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In the quest for more efficient solar thermal systems, accurately determining the thermal emittance of low-emissive materials is crucial in determining the power losses. This paper describes the calorimetric method designed to precisely measure the thermal emittance of Selective Solar Absorbers (SSAs) to be used in High Vacuum Flat Plate Collectors (HVFPCs). The method’s capability is demonstrated through the successful correction of thermal emittance values for copper samples of varying sizes, including dimensions down to 49 cm2. Results highlight the method’s potential to significantly reduce measurement errors associated with small-size and/or low-emittance samples, providing a path forward to improve the design and efficiency of SSAs. This research marks a significant step in advancing solar thermal technology by enabling emittance measurements with a precision better than 0.003, which is essential for the development of high-performance solar thermal absorbers. The method has also been applied to correct the thermal emittance value of SSA measured in previous measurement campaigns, and it allows a better estimation of the SSA efficiency conversion curve.

Degradation issues correlated to microstructural changes are the main obstacles to solid oxide fuel cell and electrolyser applications, making their identification and understanding fundamental steps. Coupling experimental activities with modelling, this work analyses the state-of-the-art Ni-YSZ (Yttria-Stabilized Zirconia)/YSZ/CGO (Cerium Gadolinium Oxide)/LSCF (Lanthanum Strontium Cobalt Ferrite)-CGO-based cell after 1000 h of galvanostatic electrolysis operation at fixed temperature and high steam composition in the inlet gas. Following a multiscale approach, the system behaviour is characterized through electrochemical impedance spectra and polarization curves as well as studying microstructure evolution, with a focus on Ni-cermet functional layer in view of Ni instability detected as the main degradation cause. A comparison with a cell consisting of the same initial geometrical structure and materials but aged in fuel cell mode allows to highlight the influence of operating mode and parameters on Ni-YSZ microstructure. Ni particle size and phase fraction variations experimentally observed on the electrode surface are correlated to water content and applied polarization simulated local values. Ni uneven distribution at the electrolyte interface and particle coarsening, above all, lead to an increase in polarization loss under electrolysis and fuel cell mode, respectively, since both penalise the charge transfer reaction and migration.

Dataset supporting publication of manuscript_GCB-B-RA-24-138

Anion exchange membrane water electrolyser showing the chemical structure of hydroxyl-conductive 2D hBN-based anion exchange membrane (AEM). The developed AEMs exhibit high hydroxyl conductivity, superior mechanical and electrochemical stability.

The Arctic climate system is in great distress, warming faster than the rest of the world and transforming more rapidly than previously anticipated. Sustained and harmonized multidisciplinary observations at key locations are needed to fill knowledge gaps and evaluate the ongoing climate change impacts on the complex Arctic marine system. For more than a decade, the Distributed Biological Observatory (DBO) has functioned as a “detection array” for ecosystem changes and trends in the Pacific sector of the Arctic Ocean. This long-term collaborative initiative builds on active involvement of scientists conducting in situ observations within marine disciplines to systematically document how the arctic marine ecosystem is transforming with environmental change. The DBO concept is currently being expanded into other sectors of the Arctic, including Davis Strait and Baffin Bay, the Atlantic Arctic gateway area, and the East Siberian Sea. Through increased collaboration and joint practices, findings from these regional areas can leverage to pan-Arctic perspectives and improve our understanding of the entire Arctic Ocean. Common practices are now being developed, including key phenomena and relevant indicators to study. Also, we strive towards harmonized routines for sampling, analysis and data sharing to increase the comparability across both disciplines and regions, and to improve the usability of our in-situ observations also for the modelling and remote sensing scopes. An ambition is, moreover, to expand from today's predominantly open-sea coverage towards coastal regions, to the benefit of both local communities and researchers. The process of establishing a pan-Arctic DBO network is to a large part facilitated by the EU Horizon project Arctic PASSION (2022-2025). Here, we present the latest developments and shared priorities, as well as our vision of how to incorporate our efforts into other parallel processes aiming to strengthen the pan-Arctic observing system towards, during and beyond the upcoming IPY.

Published online: 26 November 2024 This study investigates the relationships between social and political trust and views on energy affordability, which are crucial for promoting sustainable energy practices. The findings indicate that social and especially political trust are negatively correlated with perceptions of energy affordability. The study also finds that the probability of being highly concerned about energy affordability is declining in the level of trust, especially in countries, as those in Southern European, that prioritize energy affordability. These findings confirm the critical role of trust in effective energy policies, fostering public support for transitions to sustainable energy systems. The study recommends that policymakers enhance transparency, accountability, and public engagement to build trust, thereby improving perceptions of energy affordability and supporting sustainable energy transitions.

Around 600 million m3 of wastewater and 6 million tonnes of leather solid wastes, are generated annually worldwide, with a chromium content of 1 to 4 %. In this context, the thermochemical valorisation of tannery sludge (TS) by hydrothermal liquefaction (HTL) process represents a promising route both for the reduction of the material to dispose in landfill and for the production of an energy carrier. HTL process produces bio-crude from wet biomasses in a hot pressurised water environment, thus avoiding the energy-intensive drying step commonly associated to other thermochemical processes. Moreover, HTL, not aiming at the complete oxidation of the organic component, potentially avoids the oxidation of Cr in its harmful hexavalent form. In this study, a TS was investigated as solid waste for HTL carried out in a 500 mL batch reactor to obtain a bio-crude for energy purposes. Results show that, under the best operating HTL condition (350 °C and 10 min), the H/C ratio of bio-crude was similar to that of starting biomass while the O/C ratio was about three times smaller than in the parent TS. The bio-crude yield was about 25–30 % on dry and ash-free basis, with an associated energy recovery of about 40–45 %. NMR analysis of bio-crude revealed that it is a complex mixture mainly constituted by aliphatic units. Moreover, ICP-MS, atomic absorption and UV–visible spectroscopy analyses proved that inorganic elements are mainly retrieved in the solid residue, and that Cr was present in its starting trivalent form.

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.