Synechocystisgathered momentum in modelling studies and biotechnological applications owing to multiple factors like fast growth, ability to fix carbon dioxide into valuable products, and the relative ease of genetic manipulation.Synechocystisphysiology and metabolism, and consequently, the productivity ofSynechocystis-based photobioreactors (PBRs), are heavily light modulated. Here, we set up a turbidostat-controlled lab-scale cultivation system in order to study the influence of varying orange–red light intensities onSynechocystisgrowth characteristics and photosynthetic activity.Synechocystisgrowth and photosynthetic activity were found to raise as supplied light intensity increased up to 500 μmol photons m⁻²s⁻¹and to enter the photoinhibition state only at 800 μmol photons m⁻²s⁻¹. Interestingly, reverting the light to a non-photo-inhibiting intensity unveiledSynechocystisto be able to promptly recover. Furthermore, our characterization displayed a clear correlation between variations in growth rate and cell size, extending a phenomenon previously observed in other cyanobacteria. Further, we applied a modelling approach to simulate the effects produced by varying the incident light intensity on its local distribution within the PBR vessel. Our model simulations suggested that the photosynthetic activity ofSynechocystiscould be enhanced by finely regulating the intensity of the light incident on the PBR in order to prevent cells from experiencing light-induced stress and induce their exploitation of areas of different local light intensity formed in the vessel. In the latter case, the heterogeneous distribution of the local light intensity would allowSynechocystisfor an optimized usage of light.

Nowadays, sustainable and renewable energy production is a global priority. Over the past decade, several Power-to-X (PtX) technologies have been proposed to store and convert the surplus of renewable energies into chemical bonds of chemicals produced by different processes. CO₂ is a major contributor to climate change, yet it is also an undervalued source of carbon that could be recycled and represents an opportunity to generate renewable energy. In this context, PtX technologies would allow for CO₂ valorization into renewable fuels while reducing greenhouse gas (GHG) emissions. With this work we want to provide an up-to-date overview of biomethanation as a PtX technology by considering the biological aspects and the main parameters affecting its application and scalability at an industrial level. Particular attention will be paid to the concept of CO₂-streams valorization and to the integration of the process with renewable energies. Aspects related to new promising technologies such as in situ, ex situ, hybrid biomethanation and the concept of underground methanation will be discussed, also in connection with recent application cases. Furthermore, the technical and economic feasibility will be critically analyzed to highlight current options and limitations for implementing a sustainable process.

This paper presents an in-depth investigation of the biogeochemical modeling approaches applied to underground hydrogen storage. It delves into the intricate dynamics of hydrogen in the subsurface, focusing on small (pore-lab scale) and reservoir-scale models, highlighting the importance of capturing microbial, geochemical, and fluid flow dynamic interactions in porous media to simulate storage performance accurately. Small-scale models offer detailed insights into localized phenomena, such as microbial hydrogen consumption and mineral reactions, and can be verified and calibrated against laboratory data. Conversely, large-scale models are essential to assess the feasibility of a project and forecast the storage performance, but cannot be proven by real data yet. This work addresses the challenge of transitioning from fine-scale to reservoir models, integrating spatial heterogeneity and long-term dynamics while retaining biogeochemical complexity. Through the use of several simulation tools, like PHREEQC, Comsol, DuMuX, Eclipse, CMG-GEM, and others, this study explores how modeling approaches are evolving to incorporate multiphysics processes and biochemical feedback loops, which are essential for predicting hydrogen retention, flow, and potential risks. The findings highlight the strengths and limitations of current modeling techniques and suggest a workflow for exploiting at best existing modeling capabilities and developing reservoir models to support hydrogen storage appraisal and management.