Italy

This study evaluates the performance of three anion-exchange membranes (FAS-50 AMX Fujifilm-AEM) and a diaphragm separator (Zirfon® UTP 500) in alkaline water sono-electrolysis using a 25 % KOH electrolyte at ambient temperature. Energy efficiency hydrogen production kinetics and membrane stability were assessed experimentally and through modeling. Among the tested separators Zirfon achieved the highest energy efficiency outperforming AEM AMX and FAS-50. Hydrogen production rates under silent conditions ranged from 2.55 µg/s (AEM) to 2.92 µg/s (FAS-50) while sonication (40 kHz 60 W) increased rates by 0.03–0.12 µg/s with the strongest relative effect observed for FAS-50 (≈4.0 % increase). By contrast Zirfon and AEM showed slight efficiency reductions (0.5–2 %) under ultrasound due to their higher structural resistance. Ion-exchange capacity tests confirmed significant degradation of polymeric membranes (IEC losses of 60–90 %) while Zirfon maintained stability in 25 % KOH. Modeling results showed that the diaphragm resistance was dominated by the ohmic losses (55–86 %) with ultrasound reducing bubble coverage and associated resistance only marginally (<0.02 V). Overall Zirfon demonstrated superior stability and efficiency for long-term operation while ultrasound primarily enhanced hydrogen evolution kinetics in mechanically weaker polymeric membranes.

Separation of H2 from CO2 is crucial in industry since they are the products of water gas shift reaction. In addition the demand for pure H2 as well as the potential reuse of CO2 as reactant are increasing as a consequence of the transition from fossil fuels to decarbonization processes. In this scenario this work aims to propose a possible solution to get simultaneously pure H2 and CO2 meeting the world’s requirements in terms of reduction of CO2 emissions and transition to cleaner energy. A simulated plant combining Pd-based and SAPO-34 membrane modules is able to provide pure H2 with a final recovery higher than 97%. In addition the entire CO2 fed to SAPO-34 unit is recovered in the permeate stream with a concentration of 97.7%. A cost analysis shows that feed gas gives a higher contribution than compression heat exchange and membranes (e.g. 70 20 3 and 7% respectively). Net profit and net present value are positive within a specific feed gas price range (e.g. net profit up to 0.10 and 0.155 $/Nm3 depending on the labour cost set) showing that the process can be cost-effective and profitable. H2 purification cost ranges between 2.6 and 7.8 $/kg.

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options metal hydrides offer high volumetric storage density and operational safety yet their application is limited by thermodynamic kinetic and compositional constraints. In this work we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure enthalpy and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation using the PADRE algorithm on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore clustering and cross-validation analyses highlight the limited generalizability of models across different material classes though high accuracy is achieved when training and testing within a single hydride family (e.g. AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery emphasizing the importance of dataset diversity and representation for robust property prediction.

Renewable hydrogen is a promising pathway to decarbonise hard-to-electrify sectors though its widespread deployment remains hindered by economic challenges. Hydrogen valleys integrated regional systems have emerged as a strategic solution to scale up hydrogen infrastructure and demand. This study assesses the technoeconomic feasibility of a hydrogen valley in southeastern Crete based on the CRAVE-H2 project using a MixedInteger Linear Programming (MILP) optimisation model. The system serves multiple end-uses: touristic fuel cell buses and a vessel as well as cold ironing for ships at berth. In addition to renewable generators electricity can be supplied via a hybrid storage system or purchased from the grid with dispatch optimised according to hourly market prices. A customised modelling framework is developed within PyPSA using the Linopy extension enabling the inclusion of piecewise affine approximations of non-linear performance curves for electrolysers and fuel cells alongside operating range constraints. Hydrogen leakage is also explicitly modelled to assess its environmental and economic implications. The model delivers optimal component sizing energy dispatch strategies and key performance metrics including Levelised Cost Of Hydrogen (LCOH) aggregated Levelised Cost Of Energy (LCOE) and carbon intensity. Most scenarios yield competitive LCOH values between 5.36 and 8.21 €/kgH2 increasing to 15 €/kgH2 under full decarbonisation due to extensive storage investments. Hydrogen emissions that may exceed 10 % of total production in worst-case scenarios become more pronounced in fully decarbonised scenarios. These findings underline the importance of emissions tracking and provide practical insights to inform the design of cost-effective low-emission hydrogen valleys.

The growing demand for air connectivity coupled with the forecasted increase in passengers by 2040 implies an exigency in the aviation sector to adopt sustainable approaches for net zero emission by 2050. Sustainable Aviation Fuel (SAF) is currently the most promising short-term solution; however ensuring its overall sustainability depends on reducing the life cycle carbon footprints. A key challenge prevails in hydrogen usage as a reactant for the approved ASTM routes of SAF. The processing conversion and refinement of feed entailing hydrodeoxygenation (HDO) decarboxylation hydrogenation isomerisation and hydrocracking requires substantial hydrogen input. This hydrogen is sourced either in situ or ex situ with the supply chain encompassing renewables or non-renewables origins. Addressing this hydrogen usage and recognising the emission implications thereof has therefore become a novel research priority. Aside from the preferred adoption of renewable water electrolysis to generate hydrogen other promising pathways encompass hydrothermal gasification biomass gasification (with or without carbon capture) and biomethane with steam methane reforming (with or without carbon capture) owing to the lower greenhouse emissions the convincing status of the technology readiness level and the lower acidification potential. Equally imperative are measures for reducing hydrogen demand in SAF pathways. Strategies involve identifying the appropriate catalyst (monometallic and bimetallic sulphide catalyst) increasing the catalyst life in the deoxygenation process deploying low-cost iso-propanol (hydrogen donor) developing the aerobic fermentation of sugar to 14 dimethyl cyclooctane with the intermediate formation of isoprene and advancing aqueous phase reforming or single-stage hydro processing. Other supportive alternatives include implementing the catalytic and co-pyrolysis of waste oil with solid feedstocks and selecting highly saturated feedstock. Thus future progress demands coordinated innovation and research endeavours to bolster the seamless integration of the cutting-edge hydrogen production processes with the SAF infrastructure. Rigorous technoeconomic and life cycle assessments alongside technological breakthroughs and biomass characterisation are indispensable for ensuring scalability and sustainability