Development of a high-performance electrolyzer for efficient hydrogen production via electrode modification with a commercial catalyst
Bibliographic record
Abstract
A potential strategy to promote the use of clean energy is the development of catalyst-coated cathodic electrodes that are economical, effective, and sustainable to enhance the generation of hydrogen (H 2 ) through the electrolysis process. This study investigates the unique design and use of stainless steel (SS) coated with a Cu-NiZnFeOx catalyst as both anode and cathode electrodes in the alkaline electrolysis process. The electrode exhibits an improved electrochemical behavior, achieving a current density of 92 mA/cm 2 at an applied voltage of 2.5 V with a surface area of 36 cm 2 in 1 M KOH electrolyte at 25 °C. Furthermore, the H 2 production is systematically investigated by varying electrolyte concentration, applied voltage, and temperature. The results demonstrate that H 2 production increases significantly with enhanced electrolyte concentration (3102 mL at 2 M KOH), applied voltage (3468 mL at 3.0 V), and temperature (3202 mL at 60 °C) over a 300 min electrolysis time. However, optimal operating conditions are determined to be 1 M KOH, 2.5 V, and 25 °C, balancing performance and energy efficiency. The improved performance is primarily attributed to enhanced ionic conductivity, reduced internal resistance, and the synergistic catalytic activity of the Cu-integrated NiZnFeOx coating. • Cu-NiZnFeOx coated stainless steel is prepared for catalyzing hydrogen production. • Coating boosts stability and current density to 0.18 A/cm 2 in alkaline media. • Efficient H 2 production of 4300 mL is achieved in 2 M KOH solution at 2.5 V. • Better H 2 production is obtained through Cu synergy, conductivity, and resistance drop. • Cu-NiZnFeOx cathode presents great hydrogen production in alkaline electrolysis.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".