Insights on a methanation catalyst aging process: Aging characterization and kinetic study

Power to gas systems is one of the most interesting long-term energy storage solutions. As a result of the high exothermicity of the CO2 methanation reaction, the catalyst in the methanation subsystem is subjected to thermal stress. Therefore, the performance of a commercial Ni/Al2O3 catalyst was investigated over a series of 100 hour-long tests and in-process relevant conditions, i.e. 5 bar from 270 to 500 °C. Different characterization techniques were employed to determine the mechanism of the observed performance loss (N2 physisorption, XRD, TPO). The TPO analysis excluded carbon deposition as a possible cause of catalyst aging. The BET analysis evidenced a severe reduction in the total surface area for the catalyst samples tested at higher temperatures. Furthermore, a direct correlation was found between the catalyst activity decline and the drop of the catalyst specific surface. In order to correctly design a reliable methanation reactor, it is essential to have a kinetic model that includes also the aging kinetics. For this purpose, the second set of experiments was carried out, in order to determine the intrinsic kinetics of the catalyst. The kinetic parameters were identified by using nonlinear regression analysis. Finally, a power-law aging model was proposed to consider the performance loss in time

Point Defects in Two-Dimensional Indium Selenide as Tunable Single-Photon Sources

In the past few years remarkable interest has been kindled by the development of nonclassical light sources and, in particular, of single-photon emitters (SPE), which represent fundamental building blocks for optical quantum technology. In this Letter, we analyze the stability and electronic properties of an InSe monolayer with point defects with the aim of demonstrating its applicability as an SPE. The presence of deep defect states within the InSe band gap is verified when considering substitutional defects with atoms belonging to group IV, V, and VI. In particular, the optical properties of Ge as substitution impurity of Se predicted by solving the Bethe-Salpeter equation on top of the GW corrected electronic states show that transitions between the valence band maximum and the defect state are responsible for the absorption and spontaneous emission processes, so that the latter results in a strongly peaked spectrum in the near-infrared. These properties, together with a high localization of the involved electronic states, appear encouraging in the quest for novel SPE materials

Electron trapping in amorphous Al2O3

The electron trapping in MOS capacitors with amorphous Al2O3 as an insulating layer was studied through pulsed capacitance-voltage technique. A positive shift of the voltage value corresponding to a constant capacitance (VC) was observed. The dependences of the voltage instability with the applied bias and the charging time were investigated. Two different contributions could be distinguished: a hysteresis phenomenon observed on each measurement cycle, and a permanent accumulated VC-shift to which each measurement cycle contributes. A physical model based on tunneling transitions between the substrate and defects within the oxide was implemented. From the fitting procedure within the energy range covered in our measurements (1.7-2.7 eV below the conduction band edge), the trap density was found to decrease exponentially with trap energy depth from 3.0 × 1020 cm-3eV-1 to 9.6 × 1018 cm-3eV-1, with a uniform spatial distribution within the first 2 nm from the semiconductor interface for the hysteresis traps.Fil: Sambuco Salomone, Lucas Ignacio. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física. Laboratorio de Física de Dispositivos Microelectrónica; ArgentinaFil: Campabadal, F.. Instituto de Microelectronica de Barcelona; EspañaFil: Faigon, Adrián Néstor. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física. Laboratorio de Física de Dispositivos Microelectrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long". Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long"; Argentin

Impact of Power-to-Gas on distribution systems with large renewable energy penetration

The exploitation of the Power-to-Gas (PtG) technology can properly support the distribution system operation in case of large penetration of Renewable Energy Sources (RES). This paper addresses the impact of the PtG operation on the electrical distribution systems. A novel model of the PtG plant has been created to be representative of the entire process chain, as well as to be compatible with network calculations. The structure of the model with the corresponding parameters has been defined and validated on the basis of measurements gathered on a real plant. The PtG impact on the distribution systems has then been simulated on two network models representing a rural and a semi-urban environment, respectively. The testing has been carried out by defining a set of cases that contain critical situations for the distribution network, caused by RES plant placement. The objectives of the introduction of PtG are the reduction of the reverse power flow, as well as the reduction of the overcurrent and overvoltage issues in the distribution system. The results obtained from annual simulations lead to considerable reduction (from 78 to 100%) of the reverse power flow with respect to the base case, and to alleviating (or even solving) the overcurrent and overvoltage problems of the networks. These results indicate PtG as a possible solution for guaranteeing a smooth transition towards decarbonized energy systems. The capacity factors of the PtG plants largely vary depending on the network topology, the RES penetration, the number of the PtG plants and their sizes. From the test cases, the performance in a rural network (where the minimum capacity factor is about 50%) resulted better than in a semi-urban network (where the capacity factor values range between 21% and 60%)

Untargeted metabolomic profile for the detection of prostate carcinoma-preliminary results from PARAFAC2 and PLS-DA Models

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares–discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach

Efficient mapping of CO adsorption on Cu1−xMx bimetallic alloys via machine learning

The electrochemical reduction of CO2 (CO2RR) has the potential to allay the greenhouse gas effect while also addressing global energy challenges by producing value-added fuels and chemicals (mostly C-2 molecules such as ethylene and ethanol). However, due to the complicated chemical pathways involved, achieving high selectivity and efficiency towards specific reduction products remains challenging. In fact, the design of more selective and efficient catalysts often relies on trial-and-error approaches, which are very time consuming and resource intensive. In response, driven by the inherent importance of CO adsorption energy in the conversion of CO2 into C2+ hydrocarbons and alcohols, we propose a two-step approach employing machine learning classification and regression algorithms to predict CO binding energies on CuM(111)/(100) (M = Al, Ti, V, Fe, Co, Ni, Zn, Nb, Mo, Ru, Pd, Ag, Cd, Sn, Sb, Hf, W, Ir, Pt, Au) bimetallic surfaces. Firstly, we assess the stability of each adsorption site by utilizing classification algorithms. Subsequently, focusing exclusively on the stable sites, we employ regression models to predict the adsorption energies of CO. Remarkably, by employing a Gradient Boosting Classifier for classification, together with a Gradient Boosting Regressor for regression, we predict CO binding energies with a high level of robustness and accuracy for Cu bimetallic alloys with up to 17% surface impurity concentrations. The accuracy of our models is demonstrated by F1 scores exceeding 96% and a mean square error below 0.05 eV(2) for the classification and regression parts, respectively. These remarkable results highlight the adaptability of our approach and its capability for efficiently screening Cu-based CO2RR electrocatalysts, enabling rapid evaluation of promising candidates for future in-depth explorations