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Data · dataset · 2026

Macroscopic structures based on La0.8Me0.2NiO3±δ (Me = Al, Ba and Ca) perovskites for renewable hydrogen production through thermochemical water splitting

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The worrying energy and climate situations make it necessary to face a transition to a carbon-neutral economy.

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In this context, green hydrogen plays a crucial role in the future energy landscape. Besides other technologies, thermochemical water splitting represents a promising route for renewable hydrogen generation, using thermosolar energy as the primary energy source.

In this study, different types of perovskites La0.8Me0.2NiO3±δ (Me = Al, Ba and Ca) were synthesised via reactive grinding. Their redox performance was evaluated under different thermal reduction temperatures (1200-800 °C), obtaining materials with hydrogen production values ranging from 4.51 to 5.31 cm3STP/gmaterial·cycle when the reduction was performed at 800 °C, exceeding those already reported values for similar materials at higher temperatures.

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In order to obtain suitable configurations for their implementation in solar reactors, powdered perovskites were shaped into macrostructures such as pellets, reticulated porous ceramic (RPC) structures and thin films deposited over ceramic monoliths. Compared to powdered materials, the macrostructures exhibited higher hydrogen production attributed to enhanced gas-solid contact and more efficient heat transfer within the structures.

The best performance was obtained by La0.8Ca0.2NiO3±δ supported as a thin layer over the ceramic monolithic structure, with productions up to 14.91–16.33 cm3STP/gmaterial·cycle using thermal reduction temperatures of 800 and 1000 °C, respectively. These results confirm that shaping strategies enhance the already remarkable redox activity of perovskites and enable their integration into volumetric solar reactors. This represents a significant step forward in the development of scalable green hydrogen production systems based on renewable solar thermal energy.

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Ingeniería · Química
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Climate change science 68%
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e-cienciaDatosdoi:10.21950/JV7VLK13 d agoJSON v1
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