4.4 Article

Reduction of 1/f Noise in Single-Walled Carbon Nanotubes (SWCNTs) Using Gas Adsorption Technique

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ADSORPTION SCIENCE & TECHNOLOGY
卷 2022, 期 -, 页码 -

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SAGE PUBLICATIONS INC
DOI: 10.1155/2022/3244702

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Single-walled carbon nanotubes (SWCNT) play a significant role in electromagnetic absorption and shielding, but are limited by 1/f noise. This research proposes a method to reduce the noise amplitude by treating SWCNT with nitric acid, which increases the resistance and decreases the excess carrier flow.
Single-walled carbon nanotube (SWCNT) plays a major role in electromagnetic absorption and shielding. Their applications as semiconductors make a breakthrough in communication by miniaturizing the communication devices. The main drawback of the SWCNT is found to 1/f noise. Because of this limitation, high attenuation at the low-frequency band cannot be achieved, limiting its application in terms of selectivity. The spectral density study shows that the noise's amplitude is directly proportional to the temperature and inversely proportional to the number of carriers in the nanotube. The SWCNT is mainly synthesized using hydrocarbons which contains carbonaceous impurities. On the removal of impurities, more surface oxygen functional groups are formed. On the other hand, the diameter of the carbon nanotube is very small, increasing the resistance of carrier flow. In this research work, gas adsorption was used in SWCNT by treating the carbon nanotube using nitric acid. Isotherms determine porous size. The adsorbate-adsorbent interaction on carbon nanotube reduces the microporosity in the surface by treating with nitric acid. Therefore, the density of the surface increases and the CNT bundle separation will be reduced, increasing the carbon nanotube's resistivity. This increase in resistivity reduces the excess carrier flow; therefore, the temperature will reduce the 1/f noise. The proposed system is cost-effective and has shown 11% improvement by reducing the noise amplitude by increasing carbon nanotube resistance. This proposed method has less complexity compared with existing models.

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