Gaydaa AlZohbi

The transition to a green hydrogen economy is impeded by two computational and physical challenges: huge overpotential losses in water electrolysis and thermoacoustic/flashback instabilities when hydrogen combusts directly. The use of conventional high-throughput screening of electrocatalysts relying on Density Functional Theory is computationally infeasible in large compositional spaces, while H flame direct numerical simulation suffers from extreme multi-scale turbulence-chemistry interactions. In this chapter, we offer a combined machine learning method, integrating material-level electrocatalyst discovery with system-level combustion control. Firstly, we test ensemble tree models and Graph Neural Networks for forecasting adsorption free energies at transition metal hydroxides, alloys and high-entropy surfaces. Then we describe reduced-order surrogate models, like CNN-LSTMs, Autoencoders and PINNs to combust hydrogen-fueled internal combustion engines and gas turbines for prediction of flashback, identification of thermoacoustic modes, and reduction of NOx emissions in real time.

Green hydrogen (H₂) production is a pivotal element of Saudi Arabia’s Vision 2030. This study presents a comprehensive feasibility assessment of a high-efficiency H₂ production system integrating a photovoltaic (PV) array and a high-concentration photovoltaic (HCPV) system with a Proton Exchange Membrane (PEM) electrolyzer, specifically designed for harsh climatic conditions. To evaluate the system’s viability, a comprehensive simulation model was developed based on rigorously validated mathematical formulations. First, the performance of the PEM electrolyzer and the effectiveness of the cooling system were modeled using established electrochemical and thermal correlations derived from the peer-reviewed literature. Second, this model was applied to evaluate the system’s long-term energy yield and economic performance. Key economic metrics, including the levelized cost of H₂ (LCOH) and discounted payback period (DPBP), were calculated based on local solar irradiance data, followed by a sensitivity analysis to assess the economic robustness under varying market conditions. The results demonstrate that the proposed configuration is not only technically feasible—maintaining high efficiency despite harsh environmental conditions—but also economically viable, offering a competitive LCOH and attractive DPBP that align with the strategic objectives of Saudi Vision 2030.

This review evaluates the role of advanced materials in optimizing the efficiency, sustainability, and market integration of solar photovoltaic (PV) technologies. Our work bridges insights from both mature (crystalline silicon (c-Si)) and novel perovskites (PSs), organic photovoltaics (OPVs), and quantum dot solar cell (QDSC) materials, thereby providing a unified view of the present and the future of PV research. We highlight the key breakthroughs for the different material classes, describing their unique features, record performance, and contribution to lowering the cost of solar energy. In particular, while some progress has been made, we recognize that challenges such as the stability of the device under varying environmental conditions, the environmental impact of the materials, and the scalability of the manufacturing processes are still there. In conclusion, we give an overview of the research topics that can pave the way for the future. We support the formation of hybrid structures, the finding of lead-free alternatives, multi-junction architectures, and integrated solutions that not only help to overcome the current limitations but also facilitate the global energy transition.

The growing environmental issues of the 21st century demand a paradigm shift in our material choices. This chapter investigates the emerging field of sustainable materials, analyzing their definition, classification, and different applications across industries. It explores the lifecycle evaluation of materials, emphasizing the significance of circular economy principles and minimized environmental impact. From bio-based polymers and recycled composites to advanced low-carbon concrete and sustainably sourced timber, the chapter features the advanced materials leading to a more sustainable future. Furthermore, it addresses the obstacles to broad implementation and highlight strategies for promoting sustainable material integration in design and manufacturing. This chapter aims to provide a comprehensive overview for researchers, practitioners, and policymakers seeking to embrace a material-conscious approach to building a resilient and sustainable world.

Electric vehicles (EVs) are generally understood as a major part of a sustainable transport system. However, they still suffer from quite a few issues, R for example, poor battery range, the requirement for a robust infrastructure, long charging times, and high purchase prices. Artificial Intelligence (AI) is very capable of solving these problems. Recent studies from 2024 to 2025 reveal that the deployment of AI in EVs, among other benefits, can lead to a reduction of maintenance costs by up to 40% and a drop in unplanned downtime by 70% through predictive analytics. Besides that, AI-enabled energy management systems have displayed the ability to lower the maximum power requirement to the grid by about 34% in case of large, scale adoption, while simultaneously optimizing battery thermal management that helps in extending battery life by almost 30%. This article is intended to shed light on the diverse ways in which AI can help accelerate the adoption of EVs. The article investigates different ways in which AI can help in maximizing energy efficiency so as to increase battery range through smart driving techniques and superior thermal management. Moreover, the article confronts the issues that hinder the use of AI in EVs such as data requirements, computational complexity, and safety precautions. In the end, it looks at the future of AI in electric cars, focusing on potential breakthroughs such as Vehicle to Grid (V2G) synchronization and its revolutionary effects on the global automotive industry.

Addressing the critical need for sustainable, high-density hydrogen (H₂) carriers to decarbonize the global energy landscape, this paper presents a comprehensive critical review of ammonia’s pivotal role in the energy transition, with a specific focus on its application in the transportation sector. While H₂ is recognized as a future fuel, its storage and distribution challenges necessitate alternative vectors. Ammonia (NH₃), with its compelling advantages including high volumetric H₂ density, established global infrastructure, and potential for near-zero greenhouse gas emissions, emerges as a leading candidate. This review uniquely synthesizes the evolving landscape of sustainable NH₃ production pathways (e.g., green NH₃ from renewable electricity) with a systematic analysis of technological advancements to investigate its direct utilization as a transportation fuel. The paper critically examines the multifaceted challenges and opportunities associated with NH₃-fueled vehicles, refueling infrastructure development, and comprehensive safety considerations, alongside their environmental and economic implications. By providing a consolidated, forward-looking perspective on this complex energy vector, this paper offers crucial insights for researchers, policymakers, and industry stakeholders, highlighting NH₃’s transformative potential to accelerate the decarbonization of hard-to-abate transportation sectors and contribute significantly to a sustainable energy future.

Hydrogen has the potential to decarbonize the energy and industrial sectors in the future, mainly if it is generated by water electrolysis. The proton-exchange membrane water electrolysis (PEMWE) system is regarded as a propitious technology to produce green hydrogen from water using power supplied by renewable energy sources. It offers many benefits, such as high performance, high proton conductibility, quick response, compact size, and low working temperature. Many conceptual and functional parameters influence the effectiveness of PEM, including temperature, pressure of anode and cathode regions, water content and wideness of the layer, and cathode and anode exchange current density. In addition, the anodic half-reaction (known as the oxygen evolution reaction (OER)) and cathodic half-reaction (known as the hydrogen evolution reaction (HER)) perform an important function in the development of PEMWE. The current study aims to present these parameters and discuss their impacts on the performance of PEM. Also, the PEM efficiency is presented. The different methods used to enhance the scattering of OER electrocatalysts and minimize catalyst loading to minimize the price of PEMWE are also highlighted. Moreover, the alternative noble metals that could be used as electrocatalysts in HER and OER to minimize the cost of PEM are reviewed and presented.

The global growth of population and the rise of industrialization in developing nations led to rise the global energy demand that has attained highest level ever. The energy demand is projected to experience more increase in the coming decades, resulting in rising the carbon emissions and creating a global tension on the way of dealing with this growth. Renewable energy is one of the keys potentials to cope with the rise of the global energy demand. The current paper presents the current situation of the global energy sector, and the different challenges facing the integration of renewable energy in the energy system. Additionally, the paper highlights the different solutions that could facilitate the integration of renewable energy and enhance the benefits offered by renewable energy resources.