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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.

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.

Milk yield and its composition show individual variation due to the effects of the environment. Previous studies suggest that meteorological variables exert negative effects on milk yield and composition, especially during summer. This study aimed to examine the effects of meteorological variables on bulk milk composition in the Sardinian sheep production system. In this work, a total of 218,170 records belonging to 4562 dairy sheep farms were merged with the meteorological data provided by 60 meteorological stations located on Sardinia Island (Italy). Milk composition in the late spring and summer recorded during a 5-year period was used to evaluate the impact of climate exposure on bulk milk traits. The milk quality was analyzed using a linear mixed model that included the fixed effects of the year of sampling, the flock size, the temperature humidity index (THI) and the random effect of the flock. The variability of milk composition explained by flock and management ranged from 30 to 64%. The flock size exerted a significant effect on milk composition: large flocks characterized by advanced management and feeding techniques resulted in higher milk quality (e.g., higher protein and fat, lower lactose) compared to traditionally managed small flocks. The impact of THI on milk composition was statistically significant across different milk quality traits (p < 0.001); the effect of thermal stress varied according to the month of lactation. For instance, milk fat content in May increased by +0.4% for THI > 76. In June, no relevant differences were observed, whereas a decrease in fat percentage was observed in July as THI values increased (up to −0.5% for THI > 76). While somatic cell counts remained relatively stable across different conditions, total bacterial count showed greater seasonal variability, peaking during warmer periods. In addition, using factor analysis, we developed a multivariate meteorological index (MMI), which explained 51% of the variance of the original meteorological data. MMI was highly correlated with THI (r = 0.75). The same linear mixed model applied for modeling THI was used to assess the effect of MMI on milk traits. Fat, protein fractions and lactose showed significant variation across MMI classes (p-value < 0.001) in the same direction as those based on THI. Overall, our findings underscore the impact of both flock size and environmental conditions on milk quality, with heat stress and traditional versus modern management practices leading to measurable differences in milk traits.