Abbas Zeinijahromi

Microbial processes are increasingly recognized as critical modifiers of solid–fluid interfaces in subsurface systems; however, their role in hydrogen–mineral interfacial chemistry remains poorly constrained. Here, we demonstrate that microbial colonization fundamentally reprograms the interfacial behavior of iron-bearing minerals, overriding intrinsic pressure–temperature controls. Using pyrite (FeS2) and hematite (Fe2O3) as representative substrates, we integrated high-pressure/high-temperature experiments (1–12 MPa, 25–50 °C) with multiscale surface and geochemical analyses, including contact-angle measurements, XPS, ICP–OES, SEM–EDS, and AFM. Abiotic systems exhibit pressure-enhanced hydrophobicity and temperature-driven hydrophilicity; however, microbial colonization overrode both effects, driving the minerals toward strongly hydrophilic states. These biological transformations are governed by coupled biogeochemical mechanisms, including Fe–S redox transformations, ion redistribution, mineral dissolution, extracellular polymeric substance (EPS) adsorption, and nanoscale surface roughening associated with biofilm formation. Collectively, these processes alter surface chemistry and topography, producing emergent wettability states that cannot be predicted from abiotic conditions alone. Our findings establish microbial activity as a dominant control on hydrogen–mineral interfacial behavior, with direct implications for hydrogen storage efficiency, reactive transport, and the design of sustainable subsurface energy systems.

The growing impacts of global warming demand urgent climate-change mitigation strategies, with carbon storage in saline aquifers emerging as a promising solution. These aquifers, for their high porosity and permeability, offer significant potential for CO₂ sequestration. Among the trapping mechanisms, solubility trapping—where CO₂ dissolves into brine—stands out for its long-term effectiveness. However, CO₂ dissolution alters brine density, initiating density-driven convection that enhances CO₂ migration. Accurate modelling of these density changes is essential for optimising CO₂ storage strategies and improving long-term sequestration outcomes. This study presents a two-step explainable artificial intelligence (XAI) framework for predicting the density of CO₂-dissolved brine in geological formations. A dataset comprising 3393 samples from 14 different studies was utilised, capturing a wide range of brine compositions and salinities. Given the complexity of brine–CO₂ interactions, a two-step modelling approach was adopted. First, a random forest (RF) model predicted the brine volume (as the proxy for the density) without dissolved CO₂, and then, a second RF model predicted the impact of CO₂ dissolution on the brine’s volume. Feature importance analysis and SHapley Additive exPlanations (SHAP) values provided interpretability, revealing the dominant role of temperature and ion mass in the absence of CO₂ and the significant influence of dissolved CO₂ in more complex systems. The model showed excellent predictive performance, with R² values of 0.997 and 0.926 for brine-only and CO₂-dissolved solutions, respectively. Future studies are recommended to expand the dataset, explore more complex systems, and investigate alternative modelling techniques to further enhance the predictive capabilities.

As global population growth and urbanisation intensify energy demands, the quest for sustainable energy sources gains paramount importance. Hydrogen (H₂) emerges as a versatile energy carrier, contributing to diverse processes in energy systems, industrial applications, and scientific research. To harness the H₂ potential effectively, a profound grasp of its thermodynamic properties across varied conditions is essential. While field and laboratory measurements offer accuracy, they are resource-intensive. Experimentation involving high-pressure and high-temperature conditions poses risks, rendering precise H₂ solubility determination crucial. This study evaluates the application of Deep Neural Networks (DNNs) for predicting H₂ solubility in n-alkanes. Three DNNs are developed, focusing on model structure and overfitting mitigation. The investigation utilises a comprehensive dataset, employing distinct model structures. Our study successfully demonstrates that the incorporation of dropout layers and batch normalisation within DNNs significantly mitigates overfitting, resulting in robust and accurate predictions of H₂ solubility in n-alkanes. The DNN models developed not only perform comparably to traditional ensemble methods but also offer greater stability across varying training conditions. These advancements are crucial for the safe and efficient design of H₂-based systems, contributing directly to cleaner energy technologies. Understanding H₂ solubility in hydrocarbons can enhance the efficiency of H₂ storage and transportation, facilitating its integration into existing energy systems. This advancement supports the development of cleaner fuels and improves the overall sustainability of energy production, ultimately contributing to a reduction in reliance on fossil fuels and minimising the environmental impact of energy generation.

Underground CO₂ storage is crucial for sustainability as it reduces greenhouse gas (GHG) emissions, helping mitigate climate change and protect the environment. This research explores the use of Explainable Artificial Intelligence (XAI) to enhance the predictive modelling of CO₂ solubility in brine solutions. Employing Random Forest (RF) models, the study integrates Shapley Additive exPlanations (SHAP) analysis to uncover the complex relationships between key variables, including pressure (P), temperature (T), salinity, and ionic composition. Our findings indicate that while P and T are primary factors, the contributions of salinity and specific ions, notably chloride ions (Cl⁻), are essential for accurate predictions. The RF model exhibited high accuracy, precision, and stability, effectively predicting CO₂ solubility even for brines not included during the model training as evidenced by R² values greater than 0.96 for the validation and testing samples. Additionally, the stability assessment showed that the Root Mean Squared Error (RMSE) spans between 8.4 and 9.0 for 100 different randomness, which shows good stability. SHAP analysis provided valuable insights into feature contributions and interactions, revealing complex dependencies, particularly between P and ionic strength. These insights offer practical guidelines for optimising CO₂ storage and mitigating associated risks. By improving the accuracy and transparency of CO₂ solubility predictions, this research supports more effective and sustainable CO₂ storage strategies, contributing to the overall goal of reducing greenhouse gas emissions and combating climate change.

The success of CO₂ storage projects largely depends on addressing formation damage, such as salt precipitation, hydrate formation, and fines migration. While analytical models for reservoir behaviour during CO₂ storage in aquifers and depleted gas fields are widely available, models addressing formation damage and injectivity decline are scarce. This work aims to develop an analytical model for CO₂ injection in a layer-cake reservoir, considering permeability damage. We extend Dietz’s model for gravity-dominant flows by incorporating an abrupt permeability decrease upon the gas-water interface arrival in each layer. The exact Buckley-Leverett solution of the averaged quasi-2D (x, z) problem provides explicit formulae for sweep efficiency, well impedance, and skin factor of the injection well. Our findings reveal that despite the induced permeability decline and subsequent well impedance increase, reservoir sweep efficiency improves, enhancing storage capacity by involving a larger rock volume in CO₂ sequestration. The formation damage factor d, representing the ratio between damaged and initial permeabilities, varies from 0.016 in highly damaged rock to 1 in undamaged rock, resulting in a sweep efficiency enhancement from 1–3% to 50–53%. The developed analytical model was applied to predict CO₂ injection into a depleted gas field.