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Advanced NH 3 Detection by 1D Nanostructured La:ZnO Sensors with Novel Intrinsic p-n Shifting and Ultrahigh Baseline Stability.

Ahmad AjjaqFatih BulutÖzgür OzturkSelim Acar
Published in: ACS sensors (2024)
Due to its stability, transportability, and ability to be produced using renewable energy sources, NH 3 has become an attractive option for hydrogen production and storage. Detecting NH 3 is then essential, being a toxic and flammable gas that can pose dangers if not properly monitored. ZnO chemiresistive sensors have shown great potential in real NH 3 monitoring applications; yet, research and development in this area are ongoing due to reported limitations, like baseline instabilities and sensitivity to environmental factors, including temperature, humidity, and interferent gases. Herein, we suggest an approach to obtain sensors with competitive performance based on ZnO semiconducting metal oxides. For this purpose, one-dimensional nanostructured pure and La-doped ZnO films were synthesized hydrothermally. Incorporating large rare earth ions, like La, into the bulk lattice of ZnO is challenging and can lead to surface defects that are influential in gas-sensing reactions. The sensors experienced a temperature-induced p-n shifting at about 100 °C, verified by the Hall effect and AC impedance measurements. The doped sensor showed exceptional stepwise baseline stability and outstanding performance at a relatively low operating temperature (150 °C) with a sensing response of 91 at best (@ 50 ppm NH 3 ) and recorded a tolerance to water vapor up to 70% RH. Alongside p-n shifting, the enhanced performance was discussed in correlation with La doping-triggered changes in the nanostructural and surfacial properties of the films. We validated the proposed technique by producing similar sensors and performing multiple replicates to ensure consistency and reproducibility. We also introduced the fill factor concept into the gas sensor field as a new trustworthy parameter that could improve sensor performance assessment and help rate sensors based on deviation from ideality.
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