Springer series in materials science, Journal Year: 2024, Volume and Issue: unknown, P. 139 - 212
Published: Dec. 30, 2024
International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 67, P. 1270 - 1294
Published: March 27, 2024
The demand for clean and sustainable energy solutions is escalating as the global population grows economies develop. Fossil fuels, which currently dominate sector, contribute to greenhouse gas emissions environmental degradation. In response these challenges, hydrogen storage technologies have emerged a promising avenue achieving sustainability. This review provides an overview of recent advancements in materials technologies, emphasizing importance efficient maximizing hydrogen's potential. highlights physical methods such compressed (reaching pressures up 70 MPa) material-based approaches utilizing metal hydrides carbon-containing substances. It also explores design considerations, computational chemistry, high-throughput screening, machine-learning techniques employed developing materials. comprehensive analysis showcases potential addressing demands, reducing emissions, driving innovation.
Language: Английский
Citations
62
Materials Today Energy, Journal Year: 2024, Volume and Issue: 41, P. 101542 - 101542
Published: Feb. 29, 2024
Language: Английский
Citations
48
Advances in Colloid and Interface Science, Journal Year: 2024, Volume and Issue: 332, P. 103271 - 103271
Published: Aug. 8, 2024
Language: Английский
Citations
44
Chemical Society Reviews, Journal Year: 2024, Volume and Issue: unknown
Published: Jan. 1, 2024
Covalent organic frameworks (COFs) have gained considerable attention due to their design possibilities as the molecular building blocks that can stack in an atomically precise spatial arrangement.
Language: Английский
Citations
26
Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125346 - 125346
Published: Jan. 17, 2025
Language: Английский
Citations
2
Journal of Membrane Science, Journal Year: 2024, Volume and Issue: 713, P. 123256 - 123256
Published: Sept. 3, 2024
Machine learning (ML) has been rapidly transforming the landscape of natural sciences and potential to revolutionize process data analysis hypothesis formulation as well expand scientific knowledge. ML particularly instrumental in advancement cheminformatics materials science, including membrane technology. In this review, we analyze current state-of-the-art membrane-related applications from perspectives. We first discuss foundations different algorithms design choices. Then, traditional deep methods, application examples literature, are reported. also importance both molecular membrane-system featurization. Moreover, follow up on discussion with science detail literature using data-driven methods property prediction fabrication. Various fields discussed, such reverse osmosis, gas separation, nanofiltration. differentiate between downstream predictive tasks generative design. Additionally, formulate best practices minimum requirements for reporting reproducible studies field membranes. This is systematic comprehensive review science.
Language: Английский
Citations
14
Energy & Fuels, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 18, 2024
Rapid urbanization and population growth have intensified global energy demand, with fossil fuel consumption aggravating air pollution climate change. Hydrogen, a clean carrier, is essential for transitioning to low-carbon economy. This study examines the color-coded classification of hydrogen production pathways, derived from both renewable non-renewable sources, their emission profiles. Additionally, it delves into critical aspects storage transportation, highlighting need robust infrastructure ensure effective integration system. The concludes that traditional methods, such as coal gasification steam methane reforming (SMR), significantly contribute due reliance on fuels lack carbon capture. While blue hydrogen, utilizing capture (CCS), offers reduction in greenhouse gas (GHG) emissions, turquoise green produced via pyrolysis water electrolysis, respectively, present cleaner alternatives zero GHG emissions. With regard storage, metal complex hydrides emerge cost-effective options, while compressed suitable large-scale storage. For applications demanding high density, liquefied cryo-compressed are viable, despite associated costs complexities. pressurized tanks, cryogenic liquid tankers, pipelines considered. Pipelines favored long-distance transportation cost-effectiveness, tankers preferred short distances, higher requirements.
Language: Английский
Citations
12
Separation and Purification Technology, Journal Year: 2025, Volume and Issue: unknown, P. 132203 - 132203
Published: Feb. 1, 2025
Language: Английский
Citations
1
Energy Reviews, Journal Year: 2024, Volume and Issue: 4(1), P. 100106 - 100106
Published: Aug. 10, 2024
Energy drives the development of human civilization, and hydrogen energy is an inevitable choice under goal "global transition". As technology continues to advance, solid-state storage materials have attracted significant attention as efficient solution for storage. However, existing research methods, such experimental preparation theoretical calculations, are inefficient costly. Here, we summarize latest advancements high-throughput screening (HTS) machine learning (ML) materials. It elaborates on advantages HTS ML in rapid material screening, performance assessment prediction, so on. We place particular emphasis exploration analysis progress involving application various types Additionally, discuss integrating ML, emphasizing this comprehensive strategy field In realm storage, artificial intelligence plays a dual role. not only enhances efficiency but also offers novel tools future design development. This will aid discovery new-type high-performance materials, facilitate their commercialization practical application.
Language: Английский
Citations
6
Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3591 - 3591
Published: July 22, 2024
Hydrogen storage materials play a pivotal role in the development of sustainable hydrogen economy. However, discovery and optimization high-performance remain significant challenge due to complex interplay structural, thermodynamic kinetic factors. Computational science has emerged as powerful tool accelerate design novel by providing atomic-level insights into mechanisms guiding experimental efforts. In this comprehensive review, we discuss recent advances crystal structure prediction performance assessment from computational perspective. We highlight applications state-of-the-art methods, including density functional theory (DFT), molecular dynamics (MD) simulations, machine learning (ML) techniques, screening, evaluating, optimizing materials. Special emphasis is placed on stable structures, properties, high-throughput screening material space. Furthermore, importance multiscale modeling approaches that bridge different length time scales, holistic understanding processes. The synergistic integration studies also highlighted, with focus validation collaborative discovery. Finally, present an outlook future directions computationally driven for applications, discussing challenges, opportunities, strategies accelerating This review aims provide up-to-date account field, stimulating further research efforts leverage methods unlock full potential
Language: Английский
Citations
5