(<i>Invited) </i>Critical and Strategic Raw Materials for Electrolysers, Fuel Cells and Metal Hydrides
Bibliographic record
Abstract
This presentation provides an examination of critical and strategic raw materials (CRMs) and their crucial role in the development of electrolyser and fuel cell technologies within the hydrogen economy [1]. It analyses a range of electrolyser technologies, including alkaline water electrolyser (AWE), proton exchange membrane water electrolyser (PEMWE), solid oxide electrolysis cell (SOEC), anion exchange membrane water electrolyser (AEMWE) and proton conducting ceramic cell (PCCEL) [2]. Each technology is examined for its specific CRM dependencies, operational characteristics, and the challenges associated with CRM availability and sustainability. The study further extends to hydrogen storage focusing on the materials employed in metal hydrides, and their CRM implications. A key aspect of this presentation is its exploration of the supply and demand dynamics of CRMs, offering a view that encompasses both the present state and future projections. The aim is to uncover potential supply risks, understand strategies, and identify potential bottlenecks for materials involved in electrolyser and fuel cell technologies, addressing both current needs and future demands as well as supply. This approach is essential for the strategic planning and sustainable development of the green hydrogen sector, emphasizing the importance of CRMs in achieving expanded electrolyser capacity leading up to 2050. References [1] Eikeng et al., Int. J. Hydrogen Energy, 2024, 71, 19, 433-464. [2] Chatenet, Pollet et al.,Chem. Soc. Rev., 2022, 51, 4583-4762. Figure 1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.186 | 0.064 |
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".