Liquid hydrogen storage for hydrogen refuelling stations: use cases review
Bibliographic record
Abstract
Liquid hydrogen (LH₂) is emerging as a key enabler in the global clean energy transition, offering a viable pathway for decarbonizing sectors such as transportation, heavy industry, and power generation. This report provides a comprehensive overview of LH₂’s technological maturity, storage and handling systems, safety protocols, and infrastructure development. It highlights LH₂’s advantages as a high‑energy‑density fuel that supports long‑distance and large‑scale applications while addressing challenges related to cost, efficiency, and safety. Advances in cryogenic storage—originating from NASA’s research—have led to near‑zero boil‑off tanks and improved materials capable of withstanding extreme conditions. The report also discusses the importance of ortho‑para hydrogen conversion, advanced insulation systems, and efficient cryogenic pumping technologies that enhance refuelling performance. Globally, LH₂ infrastructure development is progressing unevenly, with Europe, the United States, Japan, South Korea, and Canada demonstrating varying levels of policy and technological readiness. The findings underscore that while LH₂ is technically mature, its economic viability depends on reducing liquefaction energy use, improving storage performance, and standardizing refuelling systems. Continued innovation and coordinated policy action will be crucial for LH₂ to play a central role in achieving global net‑zero objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".