Effect of Components and Operating Conditions on the Performance of PEM Electrolyzers: A Review
Bibliographic record
Abstract
Hydrogen is considered to be the fuel of the future and with the advancement of fuel cell technology, there is a renewed interest in hydrogen production by the electrolysis of water. Among low-temperature water electrolysis options, polymer electrolyte membrane (PEM) electrolyzer is the preferred choice due to its compact size, intermittent use, and connectivity with renewable energy. In addition, it is possible to generate compressed hydrogen directly in the PEM electrolyzer, thereby reducing the additional pressurization cost for hydrogen storage. The development of electrocatalysts for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is a major focus of electrolysis research. Other components, such as PEMs, gas diffusion layers (GDL), and bipolar plates (BPs) have also received significant attention to enhance the overall efficiency of PEM electrolyzers. Improvements in each component or process of the PEM electrolyzer have a significant impact on increasing the energy efficiency of the electrolyzer. This work discusses various synthesis techniques to improve the dispersion of OER electrocatalyst and reducing catalyst loading for the PEM electrolyzer. Various techniques are discussed for the development of electrocatalysts, including nanostructured, core shell, and electrodeposition to deposit catalysts on GDL. The design and methodology of new and improved GDL are discussed along with the fabrication of gas diffusion electrodes and passivation techniques to reduce the oxidation of GDL. The passivation technique of BPs using Au and Pt is summarized for its effect on electrolysis efficiency. Finally, the optimization of various operating conditions for PEM electrolyzer are reviewed to improve the efficiency of the electrolyzer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".