Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications.

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression-Ordinary Least Square "MLR-OLS" and Multi Linear Regression-Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

From the Modeling of an Electrochemical YSZ-Based Gas Sensor Used in Electrolysis Mode.

Electrochemical sensors have been used for many decades. However, the modeling of such sensors used in electrolysis mode is poorly documented, especially in the case of multiple gases' parallel actions. These are of great interest since they constitute the first brick to bring information on the natures and concentrations of gaseous mixture compositions, thanks to gray box modeling of sensor arrays, for example. Based on Butler-Volmer's equations, a model assuming parallel reactions at gold cathode has been introduced in this article and confronted with experimental results. The establishment of the model is based on the extraction of three variables: the charge transfer coefficient "α", the reaction order γ, and the reaction constant rate k0. Tests performed without pollutants and with different concentrations of oxygen could be nicely fitted using the model. The influence of the polarization current on the three variables of the model has been evaluated, showing a clear influence on the constant rate and the reaction order. Moreover, increasing the polarization current enabled us to obtain selectivity for oxidant gases. Similarly, the effect of the oxygen concentration was evaluated. Results showed that, in this case, the charge transfer coefficients "α" obtained for oxidant gases are quite different from the ones obtained in the polarization current varying conditions. Therefore, the model will be interesting in situations where polarization current and oxygen content are not varied together. Variation of polarization current can be quite interesting to obtain increased information for multivariate analysis purposes in constant oxygen content situations. Additionally, other parameters have to be considered for applications in which the oxygen content is bound to change, such as exhaust gases or combustion.

Protection of NOx Sensors from Sulfur Poisoning in Glass Furnaces by the Optimization of a "SO2 Trap".

Electrochemical NOx sensors based on yttria-stabilized zirconia (YSZ) provide a reliable onboard way to control NOx emissions from glass-melting furnaces. The main limitation is the poisoning of this sensor by sulfur oxides (SOx) contained in the stream. To overcome this drawback, an "SO2 trap" with high SOx storage capacity and low affinity to NOx is required. Two CuO/BaO/SBA-15 traps with the same CuO loading (6.5 wt.%) and different BaO loadings (5 and 24.5 wt.%, respectively) were synthetized, thoroughly characterized and evaluated as SO2 traps. The results show that the 6.5%CuO/5%BaO/SBA-15 trap displays the highest SO2 adsorption capacity and can fully adsorb SO2 for a specific period of time, while additionally displaying a very low NO adsorption capacity. A suitable quantity of this material located upstream of the sensor could provide total protection of the NOx sensor against sulfur poisoning in glass-furnace exhausts.

Selective Growth of Graphene-Confined Inkjet-Printed Sn Nanoparticles on Plastic Using Intense Pulsed Light Annealing.

Printing graphene-based nanomaterials on flexible substrates has become a burgeoning platform for next-generation technologies. Combining graphene and nanoparticles to create hybrid nanomaterials has been proven to boost device performance, thanks to their complementary physical and chemical properties. However, high growth temperatures and long processing times are often required to produce high-quality graphene-based nanocomposites. For the first time, we report a novel scalable approach for additive manufacturing of Sn patterns on polymer foil and their selective conversion into nanocomposite films under atmospheric conditions. A combination of inkjet printing and intense flashlight irradiation techniques is studied. Light pulses that are selectively absorbed by the printed Sn patterns cause a temperature of over 1000 °C to be reached locally in a split second without damaging the underlying polymer foil. The top surface of the polymer foil at the interface with printed Sn becomes locally graphitized and acts as a carbon source, transforming printed Sn into Sn@graphene (Sn@G) core-shell patterns. Our results revealed a decrease in electrical sheet resistance, with an optimal value (Rs = 72 ± 2 Ω/sq) reached when light pulses with an energy density of 12.8 J/cm2 were applied. These graphene-protected Sn nanoparticle patterns exhibit excellent resistance against air oxidation for months. Finally, we demonstrate the implementation of Sn@G patterns as electrodes for Li-ion microbatteries (LIBs) and triboelectric nanogenerators (TENGs), showing remarkable performance. This work offers new insight into the development of a versatile, eco-friendly, and cost-effective technique for producing well-defined patterns of graphene-based nanomaterials directly on a flexible substrate using different light-absorbing nanoparticles and carbon sources.

Rational Development of IT-SOFC Electrodes Based on the Nanofunctionalization of La0.6Sr0.4Ga0.3Fe0.7O3 with Oxides. Part 2: Anodes by Means of Manganite Oxide.

To promote the diffusion on the market of solid oxide fuel cell (SOFC) devices, the use of fuels other than the most appealing hydrogen and also decreasing the working temperature could show the way forward. In the first part, we concentrated our efforts on cathodes; hereby, we focused on anodes and concentrated our efforts to develop a sustainable multifuel anode. We decided to develop LSGF (La0.6Sr0.4Ga0.3Fe0.7O3)-based nanocomposites by depositing manganite oxide to enhance the performance toward propane. MnOx has been deposited by a wet impregnation method, and the powders have been largely characterized by X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray analysis, X-ray photoelectron spectroscopy, hydrogen temperature-programmed reduction, oxygen temperature-programmed desorption, and N2 adsorption. Cell performances were first collected in hydrogen as a function of both the temperature and hydrogen content. EIS measurements were studied using Nyquist and Bode plots, and they show two processes at high frequency, assigned to charge transfer at the electrode/electrolyte interface, and at low frequency due to the dissociative adsorption of hydrogen. The Arrhenius plot of area specific resistance suggests two different trends, and the activation energy decreases from 117 kJ/mol at 750 °C to 46 kJ/mol above that temperature. This behavior is often connected to chemical modification of the catalyst or changes in the limiting step processes. Power densities in hydrogen and propane were determined at 744 °C after 1 h of operation, achieving 70 mW/cm2 in H2 and 67 mW/cm2 in C3H8. The open-circuit voltage increases from 1.10 V in hydrogen to 1.13 V in propane.

Responses of a Resistive Soot Sensor to Different Mono-Disperse Soot Aerosols.

Since 2011, the Euro 5b European standard limits the particle number (PN) emissions in addition to the particulate mass (PM) emissions. New thermal engine equipped vehicles also have to auto-diagnose their own particulate filter (Diesel particulate filter or gasoil particulate filter) using on-board diagnostic (OBD) sensors. Accumulative resistive soot sensors seem to be good candidates for PM measurements. The aim of this study is to bring more comprehension about soot microstructures construction in order to link the response of such a sensor to particle size and concentration. The sensor sensitivity to the particle size has been studied using successively an electrostatic and an aerodynamic classification, showing the same trend.

Preconcentration modeling for the optimization of a micro gas preconcentrator applied to environmental monitoring.

This paper presents the optimization of a micro gas preconcentrator (µ-GP) system applied to atmospheric pollution monitoring, with the help of a complete modeling of the preconcentration cycle. Two different approaches based on kinetic equations are used to illustrate the behavior of the micro gas preconcentrator for given experimental conditions. The need for high adsorption flow and heating rate and for low desorption flow and detection volume is demonstrated in this paper. Preliminary to this optimization, the preconcentration factor is discussed and a definition is proposed.