Optimization of Electrode Patterns for an ITO-Based Digital Microfluidic through the Finite Element Simulation

Song, Zerui; Zeng, Jin; Zhou, Jiale; Yan, Binbin; Gu, Zhen; Wang, Huifeng

doi:10.3390/mi13101563

Cited by 4 publications

(5 citation statements)

References 32 publications

(32 reference statements)

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“…The electrodes were directly etched onto the bottom plate (52 mm x 50 mm) using a UV laser operating at a wavelength of 355 nm and a pulse duration of 20 μs. 45 Then, the glass plates were cleaned with ethanol for 10 minutes to remove residual material and exposed to plasma treatment for 1 minute. Following the application of a vacuum -deposited Parylene C film (~2 µm thick) onto the bottom plate surface as a dielectric layer, CYTOP films were spin-coated (2000rpm, 30s) onto both the top and bottom plates to serve as hydrophobic layers.…”

Section: Chip Fabricationmentioning

confidence: 99%

Section: Chip Fabricationmentioning

confidence: 99%

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An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

Guo,

Song,

Zhou

et al. 2023

Preprint

Self Cite

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Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high freedom in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems with informed of the transient state of droplets and their interactions. However, most related studies focus on the localization and shape recognition of droplets. Here, we develop an AI-assisted DMF framework named “μDropAI” for multistate droplet control based on droplet morphology. Semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has a strong generalization ability and can recognize droplets of different colors and shapes with an error rate of less than 0.63%. It enables control of droplets without user intervene. The proposed system will inspire the development of semantic-driven DMF systems which can interface with artificial general intelligence (AGl) models for fully automatic control.

show abstract

Section: Chip Fabricationmentioning

confidence: 99%

Section: Chip Fabricationmentioning

confidence: 99%

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

Guo,

Song,

Zhou

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The electrodes were directly etched onto the bottom plate (52 mm x 50 mm) using a UV laser operating at a wavelength of 355 nm and a pulse duration of 20 µs. 47 Then, the glass plates were cleaned with ethanol for 10 minutes to remove residual material and exposed to plasma treatment for 1 minute. Following the application of a vacuum-deposited Parylene C lm (~ 2 µm thick) onto the bottom plate surface as a dielectric layer, CYTOP lms were spin-coated (2000rpm, 30s) onto both the top and bottom plates to serve as hydrophobic layers.…”

Section: Chip Fabricationmentioning

confidence: 99%

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

Gu,

Guo,

Song

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high freedom in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems with informed of the transient state of droplets and their interactions. However, most related studies focus on the localization and shape recognition of droplets. Here, we develop an AI-assisted DMF framework named "µDropAI" for multistate droplet control based on droplet morphology. Semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has a strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%. It enables control of droplets without user intervention. The coefficient variation (CV) of the volumes of split droplets can be limited to below 2.8%, which is lower than the CV of traditional dispense, contributing to an improvement in the precision of droplet split. The proposed system will inspire the development of semantic-driven DMF systems which can interface with multimodal large language models (MLLM) for fully automatic control.

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“…Coatings 2024, 14, 178 3 of 16 potentially interesting technical decision for microfluidics applications (as an alternative to ITO with UV treatment [45], patterned ITO [46], or ITO coated on a PET film [47]).…”

Section: Introductionmentioning

confidence: 99%

Perspective Coatings Based on Structured Conducting ITO Thin Films for General Optoelectronic Applications

Toikka,

Ilin,

Kamanina

2024

Coatings

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In many electro-optical devices, the conductive layer is an important key functional element. Among others, unique indium tin oxide (ITO) contacts take priority. ITO structure is widely used as the optical transparent and electrically conductive material in general optoelectronics, biosensors and electrochemistry. ITO is one of the key elements in the liquid crystal (LC) displays, spatial light modulators (SLMs) and LC convertors. It should be mentioned that not only the morphology of this layer structure but also the surface features play an important role in the study of the physical parameters of the ITO. In order to switch the surface properties (roughness, average tilt angle and surface free energy) of the ITO via the laser-oriented deposition (LOD) method, carbon nanotubes (CNTs) were implanted. In the LOD technique, the CO2 laser (λ = 10.6 μm, P = 30 W) with the control electric grid was used. The switching of the deposition conditions was provided via the varying electrical strength of the control grid in the range of 100–600 V/cm. The diagnostics of the surfaces were performed using AFM analysis and wetting angle measurements. The components of the surface free energy (SFE) were calculated using the OWRK method. The main experimental results are as follows: the roughness increases with a rise in the electric field strength during the deposition of the CNTs; the carbon nanotubes provide a higher level of the dispersive component of SFE (25.0–31.4 mJ/m2 against 22.2 mJ/m2 in the case of pure ITO); the CNTs allow an increase in the wetting angle of the 5CB liquid crystal drops from 38.35° to 58.95°. Due to the possibility of the switching properties of the ITO/CNT surfaces, these modifications have potential interest in microfluidics applications and are useful for the liquid crystal’s electro-optics.

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Optimization of Electrode Patterns for an ITO-Based Digital Microfluidic through the Finite Element Simulation

Cited by 4 publications

References 32 publications

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

Perspective Coatings Based on Structured Conducting ITO Thin Films for General Optoelectronic Applications

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