Breath analysis has received much attention for its non-invasive monitoring of human pathological conditions. The exhaled volatile organic compounds (VOCs) from patients can be utilized as potential biomarkers of specific diseases and disorders. For instance, acetone can be the indicator of diabetes and ethanol is an important interfering gas during the breath analysis. This work focuses on the improvement of the gas sensing selectivity for ethanol and acetone, by taking advantage of the intriguing redox behavior of palladium oxide (PdO) in the temperature range between 100 and 250^oC.

Metal oxide semiconductor (MOS) gas sensors have been extensively studied because of their rapid response and high sensitivity. Furthermore, fabrication of MOS gas sensors is simple and compatible to existing semiconductor fabrication processes. In this study, sputter-deposited PdO nanoflake thin films were used as the sensing material. PdO is a p-type semiconductor with a band gap of 2.2eV and thermally stable up to 800^oC in atmosphere. In addition, PdO has a high catalytic reactivity toward reducing gases, such as CO and CH₄. For the application of gas sensing, PdO is a good sensitizer in gas sensors for sensitivity and selectivity improvement.

According to our previous works, PdO demonstrates characteristic sensing responses toward reducing gases due to its distinctive reduction and reoxidation behaviors. The characteristic sensing response may be ascribed to the reduction of PdO and subsequent reoxidation. In general, the gas sensing principle based on MOS gas sensors is described by two sensing mechanism models: ionosorption and oxygen vacancy models. The characteristic redox property of PdO can modify the gas sensing behavior predicted by the two models. Moreover, adsorption of intermediate adspecies on the PdO sensor in operation also affects the sensing response.

Conventional MOS sensors use the sensor signal, which is typically determined under a steady sensing condition, as the input variable for the data processing of gas classification. In this case, simple mathematical models, such as principle component analysis (PCA) and linear discriminant analysis (LDA), can be used to classify the input signals. However, these simple models have poor performance in selectivity improvement and thus have limited applications for MOS gas sensors.

Since the PdO gas sensor demonstrates dynamic characteristic responses toward VOC gases, we extracted characteristic response features as the input variables for the artificial neural network (ANN) method to improve the selectivity of VOC gas sensing.

Figure (a) shows the electrical sensing response of PdO exposed to a dry air gas mixture of VOCs at 250^oC. Ammonia is also included in the study because it is a strong reducing agent and an important indicator of renal diseases and asthma. At the early stage of sensing, the sensing current promptly drops upon the exposure to the gases. The sensing current then gradually rises and reaches the equilibrium. The first prompt current drop can be ascribed to reduction of preadsorbed oxygen anions and PdO by VOCs on the basis of ionosorption and oxygen vacancy models. Reducing gases can react with oxygen anions and lattice oxygen atoms, releasing negative charges to the PdO sensor and thus decreasing its conductance. The latter current rise is a result of the formation of metal Pd nano-domain, which is a surface product of the PdO reduction. Chemical characterizations indicate that ethanol, acetone and ammonia undergo dehydrogenation on the PdO surface. These reducing gases can provide a plentiful amount of hydrogen to reduce the PdO substrate, depending on their bond strength and adsorption structure on the sensor. The reduction rate of the PdO substrate greatly depends on the surface coverage of hydrogen adatoms and the sensing temperature. As a result, the PdO sensor yields distinct response profiles when it is exposed to the studied gases at different temperatures. Two characteristic features extracted from the response profiles for the ANN method are depicted in figure (b). The two features are the slope of the conductance rise trace and the ratio of conductance change at two time points. The classification result is listed in table 1, demonstrating a high classification rate.

In summary, the gas sensing behavior of PdO toward VOCs was studied. Characteristic features from the electrical response profiles were used as variables for the ANN pattern recognition algorithm to enhance the sensing selectivity of PdO toward VOCs.