journal article·2022-09Open access

Controlling Ag microfibres during the electrospinning process using a manipulated electric field for the maskless patterning of transparent conductive electrodes

Assareeya Aoboun, Gi-Hwan Kang, Chiravoot Pechyen, Dong-Youn Shin

Abstract

A novel and facile electrospinning method is presented to fabricate transparent conductive electrodes (TCEs) on the intended area without the use of a physical medium, such as a stencil mask. By applying two different voltages to the ground and underground collectors, the electric fields between the nozzle-to-ground and nozzle-to-underground collectors are manipulated and the jet path of silver (Ag) microfibres can be significantly deflected towards where Ag microfibres are required. Using the developed electrospinning method, full-area TCEs are fabricated without using electric field manipulation for understanding the baseline performance. They exhibit a lowest sheet resistance (Rs) of 0.62 ± 0.19 Ω/sq with optical transmittance (T%) of 86.69 ± 1.55, and the highest T% of 94.76 ± 0.87 with Rs of 20.90 ± 0.45 Ω/sq. For demonstrating the capability of the developed electrospinning method, localised TCEs are fabricated by regulating the electric field between the nozzle-to-ground and nozzle-to-underground collectors, thereby controlling the deposition region of the Ag microfibres on the substrate; the approximate Rs of each localised TCE pattern is well below 15 Ω/sq. The shortest separated gap between localised TCEs is 466.23 μm on average, achieved by electric field manipulation without the use of a physical medium. Using this maskless patterning technique, solar media façades are demonstrated, and their relative performance is found to be as high as 96.28% with respect to bare solar modules.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Electrospun Nanofibers in Biomedical Applications · Nanomaterials and Printing Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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