3D-printed composites a popular research target in the field of engineering. However, a significant amount of understanding and basic research is still necessary to decode the fundamental process mechanisms of combining enhanced functionality and additively manufactured composites. In this review, external field-assisted additive manufacturing techniques for polymer composites are discussed with respect to (1) self-assembly into complex microstructures, (2) control of fiber orientation for improved interlayer mechanical properties, and (3) incorporation of multi-functionalities such as electrical conductivity, self-healing, sensing, and other functional capabilities. A comparison between reinforcement shapes and the type of external field used to achieve mechanical property improvements in printed composites is addressed. Research has shown the use of such materials in the production of parts exhibiting high strength-to-weight ratio for use in aerospace and automotive fields, sensors for monitoring stress and conducting electricity, and the production of flexible batteries.

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Pearl-Chain Formation of Discontinuous Carbon Fiber under an Electrical Field

Cited by 9 publications

References 34 publications

Deep-Learning Based Estimation of Dielectrophoretic Force

Deep-Learning Based Estimation of Dielectrophoretic Force

Analytical model for the transient permittivity of uncured TiO ₂ whisker/liquid silicone rubber composites under an AC electric field

Composite Reinforcement Architectures: A Review of Field-Assisted Additive Manufacturing for Polymers

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Pearl-Chain Formation of Discontinuous Carbon Fiber under an Electrical Field

Cited by 9 publications

References 34 publications

Deep-Learning Based Estimation of Dielectrophoretic Force

Deep-Learning Based Estimation of Dielectrophoretic Force

Analytical model for the transient permittivity of uncured TiO 2 whisker/liquid silicone rubber composites under an AC electric field

Composite Reinforcement Architectures: A Review of Field-Assisted Additive Manufacturing for Polymers

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Analytical model for the transient permittivity of uncured TiO ₂ whisker/liquid silicone rubber composites under an AC electric field