Abstract: To counter global warming, a transition to a low-carbon economy is needed. The greenhouse sector can contribute by installing Combined Heat and Power (CHP) systems, known for their excellent energy efficiency. Due to the recent European liberalization of the energy market, glass horticulturists have the opportunity to sell excess electricity to the market and by tailored policy and support measures, regional governments can fill the lack of technical and economic knowledge, causing initial resistance. This research investigates the economic and environmental opportunities using two detailed cases applying a self managed cogeneration system. The Net Present Value is calculated to investigate the economic feasibility. The Primary Energy Saving, the CO2 Emission Reduction indicator and an Emission Balance are applied to quantify the environmental impact. The results demonstrate that a self-managed CHP system is economic viable and that CO2 emissions are reduced. (C) 2010 Elsevier Ltd. All rights reserved.

Abstract: The main drawbacks of silicon as the most promising anode material for lithium-ion batteries (theoretical capacity=3572 mAh g−1) are lithiation-induced volume changes and the continuous formation of a solidelectrolyte interphase (SEI) upon cycling. A recent strategy is to focus on the influence of coatings and composite materials. To this end, the evolution of the SEI, as well as an applied carbon coating, on nanosilicon electrodes during the first electrochemical cycles is monitored. Two specific techniques are combined: Transmission Electron Microscopy (TEM) is used to study the surface evolution of the nanoparticles on a very local scale, whereas electrochemical impedance spectroscopy (EIS) provides information on the electrode level. A TEMEELS fingerprint signal of carbonate structures from the SEI is discovered, which can be used to differentiate between the SEI and a graphitic carbon matrix. Furthermore, the shielding effect of the carbon coating and the thickness evolution of the SEI are described.

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: 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.