Tunable Organic Active Neural Probe Enabling Near‐Sensor Signal Processing

Lee, Yong-Woo; Carnicer‐Lombarte, Alejandro; Han, Sanggil; Woodington, Ben J.; Chai, Seung Wan; Polyravas, Anastasios G.; Velasco‐Bosom, Santiago; Kim, Eun‐Hee; Malliaras, George G.; Jung, Sungjune

doi:10.1002/adma.202301782

Cited by 8 publications

(1 citation statement)

References 51 publications

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“…The highest value of Oh is determined by the dissipation of the pressure wave by the ink viscosity, whereas the lowest limit is determined by the formation of smaller satellite droplets during the jet formation process [15]. However, functional inks exhibit viscoelastic behavior in many emerging digital fabrication applications [16][17][18][19]. The addition of a small amount of functional materials can significantly influence jet formation by inducing viscoelasticity.…”

Section: Introductionmentioning

confidence: 99%

Predicting inkjet jetting behavior for viscoelastic inks using machine learning

Kim,

Wenger,

Bürgy

et al. 2023

Flex. Print. Electron.

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Inkjet printing offers significant potential for additive manufacturing technology. However, predicting jetting behavior is challenging because the rheological properties of functional inks commonly used in the industry are overlooked in printability maps that rely on the Ohnesorge and Weber numbers. We present a machine learning-based predictive model for jetting behavior that incorporates the Deborah number, the Ohnesorge number, and the waveform parameters. Tenviscoelastic inks have been prepared and their storage modulus and loss modulus measured, showing good agreement with those obtained by the theoretical Maxwell model. With the relaxation time of the viscoelastic ink obtained by analyzing the Maxwell model equations, the Deborah number could be calculated. We collected a large data set of jetting behaviors of each ink with various waveforms using drop watching. Three distinct machine learning models were employed to build predictive models. After comparing the prediction accuracy of the machine learning models, we found that multilayer perceptron showed outstanding prediction accuracy. The final predictive model exhibited remarkable accuracy for an unknown ink based on waveform parameters, and the correlation between jetting behavior and ink properties was reasonable. Finally, we developed a printability map characterized by the Ohnesorge and Deborah numbers through the proposed predictive model for viscoelastic fluids and the chosen industrial printhead.

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Section: Introductionmentioning

confidence: 99%

Predicting inkjet jetting behavior for viscoelastic inks using machine learning

Kim,

Wenger,

Bürgy

et al. 2023

Flex. Print. Electron.

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Highly Stretchable Conductor Inspired by Compliant Mechanism

Lu,

Deng,

Zhou

et al. 2023

Adv Elect Materials

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Flexible and stretchable conductors have invaluable applications in multiple domains, such as sensors, displays, and electronic skins. The stable conductance exhibited by conductors when subjected to diverse forms of deformation, such as tensile stress, curvature, or torsion, represents a fundamental characteristic. Attaining high conductivity and stretchability simultaneously in conductive materials is a formidable challenge, owing to inherent constraints in materials found in nature. To overcome this problem, an innovative approach of structurally designing conductors using existing materials to achieve high deformability and stretchability, i.e. stretchable conductors inspired by a compliant mechanism is proposed in this paper. Thus, a novel stretchable conductor inspired by flexible mechanisms is introduced. Unlike stretchable conductors based on Kirigami structures, the stretchable conductor based on flexible mechanisms can achieve large in‐plane deformation within the material's strength limit. The concept and design process of the highly deformable stretchable conductor inspired by flexible mechanisms are presented in this paper. Experimental results show that the resistance change ratio of the conductor remains within 0.05% during the 0–200% strain process. The consistency and durability of the conductor during stretching deformation are also confirmed through 500 repetitions of the test. Additionally, the experiments with the electric motor and light‐emitting diode (LED) light confirm the conductor's ability to maintain a stable current.

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On‐Demand Picoliter‐Level‐Droplet Inkjet Printing for Micro Fabrication and Functional Applications

You,

Wang,

Lin

et al. 2024

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With the advent of Internet of Things (IoTs) and wearable devices, manufacturing requirements have shifted toward miniaturization, flexibility, environmentalization, and customization. Inkjet printing, as a non‐contact picoliter‐level droplet printing technology, can achieve material deposition at the microscopic level, helping to achieve high resolution and high precision patterned design. Meanwhile, inkjet printing has the advantages of simple process, high printing efficiency, mask‐free digital printing, and direct pattern deposition, and is gradually emerging as a promising technology to meet such new requirements. However, there is a long way to go in constructing functional materials and emerging devices due to the uncommercialized ink materials, complicated film‐forming process, and geometrically/functionally mismatched interface, limiting film quality and device applications. Herein, recent developments in working mechanisms, functional ink systems, droplet ejection and flight process, droplet drying process, as well as emerging multifunctional and intelligence applications including optics, electronics, sensors, and energy storage and conversion devices is reviewed. Finally, it is also highlight some of the critical challenges and research opportunities. The review is anticipated to provide a systematic comprehension and valuable insights for inkjet printing, thereby facilitating the advancement of their emerging applications.

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Tunable Organic Active Neural Probe Enabling Near‐Sensor Signal Processing

Cited by 8 publications

References 51 publications

Predicting inkjet jetting behavior for viscoelastic inks using machine learning

Predicting inkjet jetting behavior for viscoelastic inks using machine learning

Highly Stretchable Conductor Inspired by Compliant Mechanism

On‐Demand Picoliter‐Level‐Droplet Inkjet Printing for Micro Fabrication and Functional Applications

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