On-line event detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection hy thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the may. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method

Gas Sensing of NiO-SCCNT Core–Shell Heterostructures

Hierarchical core–shell (C–S) heterostructures composed of a NiO shell deposited onto stacked‐cup carbon nanotubes (SCCNTs) are synthesized by atomic layer deposition (ALD). A film of NiO particles (0.80–21.8 nm in thickness) is uniformly deposited onto the inner and outer walls of the SCCNTs. The electrical resistance of the samples is found to increase of many orders of magnitude with the increasing of the NiO thickness. The response of NiO–SCCNT sensors toward low concentrations of acetone and ethanol at 200 °C is studied. The sensing mechanism is based on the modulation of the hole‐accumulation region in the NiO shell layer upon chemisorption of the reducing gas molecules. The electrical conduction mechanism is further studied by the incorporation of an Al2O3 dielectric layer at NiO and SCCNT interfaces. The investigations on NiO–Al2O3–SCCNT, Al2O3–SCCNT, and NiO–SCCNT coaxial heterostructures reveal that the sensing mechanism is strictly related to the NiO shell layer. The remarkable performance of the NiO–SCCNT sensors toward acetone and ethanol benefits from the conformal coating by ALD, large surface area of the SCCNTs, and the optimized p‐NiO shell layer thickness followed by the radial modulation of the space‐charge region. Peer Reviewe

Conductance model for single-crystalline/compact metal oxide gas-sensing layers in the nondegenerate limit: Example of epitaxial SnO2(101)

Semiconducting metal oxide (SMOX)-based gas sensors are indispensable for safety and health applications, for example, explosive, toxic gas alarms, controls for intake into car cabins, and monitor for industrial processes. In the past, the sensor community has been studying polycrystalline materials as sensors where the porous and random microstructure of the SMOX does not allow a separation of the phenomena involved in the sensing process. This led to conduction models that can model and predict the behavior of the overall response, but they were not capable of giving fundamental information regarding the basic mechanisms taking place. The study of epitaxial layers is a definite improvement, allowing clarifying the different aspects and contributions of the sensing mechanisms. A detailed analytical model of the transduction function for n-A nd p-type single-crystalline/compact metal oxide gas sensors was developed that directly relates the conductance of the sample with changes in the surface electrostatic potential. Combined dc resistance and work function measurements were used in a compact SnO2(101) layer in operando conditions that allowed us to check the validity of our model in the region where Boltzmann approximation holds to determine the surface and bulk properties of the material.Fil: Simion, Cristian Eugen. Institut de Physique Des Matériaux, Bucarest-magurele; RumaniaFil: Schipani, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Papadogianni, Alexandra. Paul Drude Institut Fur Festkorperelektronik; AlemaniaFil: Stanoiu, Adelina. Institut de Physique Des Matériaux, Bucarest-magurele; RumaniaFil: Budde, Melanie. Paul Drude Institut Fur Festkorperelektronik; AlemaniaFil: Oprea, Alexandru. Universität Tübingen; AlemaniaFil: Weimar, Udo. Universität Tübingen; AlemaniaFil: Bierwagen, Oliver. Paul Drude Institut Fur Festkorperelektronik; AlemaniaFil: Barsan, Nicolae. Universität Tübingen; Alemani