Sustainable PGM Recovery Processes for Fuel Cell and Electrolyzer Applications
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs) are pivotal technologies in developing sustainable energy infrastructure, through hydrogen as the primary energy vector. However, the large-scale deployment of PEMFCs and PEMWEs is constrained by their reliance on platinum group metals (PGMs), which are scarce and costly. While significant advancements have been made to reduce PGM loading without compromising efficiency and durability, these efforts have to be coupled with the development of sustainable PGM recovery methods from end-of-life components to ensure the scalability of PEMFC and PEMWE technologies. This article provides an overview of PGM recycling methods from a fuel cell and electrolyzer technology applications. Specifically, we briefly introduce conventional recovery methods, such as pyrometallurgical and hydrometallurgical processes, which offer high efficiency but are associated with high energy consumption, waste generation, and severe environmental concerns. Then, we discuss emerging green solvents and innovative extraction methods, highlighting their benefits and challenges for regenerating PGMs into electrocatalysts. Finally, key challenges in PGM recovery from secondary sources for hydrogen energy applications are presented, along with the potential of emerging recovery methods to address these challenges. Advancing these sustainable recovery strategies will be critical to establishing a circular economy for hydrogen and fuel cell technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".