Visible light enhanced black NiO sensors for ppb-level NO2 detection at room temperature
Source abstract
Abstract
Abstract Although extensive studies have been carried out on n-type semiconductors for room-temperature gas sensor applications, some intrinsic problems remain. Therefore, other interesting attempts should be adopted to solve these issues, like p-type semiconductors. Previous studies have demonstrated that p-type semiconductor gas sensors exhibit better selectivity and less humidity dependence due to the distinctive oxygen adsorption and surface reactivity. Visible light is used as the external activation source to accelerate the sensing kinetics instead of heating. Stoichiometric NiO cannot absorb visible lights. Inspired by the works of black TiO2, we adopted three methods to prepare black NiO. XPS characterizations reveal that the presence of Ni3+ ions leads to the formation of black NiO. However, not all black NiO samples show good responses to NO2 at room temperature. Three main routes: synthesizing specific morphology with large specific surface area and porosity, introduction of Ni3+ ions and oxygen vacancies, are needed to get the enhanced sensing performance. The black NiO samples with large specific surface area and oxygen vacancies and Ni3+ ions show obvious response towards ppb-level NO2 with visible light irradiation at room temperature. Furthermore, light wavelength is found to play a vital role in the sensing characteristics, and blue light is the optimal choice. Different from traditional NiO sensors operated at high temperatures exhibiting superior response to reducing gases, the black NiO show excellent selectivity towards oxidizing gas, ppb-level NO2, at room temperature illuminated by blue light. In contrast with n-type semiconductors, the black NiO samples also exhibit less humidity dependence.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Transition Metal Oxide Nanomaterials · ZnO doping and properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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