Preparation and characterization of nickel oxide-based EGFET pH sensors

Fa Lin, Hsi Yen Chang, Sou Hui Hsiao, Huey Ing Chen, Wen Chau Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

NiO films based extended gate field-effect transistor (EGFET) pH sensors were fabricated and investigated in this work. Experimentally, nickel oxide (NiO) nanoparticles were first prepared via precipitation method. Subsequently, the NiO film was deposited on the FTO glass substrate by spin-coating. In the precipitation of NiO, two precipitants, NaOH and NH4OH solutions, were used. The influences of preparation conditions including precipitant, coating number, and calcination temperature on the properties of NiO films and pH sensing performances of devices were investigated. From experimental results, it was found that the NaOH-derived NiO devices exhibited superior sensing performances than the NH4OH-derived ones, due to their smaller grain size and denser packing of NiO film. It also revealed that, a highest sensitivity of 53.40 mV/pH was achieved in the pH range from 2 to 12 with a good linearity of 0.9989, which was fabricated with a precipitant of NaOH solution, a coating number of 10, and a calcination temperature of 400°C. Moreover, the device showed a negligible hysteresis effect.

Original languageEnglish
Title of host publication2015 9th International Conference on Sensing Technology, ICST 2015
PublisherIEEE Computer Society
Pages402-405
Number of pages4
ISBN (Electronic)9781479963140
DOIs
Publication statusPublished - 2016 Mar 21
Event9th International Conference on Sensing Technology, ICST 2015 - Auckland, New Zealand
Duration: 2015 Dec 82015 Dec 11

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
Volume2016-March
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073

Other

Other9th International Conference on Sensing Technology, ICST 2015
CountryNew Zealand
CityAuckland
Period15-12-0815-12-11

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering

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