Abstract: Highly monodispersed platinum-based nanoalloys are the best-known catalysts for the oxygen reduction reaction. Although certainly promising, the durability and stability are among the main requirements for commercializing fuel cell electrocatalysts in practical applications. Herein, we synthesize highly stable, durable and catalytic active monodispersed PtPd nano-particles encapsulated in a unique one particle@one cell structure by adjusting the viscosity of solvents using mesocellular foam. PtPd nanoparticles in mesocellular carbon foam exhibit an excellent electrocatalytic activity (over 4 times mass and specific activities than the commercial Pt/C catalyst). Most importantly, this nanocatalyst shows no obvious change of structure and only a 29.5% loss in electrochemically active surface area after 5000 potential sweeps between 0.6 and 1.1 V versus reversible hydrogen electrode cycles. (C) 2014 Elsevier Ltd. All rights reserved.

Abstract: We present a model that describes the effect of elastic constraint on the thermodynamics of hydrogen absorption and desorption in biphasic core-shell nanoparticles, where the core is a hydride forming metal. In particular, the change of the hydride formation enthalpy and of the equilibrium pressure for the metal/hydride transformation are described as a function of nanoparticles radius, shell thickness, and elastic properties of both core and shell. To test the model, the hydrogen sorption isotherms of Mg-MgO core-shell nanoparticles, synthesized by inert gas condensation, were measured by means of optical hydrogenography. The model's predictions are in good agreement with the experimentally determined plateau pressure of hydrogen absorption. The features that a core-shell systems should exhibit in view of practical hydrogen storage applications are discussed with reference to the model and the experimental results. Copyright (C) 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Abstract: We present a combined study of experimental and computational work for a dielectric barrier discharge (DBD) used for CH4 conversion into H2. More specifically, we investigated the influence of N2 as an impurity (150,000 ppm) and as additive gas (199%) on the CH4 conversion and H2 yield. For this purpose, a zero-dimensional chemical kinetics model is applied to study the plasma chemistry. The calculated conversions and yields for various gas mixing ratios are compared to the obtained experimental values, and good agreement is achieved. The study reveals the significance of the View the MathML source and View the MathML source metastable states for the CH4 conversion into H2, based on a kinetic analysis of the reaction chemistry.

Abstract: Energy supply is essential for the functioning and well-being of a society. Decision-makers are faced with the challenge to balance burdens and benefits of energy supply practices with the aim to achieve environmental, economic, and social sustainability. Literature exhibits a broad variety of sustainability assessment frameworks for energy supply technologies. However, there is no consensus on which aspects need to be covered for a comprehensive assessment of sustainability. While some aspects, such as environmental emission damage, receive predominant attention, there is a lack of coverage and adequate quantification for others. This led in the past to an unbalanced basis for decision-making.
Based on an analysis of literature, 12 impact categories were identified for the assessment of energy technologies. The analysis included the judgement of quantification approaches regarding their significance for describing the impact categories and their maturity resulting in the proposal of 12 concrete indicators. A framework is proposed to manage and integrate the assessment of single impact categories. The framework produces normalized and weighted output indicators to use in the form of a dashboard or alternatively a single sustainability index for informed decision-making.
Finally, the proposed sustainability assessment framework relies on life cycle, local impact, and supply chain risks assessment. It consists of both well-established assessment methods as well as suggestions for new indicators in order to allow a full assessment of all impact categories. It thereby goes beyond the isolated assessment of impacts and offers the basis for comparison of complete energy supply mixes.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.