The excessive reliance on fossil fuels has resulted in an energy crisis, environmental pollution, and health problems, calling for alternative fuels such as biodiesel. Here, we review computational chemistry and machine learning for optimizing biodiesel production from waste. This article presents computational and machine learning techniques, biodiesel characteristics, transesterification, waste materials, and policies encouraging biodiesel production from waste. Computational techniques are applied to catalyst design and deactivation, reaction and reactor optimization, stability assessment, waste feedstock analysis, process scale-up, reaction mechanims, and molecular dynamics simulation. Waste feedstock comprise cooking oil, animal fat, vegetable oil, algae, fish waste, municipal solid waste and sewage sludge. Waste cooking oil represents about 10% of global biodiesel production, and restaurants alone produce over 1,000,000 m³ of waste vegetable oil annual. Microalgae produces 250 times more oil per acre than soybeans and 7–31 times more oil than palm oil. Transesterification of food waste lipids can produce biodiesel with a 100% yield. Sewage sludge represents a significant biomass waste that can contribute to renewable energy production.

In the pursuit of sustainable energy solutions, hydrogen emerges as a promising candidate for decarbonization. The United States has the potential to sell wind energy at a record‐low price of 2.5 cents/kWh, making hydrogen production electricity up to four times cheaper than natural gas. Hydrogen's appeal stems from its highly exothermic reaction with oxygen, producing only water as a byproduct. With an energy content equivalent to 2.4 kg of methane or 2.8 kg of gasoline per kilogram, hydrogen boasts a superior energy‐to‐weight ratio compared to fossil fuels. However, its energy‐to‐volume ratio, exemplified by liquid hydrogen's 8.5 MJ.L⁻¹ versus gasoline's 32.6 MJ.L⁻¹, presents a challenge, requiring a larger volume for equivalent energy. In addition, this review employs life cycle assessment (LCA) to evaluate hydrogen's full life cycle, including production, storage, and utilization. Through an examination of LCA methodologies and principles, the review underscores its importance in measuring hydrogen's environmental sustainability and energy consumption. Key findings reveal diverse hydrogen production pathways, such as blue, green, and purple hydrogen, offering a nuanced understanding of their life cycle inventories. The impact assessment of hydrogen production is explored, supported by case studies illustrating environmental implications. Comparative LCA analysis across different pathways provides crucial insights for decision‐making, shaping environmental and sustainability considerations. Ultimately, the review emphasizes LCA's pivotal role in guiding the hydrogen economy toward a low‐carbon future, positioning hydrogen as a versatile energy carrier with significant potential.This article is categorized under: Emerging Technologies > Hydrogen and Fuel Cells

Membrane filtration is a major process used in the energy, gas separation, and water treatment sectors, yet the efficiency of current membranes is limited. Here, we review the use of machine learning to improve membrane efficiency, with emphasis on reverse osmosis, nanofiltration, pervaporation, removal of pollutants, pathogens and nutrients, gas separation of carbon dioxide, oxygen and hydrogen, fuel cells, biodiesel, and biogas purification. We found that the use of machine learning brings substantial improvements in performance and efficiency, leading to specialized membranes with remarkable potential for various applications. This integration offers versatile solutions crucial for addressing global challenges in sustainable development and advancing environmental goals. Membrane gas separation techniques improve carbon capture and purification of industrial gases, aiding in the reduction of carbon dioxide emissions.

Pollution and the rising energy demand are two major society issues, requiring urgent solutions. Here, we review the simultaneous production of hydrogen and removal of pollutants, with a focus on mechanisms, synthesis of photocatalysts, and applications. Remarkably, this method utilizes organic pollutants as sacrificial agents for photocatalytic hydrogen production, thus both reducing pollution and producing energy. Metal-doped, cocatalyst-loaded, organic and inorganic composite, heterostructure and heterojunction photocatalysts are described. We present applications to the removal of antibiotics and organic dyes. Pollutant degradation rates range from 5.3 to 100%, with hydrogen evolution of 13.7–2724.89 μmol·g^–1h^–1.

Hydrogen is viewed as the future carbon–neutral fuel, yet hydrogen storage is a key issue for developing the hydrogen economy because current storage techniques are expensive and potentially unsafe due to pressures reaching up to 700 bar. As a consequence, research has recently designed advanced hydrogen sorbents, such as metal–organic frameworks, covalent organic frameworks, porous carbon-based adsorbents, zeolite, and advanced composites, for safer hydrogen storage. Here, we review hydrogen storage with a focus on hydrogen sources and production, advanced sorbents, and machine learning. Carbon-based sorbents include graphene, fullerene, carbon nanotubes and activated carbon. We observed that storage capacities reach up to 10 wt.% for metal–organic frameworks, 6 wt.% for covalent organic frameworks, and 3–5 wt.% for porous carbon-based adsorbents. High-entropy alloys and advanced composites exhibit improved stability and hydrogen uptake. Machine learning has allowed predicting efficient storage materials.