4.8 Article

Silicon Shadow Mask Technology for Aligning and In Situ Sorting of Semiconducting SWNTs for Sensitivity Enhancement: A Case Study of NO2 Gas Sensor

期刊

ACS APPLIED MATERIALS & INTERFACES
卷 12, 期 36, 页码 40901-40909

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsami.0c10189

关键词

chemical-free lithography; silicon shadow mask; semiconducting carbon nanotubes alignment; SWNT sorting; NO2 gas sensor; chemiresistor

资金

  1. Council of Scientific and Industrial Research (CSIR), Government of India, under the CSIR EMPOWER project [OLP5101]
  2. CSIR mission mode project [HCP0012]

向作者/读者索取更多资源

Single-walled carbon nanotubes (SWNTs) are incorporated in different device configurations such as chemiresistors and field-effect transistors (FETs) as a sensing element for the fabrication of highly sensitive and specific biochemical sensors. For this purpose, sorting and aligning of semiconducting SWNTs between the electrodes is advantageous. In this work, a silicon shadow mask fabricated using conventional semiconductor processes and silicon bulk micromachining was used to make metal contacts over SWNTs with a minimum feature of 1 mu m gap between the electrodes. The developed silicon shadow mask-based metal contact patterning process is cost-effective and free from photoresist (PR) chemical coatings and thermal processing. After a detailed investigation, sodium dodecyl sulfate (SDS), an anionic surfactant, along with ultrasonication process, was found to be effective for the removal of unclamped and metallic SWNTs, resulting in aligned and clamped semiconducting SWNTs between the electrodes. The presence of aligned semiconducting SWNTs was confirmed using atomic force microscopy (AFM), field emission scanning electron microscopy (FESEM), and Raman spectroscopy techniques. The fabricated devices were tested for nitrogen dioxide (NO2) gas sensing as a test case. The sensitivity enhancement of similar to 21 to 76% in the 20-80 ppm NO2 concentration range has been observed in the case of aligned semiconducting SWNT devices compared to the random network SWNT-based sensors.

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