Feasibility of polyethylene film as both supporting material for transfer and target substrate for flexible strain sensor of CVD graphene grown on Cu foil

Shu-xian, Cai; Liu, Xingfang; Huang, Jian‐An; Z, Liu

doi:10.1039/c7ra09492b

Cited by 15 publications

(5 citation statements)

References 45 publications

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“…The fabrication of microstructure electrodes usually requires complex molds and time-consuming transfer processes, of which patterns are mostly obtained by photolithography, 6,35 dip-coating, 36,37 and chemical etching. 38 Given the high cost and complicated production process, it is difficult to obtain a large-scale application. Recently, some new preparation methods have also appeared, such as Su et al, 39 used the leaf structure of mimosa to prepare a flexible pressure sensor, and the pressure detection range was 0−1.5 kPa.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Therefore, most studies focus on improving the sensitivity of the sensors mainly through the microstructure of the electrode surface and dielectric layer. The fabrication of microstructure electrodes usually requires complex molds and time-consuming transfer processes, of which patterns are mostly obtained by photolithography, , dip-coating, , and chemical etching . Given the high cost and complicated production process, it is difficult to obtain a large-scale application.…”

Section: Introductionmentioning

confidence: 99%

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Capacitive Pressure Sensor with Wide-Range, Bendable, and High Sensitivity Based on the Bionic Komochi Konbu Structure and Cu/Ni Nanofiber Network

Wang

Suzuki

Shao³

et al. 2019

ACS Appl. Mater. Interfaces

123

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High-performance flexible pressure sensors have an essential application in many fields such as human detection and human−computer interaction. Herein, on the basis of the dielectric layer of a bionic komochi konbu structure, we propose a low-cost and novel capacitive sensor that achieves high sensitivity and stability over a broad range of tactile pressures. Further, the flexible and durable electrode layer of the transparent junctionless copper/nickel-nanonetwork was prepared based on electrospinning and electroless deposition techniques, which ensured high bending stability and high cycle stability of our sensor. More importantly, because of the sizeable protruding structure and internal micropores in the elastomer structure we designed, the inward curling of the protruding structure and the effectual closing of the micropores increase the effective dielectric constant under the action of the compressive force, improving the sensitivity of the sensor. Measured response and relaxation time (162 ms) are 250 times faster than those of a conventional flat polydimethylsiloxane capacitive sensor. In addition, the fabricated capacitive pressure sensor demonstrates the ability to be used on wearable applications, not only to quickly recognize the tapping and bending of a finger but also to show that the pressure of the finger can be sensed when the finger grabs the object. The sensors we have developed have shown great promise in practical applications, such as human rehabilitation and exercise monitoring, as well as human−computer interaction control.

show abstract

Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Capacitive Pressure Sensor with Wide-Range, Bendable, and High Sensitivity Based on the Bionic Komochi Konbu Structure and Cu/Ni Nanofiber Network

Wang

Suzuki

Shao³

et al. 2019

ACS Appl. Mater. Interfaces

123

View full text Add to dashboard Cite

show abstract

“…Many 2D materials can be produced in a large area ∼cm 2 using chemical vapor deposition (CVD) that also show contrasts in color depending on thickness [49][50][51] but the small area ∼ µm 2 exfoliation method is still needed to obtain higher-quality 2D materials by avoiding contamination in the process of transferring it from the growth substrate [52,53]. The graphene samples in the present work were exfoliated using scotch tapes and placed on top of 285 nm SiO 2 /Si substrates as shown in figure 1(a).…”

Section: Dataset Preparationmentioning

confidence: 99%

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms

Cho

Shin

Kim

et al. 2022

Mach. Learn.: Sci. Technol.

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Mechanical exfoliation of graphene and its identification by optical inspection is one of the mile- stones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise classification methods showing a high perfor- mance even with a small number of training image datasets that require short computational time without GPU. We introduce four different tree-based machine learning algorithms – decision tree, random forest, extreme gradient boost, and light gradient boosting machine. We train them with five optical microscopy images of graphene, and evaluate their performances with multiple metrics and indices. We also discuss combinatorial machine learning models between the three single classi- fiers and assess their performances in identification and reliability. The code developed in this paper is open to the public and will be released at github.com/gjung-group/Graphene_segmentation.

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“…Second, large scale semi-insulating 4H-SiC wafers (SI-SiC) are commercially available and can be used for graphene preparation. This makes graphene devices able to be fabricated directly on SI-SiC wafers without a transfer of graphene to the foreign substrate, which overcomes the drawback of graphene grown on metal foil [36,37]. Third, and we think the most important point, fine graphene with elegant structures can be prepared on mis-cut 4H-SiC wafers through direct growth without complicated processes.…”

Section: Introductionmentioning

confidence: 97%

Growth of Ordered Graphene Ribbons by Sublimation Epitaxy

et al. 2018

Self Cite

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Ordered graphene ribbons were grown on the surface of 4° off-axis 4H-SiC wafers by sublimation epitaxy, and characterized by using scanning electron microscopy (SEM), atomic force microscopy (AFM) and micro-Raman spectroscopy (μ-Raman). SEM showed that there were gray and dark ribbons on the substrate surface, and AFM further revealed that these ordered graphene ribbons had clear stepped morphologies due to surface step-bunching. It was shown by μ-Raman that the numbers of graphene layers of these two types of regions were different. The gray region was composed of mono- or bilayer ordered graphene ribbon, while the dark region was of tri- or few-layer ribbon. Meanwhile, ribbons were all homogeneous and had a width up to 40 μm and a length up to 1000 μm, without micro defects such as grain boundaries, ridges, or mono- and few-layer graphene mixtures. The results of this study are useful for optimized growth of high-quality graphene film on silicon carbide crystal.

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Feasibility of polyethylene film as both supporting material for transfer and target substrate for flexible strain sensor of CVD graphene grown on Cu foil

Abstract: Facile utilization of polyethylene (PE) film as both the supporting material for graphene transfer from copper foil and the target substrate for flexible strain sensor preparation in a single route.

Cited by 15 publications

References 45 publications

Capacitive Pressure Sensor with Wide-Range, Bendable, and High Sensitivity Based on the Bionic Komochi Konbu Structure and Cu/Ni Nanofiber Network

Capacitive Pressure Sensor with Wide-Range, Bendable, and High Sensitivity Based on the Bionic Komochi Konbu Structure and Cu/Ni Nanofiber Network

Pixel-wise classification in graphene-detection with tree-based machine learning algorithms

Growth of Ordered Graphene Ribbons by Sublimation Epitaxy

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