Oxygen sensing with individual ZnO:Sb Micro-wires: Effects of temperature and light exposure on the sensitivity and stability
Data files
Nov 25, 2021 version files 1.32 MB
-
Fig._3._Resistance_and_temperature_as_a_function_of_time.xlsx
19.70 KB
-
Fig._4._Variation_of_resistance_with_temperature_under_Green__Red__Blue_LED_and_no_LED_light_.xlsx
9.04 KB
-
Fig._5a._Oxygen_gas_flow_at_200_C.xlsx
110.54 KB
-
Fig._5b._Oxygen_gas_flow_at_200_C_after_6_days.xlsx
118.09 KB
-
Fig._5c_.xlsx
180.81 KB
-
Fig._6a._Oxygen_gas_flow_at_room_temp._20_C_with_Blue_LED_ON.xlsx
506.09 KB
-
Fig._6b._Oxygen_gas_flow_at_200_C_under_blue_LED_light.xlsx
175.16 KB
-
Fig.2._Resistance_as_a_function_of_time_under_Blue_LED_ON_and_OFF.xlsx
196.50 KB
-
Readme.txt
541 B
Abstract
Nanostructured ZnO has been widely investigated as a gas sensing material. Antimony is an important dopant for ZnO that catalyzes its surface reactivity and thus strengthens its gas sensing capability. However, there are little to no studies on the gas sensing of antimony-doped ZnO single wires. We fabricated and characterized ZnO/ZnO:Sb core/shell micro-wires and demonstrated that individual wires are sensitive to oxygen gas flow. Temperature and light illumination strongly affect the oxygen gas sensitivity and stability of these individual wires. It was found that these micro- and nano-wire oxygen sensors at 200˚C give the highest response to oxygen, yet a vanishingly small effect of light and temperature variations. The underlying physics and the interplay between these effects are discussed in terms of surface-adsorbed oxygen, oxygen vacancies and hydrogen doping.
Methods
All four file names correspond directly with the figure numbers.
All data was collected using Labview Software, which saved the raw data as Excel (.xlxs) files.
Columns are labeled properly, where the first column is usually time.
Other columns are either the resistance of the ZnO:Sb individual wire (in Mega-Ohms) or its temperature. The column is labeled properly in each case.
Figure insets are meant to focus the attention on some specific parts of the data, but do not have any 'new' datasets other than those used to produce the actual figures.