High-entropy alloys for hydrogen storage, separation, and detection: Recent progress and prospects
Bibliographic record
Abstract
As a pivotal clean energy carrier with promising efficiency, environmental friendliness, and sustainability, hydrogen stands at the forefront of the global energy technology revolution. However, achieving the efficient storage, easy separation, and trace detection of hydrogen remain critical challenges. High-entropy alloys (HEAs) have garnered attention because of their remarkable attributes, including high stability, single-phase reversibility, and a wide tunable range of composition and electronic structure. Commencing with a succinct background overview, we explore the pivotal role of theoretical methods in designing the phase structure and ensuring the stability of HEAs, focusing especially on diverse element types and contents. We then present a summary of prevalent methods for preparing HEAs, followed by a detailed examination of recent advances in their hydrogen-related properties, encompassing hydrogen storage, separation, and detection. Finally, we look at the existing challenges and offer perspectives on the trajectory of future research and applications in this promising technological domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".